chore: record capture plugin verification

Wave 2 deeper ingestion:
- 6 new Trusted Project State entries from design-level docs:
  p05: test rig architecture, CGH specification, procurement combos
  p06: force control architecture, control channels, calibration loop
- Total state entries: ~23 (was ~17)

Observability:
- GET /admin/dashboard — one-shot system overview: memory counts
  by type/project/status, reinforced count, project state entry
  counts, recent interaction timestamp, extraction pipeline status.
  Replaces the need to query 4+ endpoints to understand system state.

Harness: 17/18 (no regression from new state entries).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

DEV-LEDGER.md

@@ -6,13 +6,14 @@
 
 ## Orientation
 
-- **live_sha** (Dalidou `/health` build_sha): `39d73e9`
-- **last_updated**: 2026-04-12 by Codex (audit branch `codex/audit-2026-04-12-final`)
-- **main_tip**: `e2895b5`
-- **test_count**: 280 passing
-- **harness**: `16/18 PASS` (p06-firmware-interface = R7 ranking tie; p06-tailscale = chunk bleed)
-- **active_memories**: 36 (p06-polisher 16, p05-interferometer 6, p04-gigabit 5, atocore 5, other 4)
-- **project_state_entries**: p04=5, p05=6, p06=6 (Wave 2 entries present on live Dalidou; 17 total visible)
+- **live_sha** (Dalidou `/health` build_sha): `8951c62` (R9 fix at e5e9a99 not yet deployed)
+- **last_updated**: 2026-04-12 by Codex (branch `codex/openclaw-capture-plugin`)
+- **main_tip**: `4f8bec7`
+- **test_count**: 290 passing (local dev shell)
+- **harness**: `17/18 PASS` (only p06-tailscale still failing)
+- **active_memories**: 41
+- **candidate_memories**: 0
+- **project_state_entries**: p04=5, p05=6, p06=6 (Wave 2 entries still present on live Dalidou; 17 total visible)
 - **off_host_backup**: `papa@192.168.86.39:/home/papa/atocore-backups/` via cron env `ATOCORE_BACKUP_RSYNC`, verified
 
 ## Active Plan
@@ -121,16 +122,19 @@ One branch `codex/extractor-eval-loop` for Day 1-5, a second `codex/retrieval-ha
 
 | id  | finder | severity | file:line                          | summary                                                                 | status       | owner  | opened_at  | resolved_by |
 |-----|--------|----------|------------------------------------|-------------------------------------------------------------------------|--------------|--------|------------|-------------|
-| R1  | Codex  | P1       | deploy/hooks/capture_stop.py:76-85 | Live Claude capture still omits `extract`, so "loop closed both sides" remains overstated in practice even though the API supports it | acknowledged | Claude | 2026-04-11 |             |
+| R1  | Codex  | P1       | deploy/hooks/capture_stop.py:76-85 | Live Claude capture still omits `extract`, so "loop closed both sides" remains overstated in practice even though the API supports it | fixed        | Claude | 2026-04-11 | c67bec0     |
 | R2  | Codex  | P1       | src/atocore/context/builder.py     | Project memories excluded from pack                                     | fixed        | Claude | 2026-04-11 | 8ea53f4     |
 | R3  | Claude | P2       | src/atocore/memory/extractor.py    | Rule cues (`## Decision:`) never fire on conversational LLM text        | open         | Claude | 2026-04-11 |             |
 | R4  | Codex  | P2       | DEV-LEDGER.md:11                   | Orientation `main_tip` was stale versus `HEAD` / `origin/main`          | fixed        | Codex  | 2026-04-11 | 81307ce     |
-| R5  | Codex  | P1       | src/atocore/interactions/service.py:157-174 | The deployed extraction path still calls only the rule extractor; the new LLM extractor is eval/script-only, so Day 4 "gate cleared" is true as a benchmark result but not as an operational extraction path | acknowledged | Claude | 2026-04-12 |             |
+| R5  | Codex  | P1       | src/atocore/interactions/service.py:157-174 | The deployed extraction path still calls only the rule extractor; the new LLM extractor is eval/script-only, so Day 4 "gate cleared" is true as a benchmark result but not as an operational extraction path | fixed        | Claude | 2026-04-12 | c67bec0     |
 | R6  | Codex  | P1       | src/atocore/memory/extractor_llm.py:258-276 | LLM extraction accepts model-supplied `project` verbatim with no fallback to `interaction.project`; live triage promoted a clearly p06 memory (offline/network rule) as project=`""`, which explains the p06-offline-design harness miss and falsifies the current "all 3 failures are budget-contention" claim | fixed        | Claude | 2026-04-12 | 39d73e9     |
-| R7  | Codex  | P2       | src/atocore/memory/service.py:448-459 | Query ranking is overlap-count only, so broad overview memories can tie exact low-confidence memories and win on confidence; p06-firmware-interface is not just budget pressure, it also exposes a weak lexical scorer | open         | Claude | 2026-04-12 |             |
-| R8  | Codex  | P2       | tests/test_extractor_llm.py:1-7    | LLM extractor tests stop at parser/failure contracts; there is no automated coverage for the script-only persistence/review path that produced the 16 promoted memories, including project-scope preservation | open         | Claude | 2026-04-12 |             |
-| R9  | Codex  | P2       | src/atocore/memory/extractor_llm.py:258-259 | The R6 fallback only repairs empty project output. A wrong non-empty model project still overrides the interaction's known scope, so project attribution is improved but not yet trust-preserving. | open         | Claude | 2026-04-12 |             |
+| R7  | Codex  | P2       | src/atocore/memory/service.py:448-459 | Query ranking is overlap-count only, so broad overview memories can tie exact low-confidence memories and win on confidence; p06-firmware-interface is not just budget pressure, it also exposes a weak lexical scorer | fixed        | Claude | 2026-04-12 | 8951c62     |
+| R8  | Codex  | P2       | tests/test_extractor_llm.py:1-7    | LLM extractor tests stop at parser/failure contracts; there is no automated coverage for the script-only persistence/review path that produced the 16 promoted memories, including project-scope preservation | fixed        | Claude | 2026-04-12 | 69c9717     |
+| R9  | Codex  | P2       | src/atocore/memory/extractor_llm.py:258-259 | The R6 fallback only repairs empty project output. A wrong non-empty model project still overrides the interaction's known scope, so project attribution is improved but not yet trust-preserving. | fixed        | Claude | 2026-04-12 | e5e9a99     |
 | R10 | Codex  | P2       | docs/master-plan-status.md:31-33   | "Phase 8 - OpenClaw Integration" is fair as a baseline milestone, but not as a "primary" integration claim. `t420-openclaw/atocore.py` currently covers a narrow read-oriented subset (13 request shapes vs 32 API routes) plus fail-open health, while memory/interactions/admin write paths remain out of surface. | open         | Claude | 2026-04-12 |             |
+| R11 | Codex  | P2       | src/atocore/api/routes.py:773-845  | `POST /admin/extract-batch` still accepts `mode="llm"` inside the container and returns a successful 0-candidate result instead of surfacing that host-only LLM extraction is unavailable from this runtime. That is a misleading API contract for operators. | open         | Claude | 2026-04-12 |             |
+| R12 | Codex  | P2       | scripts/batch_llm_extract_live.py:39-190 | The host-side extractor duplicates the LLM system prompt and JSON parsing logic from `src/atocore/memory/extractor_llm.py`. It works today, but this is now a prompt/parser drift risk across the container and host implementations. | open         | Claude | 2026-04-12 |             |
+| R13 | Codex  | P2       | DEV-LEDGER.md:12                    | The new `286 passing` test-count claim is not reproducibly auditable from the current audit environments: neither Dalidou nor the clean worktree has `pytest` available. The claim may be true in Claude's dev shell, but it remains unverified in this audit. | open         | Claude | 2026-04-12 |             |
 
 ## Recent Decisions
 
@@ -148,6 +152,15 @@ One branch `codex/extractor-eval-loop` for Day 1-5, a second `codex/retrieval-ha
 
 ## Session Log
 
+- **2026-04-12 Codex (branch `codex/openclaw-capture-plugin`, verification close)** verified the final capture-plugin behavior on Dalidou after the `message_sending` reliability fix. New OpenClaw interactions now capture reliably and the stored prompt is clean human text instead of the Discord wrapper blob. Verified examples on Dalidou: `Final capture test` and `Yes, fix it, or I'll ask opus to do it`. The oldest two wrapper-heavy captures remain in history from earlier iterations, but new captures are clean.
+- **2026-04-12 Codex (branch `codex/openclaw-capture-plugin`, polish pass 3)** changed turn pairing from `llm_output` to `message_sending`. The plugin now caches the human prompt at `before_dispatch` and posts to AtoCore only when OpenClaw emits the real outbound assistant message. This should restore reliability while keeping prompt cleanliness. Awaiting one more post-restart validation turn.
+- **2026-04-12 Codex (branch `codex/openclaw-capture-plugin`, polish pass 2)** switched prompt capture from `before_agent_reply.cleanedBody` to `before_dispatch.body` / `content`, because the earlier path still stored Discord wrapper metadata. This should bind capture to the dispatch-stage human message instead of the prompt-builder artifact. Awaiting one more post-restart turn to verify on Dalidou.
+- **2026-04-12 Codex (branch `codex/openclaw-capture-plugin`, polish pass)** tightened the OpenClaw capture plugin to use `before_agent_reply.cleanedBody` instead of the raw prompt-build input, which should prevent Discord wrapper metadata from being stored as the interaction prompt. Added `agent_end` cleanup and updated plugin docs. A fresh post-restart user turn is still needed to verify prompt cleanliness on Dalidou.
+- **2026-04-12 Codex (branch `codex/openclaw-capture-plugin`)** added a minimal external OpenClaw plugin at `openclaw-plugins/atocore-capture/` that mirrors Claude Code capture semantics: user-triggered assistant turns are POSTed to AtoCore `/interactions` with `client="openclaw"` and `reinforce=true`, fail-open, no extraction in-path. For live verification, temporarily added the local plugin load path to OpenClaw config and restarted the gateway so the plugin can load. Branch truth is ready; end-to-end verification still needs one fresh post-restart OpenClaw user turn to confirm new `client=openclaw` interactions appear on Dalidou.
+- **2026-04-12 Claude** Batch 3 (R9 fix): `144dbbd..e5e9a99`. Trust hierarchy for project attribution — interaction scope always wins when set, model project only used for unscoped interactions + registered check. 7 case tests (A-G) cover every combination. Harness 17/18 (no regression). Tests 286->290. Before: wrong registered project could silently override interaction scope. After: interaction.project is the strongest signal; model project is only a fallback for unscoped captures. Not yet guaranteed: nothing prevents the *same* project's model output from being semantically wrong within that project. R9 marked fixed.
+
+- **2026-04-12 Codex (audit branch `codex/audit-batch2`)** audited `69c9717..origin/main` against the current branch tip and live Dalidou. Verified: live build is `8951c62`, retrieval harness improved to **17/18 PASS**, candidate queue is now empty, active memories rose to **41**, and `python3 scripts/auto_triage.py --dry-run --base-url http://127.0.0.1:8100` runs cleanly on Dalidou but only exercised the empty-queue path. Updated R7 to **fixed** (`8951c62`) and R8 to **fixed** (`69c9717`). Kept R9 **open** because project trust-preservation still allows a wrong non-empty registered project from the model to override the interaction scope. Added R13 because the new `286 passing` claim could not be independently reproduced in this audit: `pytest` is absent on both Dalidou and the clean audit worktree. Also corrected stale Orientation fields (live SHA, main tip, harness, active/candidate memory counts).
+- **2026-04-12 Codex (audit branch `codex/audit-2026-04-12-extraction`)** audited `54d84b5..ac7f77d` with live Dalidou verification. Confirmed the host-side LLM extraction pipeline is operational: nightly cron points at `deploy/dalidou/cron-backup.sh`, Step 4 calls `deploy/dalidou/batch-extract.sh`, the batch script exists/executable on Dalidou, and a manual host-side run produced candidates successfully. Updated R1 and R5 to **fixed** (`c67bec0`) because extraction now runs unattended off-container. Live state during audit: build `39d73e9`, active memories **36**, candidate queue **29** (16 existing + 13 added by manual verification run), and `last_extract_batch_run` populated in AtoCore project state. Added R11-R12 for the misleading container `mode=llm` no-op and host/container prompt-parser duplication. Security note: CLI positional prompt/response text is visible in process args while `claude -p` runs; acceptable on a single-user home host, but worth remembering if Dalidou's trust boundary changes.
 - **2026-04-12 Codex (audit branch `codex/audit-2026-04-12-final`)** audited `c5bad99..e2895b5` against origin/main, live Dalidou, and the OpenClaw client script. Live state checked: build `39d73e9`, harness reproducible at **16/18 PASS**, active memories **36**, and `t420-openclaw/atocore.py health` fails open correctly with `fail_open=true`. Spot-checks of Wave 2 project-state entries matched their cited vault docs. Updated R5-R8 status reality (R6 fixed by `39d73e9`), added R9-R10, and corrected Orientation `main_tip` to `e2895b5` because the ledger had drifted behind origin/main. Note: live Dalidou is still on `39d73e9`, so branch-truth and deploy-truth are not the same yet.
 - **2026-04-12 Claude** Wave 2 trusted operational ingestion + codex audit response. Read 6 vault docs, created 8 new Trusted Project State entries (p04 +2, p05 +3, p06 +3). Fixed R6 (project fallback in LLM extractor) per codex audit. Fixed misscoped p06 offline memory on live Dalidou. Merged codex/audit-2026-04-12. Switched default LLM model from haiku to sonnet. Harness 15/18 -> 16/18. Tests 278 -> 280. main_tip 146f2e4 -> 39d73e9.
 

deploy/dalidou/batch-extract.sh

@@ -0,0 +1,54 @@
+#!/usr/bin/env bash
+#
+# deploy/dalidou/batch-extract.sh
+# --------------------------------
+# Host-side LLM batch extraction for Dalidou.
+#
+# The claude CLI is available on the Dalidou HOST but NOT inside the
+# Docker container. This script runs on the host, fetches recent
+# interactions from the AtoCore API, runs the LLM extractor locally
+# (claude -p sonnet), and posts candidates back to the API.
+#
+# Intended to be called from cron-backup.sh after backup/cleanup/rsync,
+# or manually via:
+#
+#   bash /srv/storage/atocore/app/deploy/dalidou/batch-extract.sh
+#
+# Environment variables:
+#   ATOCORE_URL      default http://127.0.0.1:8100
+#   ATOCORE_EXTRACT_LIMIT  default 50
+
+set -euo pipefail
+
+ATOCORE_URL="${ATOCORE_URL:-http://127.0.0.1:8100}"
+LIMIT="${ATOCORE_EXTRACT_LIMIT:-50}"
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+APP_DIR="$(cd "$SCRIPT_DIR/../.." && pwd)"
+TIMESTAMP="$(date -u +%Y-%m-%dT%H:%M:%SZ)"
+
+log() { printf '[%s] %s\n' "$TIMESTAMP" "$*"; }
+
+# The Python script needs the atocore source on PYTHONPATH
+export PYTHONPATH="$APP_DIR/src:${PYTHONPATH:-}"
+
+log "=== AtoCore batch extraction + triage starting ==="
+log "URL=$ATOCORE_URL  LIMIT=$LIMIT"
+
+# Step A: Extract candidates from recent interactions
+log "Step A: LLM extraction"
+python3 "$APP_DIR/scripts/batch_llm_extract_live.py" \
+    --base-url "$ATOCORE_URL" \
+    --limit "$LIMIT" \
+    2>&1 || {
+    log "WARN: batch extraction failed (non-blocking)"
+}
+
+# Step B: Auto-triage candidates in the queue
+log "Step B: auto-triage"
+python3 "$APP_DIR/scripts/auto_triage.py" \
+    --base-url "$ATOCORE_URL" \
+    2>&1 || {
+    log "WARN: auto-triage failed (non-blocking)"
+}
+
+log "=== AtoCore batch extraction + triage complete ==="

deploy/dalidou/cron-backup.sh

@@ -82,4 +82,22 @@ else
     log "Step 3: ATOCORE_BACKUP_RSYNC not set, skipping off-host copy"
 fi
 
+# Step 4: Batch LLM extraction on recent interactions (optional).
+# Runs HOST-SIDE because claude CLI is on the host, not inside the
+# Docker container. The script fetches interactions from the API,
+# runs claude -p locally, and POSTs candidates back.
+# Fail-open: extraction failure never blocks backup.
+EXTRACT="${ATOCORE_EXTRACT_BATCH:-true}"
+if [[ "$EXTRACT" == "true" ]]; then
+    SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+    log "Step 4: running host-side batch LLM extraction"
+    bash "$SCRIPT_DIR/batch-extract.sh" 2>&1 && {
+        log "Extraction complete"
+    } || {
+        log "WARN: batch extraction failed (this is non-blocking)"
+    }
+else
+    log "Step 4: ATOCORE_EXTRACT_BATCH not set to true, skipping extraction"
+fi
+
 log "=== AtoCore daily backup complete ==="

docs/master-plan-status.md

@@ -24,12 +24,15 @@ read-only additive mode.
 - Phase 5 - Project State
 - Phase 7 - Context Builder
 
-### Partial
-
-- Phase 4 - Identity / Preferences
-
 ### Baseline Complete
 
+- Phase 4 - Identity / Preferences. As of 2026-04-12: 3 identity
+  memories (role, projects, infrastructure) and 3 preference memories
+  (no API keys, multi-model collab, action-over-discussion) seeded
+  on live Dalidou. Identity/preference band surfaces in context packs
+  at 5% budget ratio. Future identity/preference extraction happens
+  organically via the nightly LLM extraction pipeline.
+
 - Phase 8 - OpenClaw Integration. As of 2026-04-12 the T420 OpenClaw
   helper (`t420-openclaw/atocore.py`) is verified end-to-end against
   live Dalidou: health check, auto-context with project detection,

openclaw-plugins/atocore-capture/README.md

@@ -0,0 +1,32 @@
+# AtoCore Capture Plugin for OpenClaw
+
+Minimal OpenClaw plugin that mirrors Claude Code's `capture_stop.py` behavior:
+
+- watches user-triggered assistant turns
+- uses OpenClaw's dispatch-stage message body (`before_dispatch.body`) for the human prompt, then pairs it with the actual outbound assistant message on `message_sending`
+- POSTs `prompt` + `response` to `POST /interactions`
+- sets `client="openclaw"`
+- sets `reinforce=true`
+- fails open on network or API errors
+
+## Config
+
+Optional plugin config:
+
+```json
+{
+  "baseUrl": "http://dalidou:8100",
+  "minPromptLength": 15,
+  "maxResponseLength": 50000
+}
+```
+
+If `baseUrl` is omitted, the plugin uses `ATOCORE_BASE_URL` or defaults to `http://dalidou:8100`.
+
+## Notes
+
+- Project detection is intentionally left empty for now. Unscoped capture is acceptable because AtoCore's extraction pipeline handles unscoped interactions.
+- Prompt cleaning is done inside the plugin by reading OpenClaw's dispatch-stage message body instead of the raw prompt-build input.
+- Turn pairing is done by caching the prompt on dispatch and posting only when OpenClaw emits the outbound assistant message, which is more reliable than pairing against raw model output events.
+- Extraction is **not** part of the capture path. This plugin only records interactions and lets AtoCore reinforcement run automatically.
+- The plugin captures only user-triggered turns, not heartbeats or system-only runs.

openclaw-plugins/atocore-capture/index.js

@@ -0,0 +1,125 @@
+import { definePluginEntry } from "openclaw/plugin-sdk/core";
+
+const DEFAULT_BASE_URL = process.env.ATOCORE_BASE_URL || "http://dalidou:8100";
+const DEFAULT_MIN_PROMPT_LENGTH = 15;
+const DEFAULT_MAX_RESPONSE_LENGTH = 50_000;
+
+function trimText(value) {
+  return typeof value === "string" ? value.trim() : "";
+}
+
+function truncateResponse(text, maxLength) {
+  if (!text || text.length <= maxLength) return text;
+  return `${text.slice(0, maxLength)}\n\n[truncated]`;
+}
+
+function shouldCapturePrompt(prompt, minLength) {
+  const text = trimText(prompt);
+  if (!text) return false;
+  if (text.startsWith("<")) return false;
+  return text.length >= minLength;
+}
+
+function buildKeys(...values) {
+  return [...new Set(values.map((v) => trimText(v)).filter(Boolean))];
+}
+
+function rememberPending(store, keys, payload) {
+  for (const key of keys) store.set(key, payload);
+}
+
+function takePending(store, keys) {
+  for (const key of keys) {
+    const value = store.get(key);
+    if (value) {
+      for (const k of keys) store.delete(k);
+      store.delete(key);
+      return value;
+    }
+  }
+  return null;
+}
+
+function clearPending(store, keys) {
+  for (const key of keys) store.delete(key);
+}
+
+async function postInteraction(baseUrl, payload, logger) {
+  try {
+    const res = await fetch(`${baseUrl.replace(/\/$/, "")}/interactions`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(payload),
+      signal: AbortSignal.timeout(10_000)
+    });
+    if (!res.ok) {
+      logger?.debug?.("atocore_capture_post_failed", { status: res.status });
+      return false;
+    }
+    return true;
+  } catch (error) {
+    logger?.debug?.("atocore_capture_post_error", {
+      error: error instanceof Error ? error.message : String(error)
+    });
+    return false;
+  }
+}
+
+export default definePluginEntry({
+  register(api) {
+    const logger = api.logger;
+    const pendingBySession = new Map();
+
+    api.on("before_dispatch", async (event, ctx) => {
+      const config = api.getConfig?.() || {};
+      const minPromptLength = Number(config.minPromptLength || DEFAULT_MIN_PROMPT_LENGTH);
+      const prompt = trimText(event?.body || event?.content || "");
+      const keys = buildKeys(ctx?.sessionKey, ctx?.sessionId, event?.sessionKey, event?.sessionId, ctx?.conversationId, event?.conversationId);
+      if (!keys.length) return;
+      if (!shouldCapturePrompt(prompt, minPromptLength)) {
+        clearPending(pendingBySession, keys);
+        return;
+      }
+      rememberPending(pendingBySession, keys, {
+        prompt,
+        sessionId: trimText(ctx?.sessionId || event?.sessionId || ""),
+        sessionKey: trimText(ctx?.sessionKey || event?.sessionKey || ""),
+        conversationId: trimText(ctx?.conversationId || event?.conversationId || ""),
+        project: ""
+      });
+    });
+
+    api.on("message_sending", async (event, ctx) => {
+      const keys = buildKeys(ctx?.sessionKey, ctx?.sessionId, ctx?.conversationId);
+      const pending = takePending(pendingBySession, keys);
+      if (!pending) return;
+
+      const response = truncateResponse(
+        trimText(event?.content || ""),
+        Number((api.getConfig?.() || {}).maxResponseLength || DEFAULT_MAX_RESPONSE_LENGTH)
+      );
+      if (!response) return;
+
+      const config = api.getConfig?.() || {};
+      const baseUrl = trimText(config.baseUrl) || DEFAULT_BASE_URL;
+      const payload = {
+        prompt: pending.prompt,
+        response,
+        client: "openclaw",
+        session_id: pending.sessionKey || pending.sessionId || pending.conversationId,
+        project: pending.project || "",
+        reinforce: true
+      };
+
+      await postInteraction(baseUrl, payload, logger);
+    });
+
+    api.on("agent_end", async (event) => {
+      clearPending(pendingBySession, buildKeys(event?.sessionKey, event?.sessionId));
+    });
+
+    api.on("session_end", async (event) => {
+      clearPending(pendingBySession, buildKeys(event?.sessionKey, event?.sessionId));
+    });
+  }
+});

openclaw-plugins/atocore-capture/openclaw.plugin.json

@@ -0,0 +1,29 @@
+{
+  "id": "atocore-capture",
+  "name": "AtoCore Capture",
+  "description": "Captures completed OpenClaw assistant turns to AtoCore interactions for reinforcement.",
+  "configSchema": {
+    "type": "object",
+    "properties": {
+      "baseUrl": {
+        "type": "string",
+        "description": "Override AtoCore base URL. Defaults to ATOCORE_BASE_URL or http://dalidou:8100"
+      },
+      "minPromptLength": {
+        "type": "integer",
+        "minimum": 1,
+        "description": "Minimum user prompt length required before capture"
+      },
+      "maxResponseLength": {
+        "type": "integer",
+        "minimum": 100,
+        "description": "Maximum assistant response length to store"
+      }
+    },
+    "additionalProperties": false
+  },
+  "uiHints": {
+    "category": "automation",
+    "displayName": "AtoCore Capture"
+  }
+}

openclaw-plugins/atocore-capture/package.json

@@ -0,0 +1,7 @@
+{
+  "name": "@atomaste/atocore-openclaw-capture",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "description": "OpenClaw plugin that captures assistant turns to AtoCore interactions"
+}

scripts/auto_triage.py

@@ -0,0 +1,247 @@
+"""Auto-triage: LLM second-pass over candidate memories.
+
+Fetches all status=candidate memories from the AtoCore API, asks
+a triage model (via claude -p) to classify each as promote / reject /
+needs_human, and executes the verdict via the promote/reject endpoints.
+Only needs_human candidates remain in the queue for manual review.
+
+Trust model:
+- Auto-promote: model says promote AND confidence >= 0.8 AND no
+  duplicate content in existing active memories
+- Auto-reject: model says reject
+- needs_human: everything else stays in queue
+
+Runs host-side (same as batch extraction) because it needs the
+claude CLI. Intended to be called after batch-extract.sh in the
+nightly cron, or manually.
+
+Usage:
+
+    python3 scripts/auto_triage.py --base-url http://localhost:8100
+    python3 scripts/auto_triage.py --dry-run  # preview without executing
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import subprocess
+import sys
+import tempfile
+import urllib.error
+import urllib.parse
+import urllib.request
+
+DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://localhost:8100")
+DEFAULT_MODEL = os.environ.get("ATOCORE_TRIAGE_MODEL", "sonnet")
+DEFAULT_TIMEOUT_S = float(os.environ.get("ATOCORE_TRIAGE_TIMEOUT_S", "60"))
+AUTO_PROMOTE_MIN_CONFIDENCE = 0.8
+
+TRIAGE_SYSTEM_PROMPT = """You are a memory triage reviewer for a personal context engine called AtoCore. You review candidate memories extracted from LLM conversations and decide whether each should be promoted to active status, rejected, or flagged for human review.
+
+You will receive:
+- The candidate memory content and type
+- A list of existing active memories for the same project (to check for duplicates)
+
+For each candidate, output exactly one JSON object:
+
+{"verdict": "promote|reject|needs_human", "confidence": 0.0-1.0, "reason": "one sentence"}
+
+Rules:
+
+1. PROMOTE when the candidate states a durable architectural fact, ratified decision, standing rule, or engineering constraint that is NOT already covered by an existing active memory. Confidence should reflect how certain you are this is worth keeping.
+
+2. REJECT when the candidate is:
+   - A stale point-in-time snapshot ("live SHA is X", "36 active memories")
+   - An implementation detail too granular to be useful as standalone context
+   - A planned-but-not-implemented feature description
+   - A duplicate or near-duplicate of an existing active memory
+   - A session observation or conversational filler
+   - A process rule that belongs in DEV-LEDGER.md or AGENTS.md, not memory
+
+3. NEEDS_HUMAN when you're genuinely unsure — the candidate might be valuable but you can't tell without domain knowledge. This should be rare (< 20% of candidates).
+
+4. Output ONLY the JSON object. No prose, no markdown, no explanation outside the reason field."""
+
+_sandbox_cwd = None
+
+
+def get_sandbox_cwd():
+    global _sandbox_cwd
+    if _sandbox_cwd is None:
+        _sandbox_cwd = tempfile.mkdtemp(prefix="ato-triage-")
+    return _sandbox_cwd
+
+
+def api_get(base_url, path, timeout=10):
+    req = urllib.request.Request(f"{base_url}{path}")
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        return json.loads(resp.read().decode("utf-8"))
+
+
+def api_post(base_url, path, body=None, timeout=10):
+    data = json.dumps(body or {}).encode("utf-8")
+    req = urllib.request.Request(
+        f"{base_url}{path}", method="POST",
+        headers={"Content-Type": "application/json"}, data=data,
+    )
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        return json.loads(resp.read().decode("utf-8"))
+
+
+def fetch_active_memories_for_project(base_url, project):
+    """Fetch active memories for dedup checking."""
+    params = "active_only=true&limit=50"
+    if project:
+        params += f"&project={urllib.parse.quote(project)}"
+    result = api_get(base_url, f"/memory?{params}")
+    return result.get("memories", [])
+
+
+def triage_one(candidate, active_memories, model, timeout_s):
+    """Ask the triage model to classify one candidate."""
+    if not shutil.which("claude"):
+        return {"verdict": "needs_human", "confidence": 0.0, "reason": "claude CLI not available"}
+
+    active_summary = "\n".join(
+        f"- [{m['memory_type']}] {m['content'][:150]}"
+        for m in active_memories[:20]
+    ) or "(no active memories for this project)"
+
+    user_message = (
+        f"CANDIDATE TO TRIAGE:\n"
+        f"  type: {candidate['memory_type']}\n"
+        f"  project: {candidate.get('project') or '(none)'}\n"
+        f"  content: {candidate['content']}\n\n"
+        f"EXISTING ACTIVE MEMORIES FOR THIS PROJECT:\n{active_summary}\n\n"
+        f"Return the JSON verdict now."
+    )
+
+    args = [
+        "claude", "-p",
+        "--model", model,
+        "--append-system-prompt", TRIAGE_SYSTEM_PROMPT,
+        "--disable-slash-commands",
+        user_message,
+    ]
+
+    try:
+        completed = subprocess.run(
+            args, capture_output=True, text=True,
+            timeout=timeout_s, cwd=get_sandbox_cwd(),
+            encoding="utf-8", errors="replace",
+        )
+    except subprocess.TimeoutExpired:
+        return {"verdict": "needs_human", "confidence": 0.0, "reason": "triage model timed out"}
+    except Exception as exc:
+        return {"verdict": "needs_human", "confidence": 0.0, "reason": f"subprocess error: {exc}"}
+
+    if completed.returncode != 0:
+        return {"verdict": "needs_human", "confidence": 0.0, "reason": f"claude exit {completed.returncode}"}
+
+    raw = (completed.stdout or "").strip()
+    return parse_verdict(raw)
+
+
+def parse_verdict(raw):
+    """Parse the triage model's JSON verdict."""
+    text = raw.strip()
+    if text.startswith("```"):
+        text = text.strip("`")
+        nl = text.find("\n")
+        if nl >= 0:
+            text = text[nl + 1:]
+        if text.endswith("```"):
+            text = text[:-3]
+        text = text.strip()
+
+    if not text.lstrip().startswith("{"):
+        start = text.find("{")
+        end = text.rfind("}")
+        if start >= 0 and end > start:
+            text = text[start:end + 1]
+
+    try:
+        parsed = json.loads(text)
+    except json.JSONDecodeError:
+        return {"verdict": "needs_human", "confidence": 0.0, "reason": "failed to parse triage output"}
+
+    verdict = str(parsed.get("verdict", "needs_human")).strip().lower()
+    if verdict not in {"promote", "reject", "needs_human"}:
+        verdict = "needs_human"
+
+    confidence = parsed.get("confidence", 0.5)
+    try:
+        confidence = max(0.0, min(1.0, float(confidence)))
+    except (TypeError, ValueError):
+        confidence = 0.5
+
+    reason = str(parsed.get("reason", "")).strip()[:200]
+    return {"verdict": verdict, "confidence": confidence, "reason": reason}
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Auto-triage candidate memories")
+    parser.add_argument("--base-url", default=DEFAULT_BASE_URL)
+    parser.add_argument("--model", default=DEFAULT_MODEL)
+    parser.add_argument("--dry-run", action="store_true", help="preview without executing")
+    args = parser.parse_args()
+
+    # Fetch candidates
+    result = api_get(args.base_url, "/memory?status=candidate&limit=100")
+    candidates = result.get("memories", [])
+    print(f"candidates: {len(candidates)}  model: {args.model}  dry_run: {args.dry_run}")
+
+    if not candidates:
+        print("queue empty, nothing to triage")
+        return
+
+    # Cache active memories per project for dedup
+    active_cache = {}
+    promoted = rejected = needs_human = errors = 0
+
+    for i, cand in enumerate(candidates, 1):
+        project = cand.get("project") or ""
+        if project not in active_cache:
+            active_cache[project] = fetch_active_memories_for_project(args.base_url, project)
+
+        verdict_obj = triage_one(cand, active_cache[project], args.model, DEFAULT_TIMEOUT_S)
+        verdict = verdict_obj["verdict"]
+        conf = verdict_obj["confidence"]
+        reason = verdict_obj["reason"]
+
+        mid = cand["id"]
+        label = f"[{i:2d}/{len(candidates)}] {mid[:8]} [{cand['memory_type']}]"
+
+        if verdict == "promote" and conf >= AUTO_PROMOTE_MIN_CONFIDENCE:
+            if args.dry_run:
+                print(f"  WOULD PROMOTE  {label}  conf={conf:.2f}  {reason}")
+            else:
+                try:
+                    api_post(args.base_url, f"/memory/{mid}/promote")
+                    print(f"  PROMOTED       {label}  conf={conf:.2f}  {reason}")
+                    active_cache[project].append(cand)
+                except Exception:
+                    errors += 1
+            promoted += 1
+        elif verdict == "reject":
+            if args.dry_run:
+                print(f"  WOULD REJECT   {label}  conf={conf:.2f}  {reason}")
+            else:
+                try:
+                    api_post(args.base_url, f"/memory/{mid}/reject")
+                    print(f"  REJECTED       {label}  conf={conf:.2f}  {reason}")
+                except Exception:
+                    errors += 1
+            rejected += 1
+        else:
+            print(f"  NEEDS_HUMAN    {label}  conf={conf:.2f}  {reason}")
+            needs_human += 1
+
+    print(f"\npromoted={promoted} rejected={rejected} needs_human={needs_human} errors={errors}")
+
+
+if __name__ == "__main__":
+    main()

scripts/batch_llm_extract_live.py

@@ -0,0 +1,300 @@
+"""Host-side LLM batch extraction — pure HTTP client, no atocore imports.
+
+Fetches interactions from the AtoCore API, runs ``claude -p`` locally
+for each, and POSTs candidates back. Zero dependency on atocore source
+or Python packages — only uses stdlib + the ``claude`` CLI on PATH.
+
+This is necessary because the ``claude`` CLI is on the Dalidou HOST
+but not inside the Docker container, and the host's Python doesn't
+have the container's dependencies (pydantic_settings, etc.).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import subprocess
+import sys
+import tempfile
+import urllib.error
+import urllib.parse
+import urllib.request
+from datetime import datetime, timezone
+
+DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://localhost:8100")
+DEFAULT_MODEL = os.environ.get("ATOCORE_LLM_EXTRACTOR_MODEL", "sonnet")
+DEFAULT_TIMEOUT_S = float(os.environ.get("ATOCORE_LLM_EXTRACTOR_TIMEOUT_S", "90"))
+MAX_RESPONSE_CHARS = 8000
+MAX_PROMPT_CHARS = 2000
+
+MEMORY_TYPES = {"identity", "preference", "project", "episodic", "knowledge", "adaptation"}
+
+SYSTEM_PROMPT = """You extract durable memory candidates from LLM conversation turns for a personal context engine called AtoCore.
+
+Your job is to read one user prompt plus the assistant's response and decide which durable facts, decisions, preferences, architectural rules, or project invariants should be remembered across future sessions.
+
+Rules:
+
+1. Only surface durable claims. Skip transient status ("deploy is still running"), instructional guidance ("here is how to run the command"), troubleshooting tactics, ephemeral recommendations ("merge this PR now"), and session recaps.
+2. A candidate is durable when a reader coming back in two weeks would still need to know it. Architectural choices, named rules, ratified decisions, invariants, procurement commitments, and project-level constraints qualify. Conversational fillers and step-by-step instructions do not.
+3. Each candidate must stand alone. Rewrite the claim in one sentence under 200 characters with enough context that a reader without the conversation understands it.
+4. Each candidate must have a type from this closed set: project, knowledge, preference, adaptation.
+5. If the conversation is clearly scoped to a project (p04-gigabit, p05-interferometer, p06-polisher, atocore), set ``project`` to that id. Otherwise leave ``project`` empty.
+6. If the response makes no durable claim, return an empty list. It is correct and expected to return [] on most conversational turns.
+7. Confidence should be 0.5 by default so human review workload is honest. Raise to 0.6 only when the response states the claim in an unambiguous, committed form (e.g. "the decision is X", "the selected approach is Y", "X is non-negotiable").
+8. Output must be a raw JSON array and nothing else. No prose before or after. No markdown fences. No explanations.
+
+Each array element has exactly this shape:
+
+{"type": "project|knowledge|preference|adaptation", "content": "...", "project": "...", "confidence": 0.5}
+
+Return [] when there is nothing to extract."""
+
+_sandbox_cwd = None
+
+
+def get_sandbox_cwd():
+    global _sandbox_cwd
+    if _sandbox_cwd is None:
+        _sandbox_cwd = tempfile.mkdtemp(prefix="ato-llm-extract-")
+    return _sandbox_cwd
+
+
+def api_get(base_url, path, timeout=10):
+    req = urllib.request.Request(f"{base_url}{path}")
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        return json.loads(resp.read().decode("utf-8"))
+
+
+def api_post(base_url, path, body, timeout=10):
+    data = json.dumps(body).encode("utf-8")
+    req = urllib.request.Request(
+        f"{base_url}{path}", method="POST",
+        headers={"Content-Type": "application/json"}, data=data,
+    )
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        return json.loads(resp.read().decode("utf-8"))
+
+
+def get_last_run(base_url):
+    try:
+        state = api_get(base_url, "/project/state/atocore?category=status")
+        for entry in state.get("entries", []):
+            if entry.get("key") == "last_extract_batch_run":
+                return entry["value"]
+    except Exception:
+        pass
+    return None
+
+
+def set_last_run(base_url, timestamp):
+    try:
+        api_post(base_url, "/project/state", {
+            "project": "atocore", "category": "status",
+            "key": "last_extract_batch_run", "value": timestamp,
+            "source": "batch_llm_extract_live.py",
+        })
+    except Exception:
+        pass
+
+
+_known_projects: set[str] = set()
+
+
+def _load_known_projects(base_url):
+    """Fetch registered project IDs from the API for R9 validation."""
+    global _known_projects
+    try:
+        data = api_get(base_url, "/projects")
+        _known_projects = {p["id"] for p in data.get("projects", [])}
+        for p in data.get("projects", []):
+            for alias in p.get("aliases", []):
+                _known_projects.add(alias)
+    except Exception:
+        pass
+
+
+def extract_one(prompt, response, project, model, timeout_s):
+    """Run claude -p on one interaction, return parsed candidates."""
+    if not shutil.which("claude"):
+        return [], "claude_cli_missing"
+
+    prompt_excerpt = prompt[:MAX_PROMPT_CHARS]
+    response_excerpt = response[:MAX_RESPONSE_CHARS]
+    user_message = (
+        f"PROJECT HINT (may be empty): {project}\n\n"
+        f"USER PROMPT:\n{prompt_excerpt}\n\n"
+        f"ASSISTANT RESPONSE:\n{response_excerpt}\n\n"
+        "Return the JSON array now."
+    )
+
+    args = [
+        "claude", "-p",
+        "--model", model,
+        "--append-system-prompt", SYSTEM_PROMPT,
+        "--disable-slash-commands",
+        user_message,
+    ]
+
+    try:
+        completed = subprocess.run(
+            args, capture_output=True, text=True,
+            timeout=timeout_s, cwd=get_sandbox_cwd(),
+            encoding="utf-8", errors="replace",
+        )
+    except subprocess.TimeoutExpired:
+        return [], "timeout"
+    except Exception as exc:
+        return [], f"subprocess_error: {exc}"
+
+    if completed.returncode != 0:
+        return [], f"exit_{completed.returncode}"
+
+    raw = (completed.stdout or "").strip()
+    return parse_candidates(raw, project), ""
+
+
+def parse_candidates(raw, interaction_project):
+    """Parse model JSON output into candidate dicts."""
+    text = raw.strip()
+    if text.startswith("```"):
+        text = text.strip("`")
+        nl = text.find("\n")
+        if nl >= 0:
+            text = text[nl + 1:]
+        if text.endswith("```"):
+            text = text[:-3]
+        text = text.strip()
+
+    if not text or text == "[]":
+        return []
+
+    if not text.lstrip().startswith("["):
+        start = text.find("[")
+        end = text.rfind("]")
+        if start >= 0 and end > start:
+            text = text[start:end + 1]
+
+    try:
+        parsed = json.loads(text)
+    except json.JSONDecodeError:
+        return []
+
+    if not isinstance(parsed, list):
+        return []
+
+    results = []
+    for item in parsed:
+        if not isinstance(item, dict):
+            continue
+        mem_type = str(item.get("type") or "").strip().lower()
+        content = str(item.get("content") or "").strip()
+        model_project = str(item.get("project") or "").strip()
+        # R9 trust hierarchy: interaction scope always wins when set.
+        # Model project only used for unscoped interactions + registered check.
+        if interaction_project:
+            project = interaction_project
+        elif model_project and model_project in _known_projects:
+            project = model_project
+        else:
+            project = ""
+        conf = item.get("confidence", 0.5)
+        if mem_type not in MEMORY_TYPES or not content:
+            continue
+        try:
+            conf = max(0.0, min(1.0, float(conf)))
+        except (TypeError, ValueError):
+            conf = 0.5
+        results.append({
+            "memory_type": mem_type,
+            "content": content[:1000],
+            "project": project,
+            "confidence": conf,
+        })
+    return results
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Host-side LLM batch extraction")
+    parser.add_argument("--base-url", default=DEFAULT_BASE_URL)
+    parser.add_argument("--limit", type=int, default=50)
+    parser.add_argument("--since", default=None)
+    parser.add_argument("--model", default=DEFAULT_MODEL)
+    args = parser.parse_args()
+
+    _load_known_projects(args.base_url)
+    since = args.since or get_last_run(args.base_url)
+    print(f"since={since or '(first run)'}  limit={args.limit}  model={args.model}  known_projects={len(_known_projects)}")
+
+    params = [f"limit={args.limit}"]
+    if since:
+        params.append(f"since={urllib.parse.quote(since)}")
+    listing = api_get(args.base_url, f"/interactions?{'&'.join(params)}")
+    interaction_summaries = listing.get("interactions", [])
+    print(f"listed {len(interaction_summaries)} interactions")
+
+    processed = 0
+    total_candidates = 0
+    total_persisted = 0
+    errors = 0
+
+    for summary in interaction_summaries:
+        resp_chars = summary.get("response_chars", 0) or 0
+        if resp_chars < 50:
+            continue
+        iid = summary["id"]
+        try:
+            raw = api_get(
+                args.base_url,
+                f"/interactions/{urllib.parse.quote(iid, safe='')}",
+            )
+        except Exception as exc:
+            print(f"  ! {iid[:8]}: fetch failed: {exc}", file=sys.stderr)
+            errors += 1
+            continue
+        response_text = raw.get("response", "") or ""
+        if not response_text.strip() or len(response_text) < 50:
+            continue
+
+        candidates, error = extract_one(
+            prompt=raw.get("prompt", "") or "",
+            response=response_text,
+            project=raw.get("project", "") or "",
+            model=args.model,
+            timeout_s=DEFAULT_TIMEOUT_S,
+        )
+
+        if error:
+            print(f"  ! {raw['id'][:8]}: {error}", file=sys.stderr)
+            errors += 1
+            continue
+
+        processed += 1
+        total_candidates += len(candidates)
+
+        for c in candidates:
+            try:
+                api_post(args.base_url, "/memory", {
+                    "memory_type": c["memory_type"],
+                    "content": c["content"],
+                    "project": c["project"],
+                    "confidence": c["confidence"],
+                    "status": "candidate",
+                })
+                total_persisted += 1
+            except urllib.error.HTTPError as exc:
+                if exc.code != 400:
+                    errors += 1
+            except Exception:
+                errors += 1
+
+    now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
+    set_last_run(args.base_url, now)
+
+    print(f"processed={processed} candidates={total_candidates} persisted={total_persisted} errors={errors}")
+
+
+if __name__ == "__main__":
+    main()

scripts/eval_data/candidate_queue_2026-04-12.txt

@@ -0,0 +1,29 @@
+ 1. [project    ] proj=atocore          AtoCore extraction must stay off the hot capture path; batch endpoint only
+ 2. [project    ] proj=atocore          Auto-promote gate: confidence ≥0.8 AND no duplicate in active memories
+ 3. [project    ] proj=atocore          AtoCore LLM extraction pipeline deployed on Dalidou host, runs via cron at 03:00 UTC via scripts/batch_llm_extract_live.py
+ 4. [project    ] proj=atocore          LLM extractor runs host-side (not in container) because claude CLI not available in container environment
+ 5. [project    ] proj=atocore          Host-side extraction script scripts/batch_llm_extract_live.py uses pure stdlib, no atocore imports for deployment simplicity
+ 6. [project    ] proj=atocore          POST /admin/extract-batch accepts mode: rule|llm, POST /interactions/{id}/extract now mode-aware
+ 7. [knowledge  ] proj=atocore          claude CLI 2.0.60 removed --no-session-persistence flag, extraction sessions now persist in claude history
+ 8. [adaptation ] proj=atocore          Durable memory extraction candidates must be <200 chars, stand-alone, typed as project|knowledge|preference|adaptation
+ 9. [adaptation ] proj=atocore          Memory extraction confidence defaults to 0.5, raise to 0.6 only for unambiguous committed claims
+10. [project    ] proj=atocore          Live Dalidou is on commit 39d73e9, not e2895b5
+11. [project    ] proj=atocore          Live harness is reproducible at 16/18 PASS
+12. [project    ] proj=atocore          Live active memories count is 36
+13. [project    ] proj=atocore          Wave 2 project-state entries on live: p04=5, p05=6, p06=6
+14. [project    ] proj=atocore          R6 is fixed by commit 39d73e9
+15. [project    ] proj=atocore          R9: R6 fix only covers empty project fallback; wrong non-empty model project can still override known interaction scope
+16. [project    ] proj=atocore          R10: Phase 8 is baseline-complete but not primary-complete; OpenClaw client covers narrow read-oriented slice of API
+17. [project    ] proj=atocore          Phase 8 is decent baseline integration milestone but not primary-ready yet
+18. [project    ] proj=atocore          4-step roadmap complete: extractor → harness → Wave 2 → OpenClaw
+19. [project    ] proj=atocore          Codex audit loop proven across two full round-trips in one session
+20. [project    ] proj=atocore          Session end state: 36 active memories, 17 project-state entries, 16/18 harness, 280 tests, main at 54d84b5
+21. [project    ] proj=atocore          AtoCore extraction stays off the hot capture path; LLM extraction runs as scheduled batch, not inline with POST /interactions.
+22. [project    ] proj=atocore          AtoCore auto-triage trust model: auto-promote only when confidence ≥0.8 AND no duplicate active memory; else needs_human.
+23. [project    ] proj=atocore          Multi-model triage: use different model for triage reviewer than extractor (sonnet for extract)
+24. [project    ] proj=atocore          R9 fix: when interaction has known project, prefer it over model's non-matching project unless model's is registered
+25. [project    ] proj=atocore          R7 ranking fix: add overlap-density as secondary signal (overlap_count / memory_token_count)
+26. [project    ] proj=atocore          Extraction pipeline skips interactions with response_chars < 50 to avoid low-signal content
+27. [project    ] proj=atocore          AtoCore triage uses independent model from extractor (extractor: sonnet, triage: different model or different prompt).
+28. [project    ] proj=atocore          AtoCore ranking scorer adds overlap-density (overlap_count / memory_tokens) as secondary signal to fix short-memory ranking.
+29. [project    ] proj=atocore          AtoCore project trust: when interaction has known project and model returns different project, prefer interaction's project unless

src/atocore/api/routes.py

@@ -35,6 +35,10 @@ from atocore.memory.extractor import (
     MemoryCandidate,
     extract_candidates_from_interaction,
 )
+from atocore.memory.extractor_llm import (
+    LLM_EXTRACTOR_VERSION,
+    extract_candidates_llm,
+)
 from atocore.memory.reinforcement import reinforce_from_interaction
 from atocore.memory.service import (
     MEMORY_STATUSES,
@@ -580,6 +584,7 @@ def api_reinforce_interaction(interaction_id: str) -> dict:
 
 class InteractionExtractRequest(BaseModel):
     persist: bool = False
+    mode: str = "rule"  # "rule" or "llm"
 
 
 @router.post("/interactions/{interaction_id}/extract")
@@ -601,7 +606,10 @@ def api_extract_from_interaction(
     if interaction is None:
         raise HTTPException(status_code=404, detail=f"Interaction not found: {interaction_id}")
     payload = req or InteractionExtractRequest()
-    candidates: list[MemoryCandidate] = extract_candidates_from_interaction(interaction)
+    if payload.mode == "llm":
+        candidates: list[MemoryCandidate] = extract_candidates_llm(interaction)
+    else:
+        candidates: list[MemoryCandidate] = extract_candidates_from_interaction(interaction)
 
     persisted_ids: list[str] = []
     if payload.persist:
@@ -755,6 +763,169 @@ def api_cleanup_backups(req: BackupCleanupRequest | None = None) -> dict:
         raise HTTPException(status_code=500, detail=f"Cleanup failed: {e}")
 
 
+class ExtractBatchRequest(BaseModel):
+    since: str | None = None
+    mode: str = "llm"
+    limit: int = 50
+    persist: bool = True
+
+
+@router.post("/admin/extract-batch")
+def api_extract_batch(req: ExtractBatchRequest | None = None) -> dict:
+    """Run batch extraction across recent interactions.
+
+    Fetches interactions since ``since`` (or since the last recorded
+    batch run), runs the extractor (rule or LLM) on each, and persists
+    any candidates as ``status=candidate``. The last-run timestamp is
+    stored in project state under ``atocore / status /
+    last_extract_batch_run`` so subsequent calls without ``since``
+    automatically pick up where the last run left off.
+
+    This endpoint is the operational home for R1 / R5 — it makes the
+    LLM extractor accessible as an API operation rather than a
+    script-only eval tool. Still NOT on the capture hot path: callers
+    invoke this endpoint explicitly (cron, manual curl, CLI).
+    """
+    payload = req or ExtractBatchRequest()
+    since = payload.since
+
+    if not since:
+        state_entries = get_state("atocore")
+        for entry in state_entries:
+            if entry.category == "status" and entry.key == "last_extract_batch_run":
+                since = entry.value
+                break
+
+    interactions = list_interactions(since=since, limit=min(payload.limit, 200))
+
+    processed = 0
+    total_candidates = 0
+    total_persisted = 0
+    errors: list[dict] = []
+
+    for interaction in interactions:
+        if not (interaction.response or interaction.response_summary):
+            continue
+        try:
+            if payload.mode == "llm":
+                candidates = extract_candidates_llm(interaction)
+            else:
+                candidates = extract_candidates_from_interaction(interaction)
+        except Exception as exc:
+            errors.append({"interaction_id": interaction.id, "error": str(exc)})
+            continue
+
+        processed += 1
+        total_candidates += len(candidates)
+
+        if payload.persist and candidates:
+            for candidate in candidates:
+                try:
+                    create_memory(
+                        memory_type=candidate.memory_type,
+                        content=candidate.content,
+                        project=candidate.project,
+                        confidence=candidate.confidence,
+                        status="candidate",
+                    )
+                    total_persisted += 1
+                except ValueError:
+                    pass  # duplicate — skip silently
+
+    from datetime import datetime, timezone
+
+    now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
+    try:
+        set_state(
+            project="atocore",
+            category="status",
+            key="last_extract_batch_run",
+            value=now,
+            source="admin/extract-batch endpoint",
+        )
+    except Exception:
+        pass  # best-effort timestamp tracking
+
+    log.info(
+        "extract_batch_complete",
+        mode=payload.mode,
+        processed=processed,
+        total_candidates=total_candidates,
+        total_persisted=total_persisted,
+        errors=len(errors),
+    )
+
+    return {
+        "processed": processed,
+        "total_candidates": total_candidates,
+        "total_persisted": total_persisted,
+        "mode": payload.mode,
+        "persist": payload.persist,
+        "since": since or "(first run)",
+        "errors": errors,
+    }
+
+
+@router.get("/admin/dashboard")
+def api_dashboard() -> dict:
+    """One-shot system observability dashboard.
+
+    Returns memory counts by type/project/status, project state
+    entry counts, recent interaction volume, and extraction pipeline
+    status — everything an operator needs to understand AtoCore's
+    health beyond the basic /health endpoint.
+    """
+    from collections import Counter
+
+    all_memories = get_memories(active_only=False, limit=500)
+    active = [m for m in all_memories if m.status == "active"]
+    candidates = [m for m in all_memories if m.status == "candidate"]
+
+    type_counts = dict(Counter(m.memory_type for m in active))
+    project_counts = dict(Counter(m.project or "(none)" for m in active))
+    reinforced = [m for m in active if m.reference_count > 0]
+
+    interactions = list_interactions(limit=1)
+    recent_interaction = interactions[0].created_at if interactions else None
+
+    # Extraction pipeline status
+    extract_state = {}
+    try:
+        state_entries = get_state("atocore")
+        for entry in state_entries:
+            if entry.category == "status" and entry.key == "last_extract_batch_run":
+                extract_state["last_run"] = entry.value
+    except Exception:
+        pass
+
+    # Project state counts
+    ps_counts = {}
+    for proj_id in ["p04-gigabit", "p05-interferometer", "p06-polisher", "atocore"]:
+        try:
+            entries = get_state(proj_id)
+            ps_counts[proj_id] = len(entries)
+        except Exception:
+            pass
+
+    return {
+        "memories": {
+            "active": len(active),
+            "candidates": len(candidates),
+            "by_type": type_counts,
+            "by_project": project_counts,
+            "reinforced": len(reinforced),
+        },
+        "project_state": {
+            "counts": ps_counts,
+            "total": sum(ps_counts.values()),
+        },
+        "interactions": {
+            "most_recent": recent_interaction,
+        },
+        "extraction_pipeline": extract_state,
+    }
+
+
 @router.get("/admin/backup/{stamp}/validate")
 def api_validate_backup(stamp: str) -> dict:
     """Validate that a previously created backup is structurally usable."""

src/atocore/context/builder.py

@@ -29,7 +29,7 @@ SYSTEM_PREFIX = (
 # Budget allocation (per Master Plan section 9):
 #   identity: 5%, preferences: 5%, project state: 20%, retrieval: 60%+
 PROJECT_STATE_BUDGET_RATIO = 0.20
-MEMORY_BUDGET_RATIO = 0.10  # 5% identity + 5% preference
+MEMORY_BUDGET_RATIO = 0.05  # identity + preference; lowered from 0.10 to avoid squeezing project memories and chunks
 # Project-scoped memories (project/knowledge/episodic) are the outlet
 # for the Phase 9 reflection loop on the retrieval side. Budget sits
 # between identity/preference and retrieved chunks so a reinforced

src/atocore/memory/extractor_llm.py

@@ -168,7 +168,6 @@ def extract_candidates_llm_verbose(
         model or DEFAULT_MODEL,
         "--append-system-prompt",
         _SYSTEM_PROMPT,
-        "--no-session-persistence",
         "--disable-slash-commands",
         user_message,
     ]
@@ -255,9 +254,28 @@ def _parse_candidates(raw_output: str, interaction: Interaction) -> list[MemoryC
             continue
         mem_type = str(item.get("type") or "").strip().lower()
         content = str(item.get("content") or "").strip()
-        project = str(item.get("project") or "").strip()
-        if not project and interaction.project:
+        model_project = str(item.get("project") or "").strip()
+        # R9 trust hierarchy for project attribution:
+        #   1. Interaction scope always wins when set (strongest signal)
+        #   2. Model project used only when interaction is unscoped
+        #      AND model project resolves to a registered project
+        #   3. Empty string when both are empty/unregistered
+        if interaction.project:
             project = interaction.project
+        elif model_project:
+            try:
+                from atocore.projects.registry import (
+                    load_project_registry,
+                    resolve_project_name,
+                )
+
+                registered_ids = {p.project_id for p in load_project_registry()}
+                resolved = resolve_project_name(model_project)
+                project = resolved if resolved in registered_ids else ""
+            except Exception:
+                project = ""
+        else:
+            project = ""
         confidence_raw = item.get("confidence", 0.5)
         if mem_type not in MEMORY_TYPES:
             continue

src/atocore/memory/service.py

@@ -446,20 +446,27 @@ def _rank_memories_for_query(
 ) -> list["Memory"]:
     """Rerank a memory list by lexical overlap with a pre-tokenized query.
 
-    Ordering key: (overlap_count DESC, confidence DESC). When a query
-    shares no tokens with a memory, overlap is zero and confidence
-    acts as the sole tiebreaker — which matches the pre-query
-    behaviour and keeps no-query calls stable.
+    Primary key: overlap_density (overlap_count / memory_token_count),
+    which rewards short focused memories that match the query precisely
+    over long overview memories that incidentally share a few tokens.
+    Secondary: absolute overlap count. Tertiary: confidence.
+
+    R7 fix: previously overlap_count alone was the primary key, so a
+    40-token overview memory with 3 overlapping tokens tied a 5-token
+    memory with 3 overlapping tokens, and the overview won on
+    confidence. Now the short memory's density (0.6) beats the
+    overview's density (0.075).
     """
     from atocore.memory.reinforcement import _normalize, _tokenize
 
-    scored: list[tuple[int, float, Memory]] = []
+    scored: list[tuple[float, int, float, Memory]] = []
     for mem in memories:
         mem_tokens = _tokenize(_normalize(mem.content))
         overlap = len(mem_tokens & query_tokens) if mem_tokens else 0
-        scored.append((overlap, mem.confidence, mem))
-    scored.sort(key=lambda t: (t[0], t[1]), reverse=True)
-    return [mem for _, _, mem in scored]
+        density = overlap / len(mem_tokens) if mem_tokens else 0.0
+        scored.append((density, overlap, mem.confidence, mem))
+    scored.sort(key=lambda t: (t[0], t[1], t[2]), reverse=True)
+    return [mem for _, _, _, mem in scored]
 
 
 def _row_to_memory(row) -> Memory:

tests/test_extraction_pipeline.py

@@ -0,0 +1,173 @@
+"""Integration tests for the extraction + triage pipeline (R8).
+
+Tests the flow that produced the 41 active memories:
+LLM extraction → persist as candidate → triage → promote/reject.
+Uses mocked subprocess to avoid real claude -p calls.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import patch
+
+import pytest
+
+from atocore.memory.extractor_llm import (
+    extract_candidates_llm,
+    extract_candidates_llm_verbose,
+)
+from atocore.memory.service import create_memory, get_memories
+from atocore.models.database import init_db
+import atocore.memory.extractor_llm as extractor_llm
+
+
+def _make_interaction(**kw):
+    from atocore.interactions.service import Interaction
+
+    return Interaction(
+        id=kw.get("id", "test-pipe-1"),
+        prompt=kw.get("prompt", "test prompt"),
+        response=kw.get("response", ""),
+        response_summary="",
+        project=kw.get("project", ""),
+        client="test",
+        session_id="",
+    )
+
+
+class _FakeCompleted:
+    def __init__(self, stdout, returncode=0):
+        self.stdout = stdout
+        self.stderr = ""
+        self.returncode = returncode
+
+
+def test_llm_extraction_persists_as_candidate(tmp_data_dir, monkeypatch):
+    """Full flow: LLM extracts → caller persists as candidate → memory
+    exists with status=candidate and correct project."""
+    init_db()
+    monkeypatch.setattr(extractor_llm, "_cli_available", lambda: True)
+    monkeypatch.setattr(
+        extractor_llm.subprocess,
+        "run",
+        lambda *a, **kw: _FakeCompleted(
+            '[{"type": "project", "content": "USB SSD is mandatory for RPi storage", "project": "p06-polisher", "confidence": 0.6}]'
+        ),
+    )
+
+    interaction = _make_interaction(
+        response="We decided USB SSD is mandatory for the polisher RPi.",
+        project="p06-polisher",
+    )
+    candidates = extract_candidates_llm(interaction)
+    assert len(candidates) == 1
+    assert candidates[0].content == "USB SSD is mandatory for RPi storage"
+
+    mem = create_memory(
+        memory_type=candidates[0].memory_type,
+        content=candidates[0].content,
+        project=candidates[0].project,
+        confidence=candidates[0].confidence,
+        status="candidate",
+    )
+    assert mem.status == "candidate"
+    assert mem.project == "p06-polisher"
+
+    # Verify it appears in the candidate queue
+    queue = get_memories(status="candidate", project="p06-polisher", limit=10)
+    assert any(m.id == mem.id for m in queue)
+
+
+def test_llm_extraction_project_fallback(tmp_data_dir, monkeypatch):
+    """R6+R9: when model returns empty project, candidate inherits
+    the interaction's project."""
+    init_db()
+    monkeypatch.setattr(extractor_llm, "_cli_available", lambda: True)
+    monkeypatch.setattr(
+        extractor_llm.subprocess,
+        "run",
+        lambda *a, **kw: _FakeCompleted(
+            '[{"type": "knowledge", "content": "machine works offline", "project": "", "confidence": 0.5}]'
+        ),
+    )
+
+    interaction = _make_interaction(
+        response="The machine works fully offline.",
+        project="p06-polisher",
+    )
+    candidates = extract_candidates_llm(interaction)
+    assert len(candidates) == 1
+    assert candidates[0].project == "p06-polisher"
+
+
+def test_promote_reject_flow(tmp_data_dir):
+    """Candidate → promote and candidate → reject both work via the
+    service layer (mirrors what auto_triage.py does via HTTP)."""
+    from atocore.memory.service import promote_memory, reject_candidate_memory
+
+    init_db()
+    good = create_memory(
+        memory_type="project",
+        content="durable fact worth keeping",
+        project="p06-polisher",
+        confidence=0.5,
+        status="candidate",
+    )
+    bad = create_memory(
+        memory_type="project",
+        content="stale snapshot to reject",
+        project="atocore",
+        confidence=0.5,
+        status="candidate",
+    )
+
+    promote_memory(good.id)
+    reject_candidate_memory(bad.id)
+
+    active = get_memories(project="p06-polisher", active_only=True, limit=10)
+    assert any(m.id == good.id for m in active)
+
+    candidates = get_memories(status="candidate", limit=10)
+    assert not any(m.id == good.id for m in candidates)
+    assert not any(m.id == bad.id for m in candidates)
+
+
+def test_duplicate_content_creates_separate_memory(tmp_data_dir):
+    """create_memory allows duplicate content (dedup is the triage
+    model's responsibility, not the DB layer). Both memories exist."""
+    init_db()
+    m1 = create_memory(
+        memory_type="project",
+        content="unique fact about polisher",
+        project="p06-polisher",
+    )
+    m2 = create_memory(
+        memory_type="project",
+        content="unique fact about polisher",
+        project="p06-polisher",
+        status="candidate",
+    )
+    assert m1.id != m2.id
+
+
+def test_llm_extraction_failure_returns_empty(tmp_data_dir, monkeypatch):
+    """The full persist flow handles LLM extraction failure gracefully:
+    0 candidates, nothing persisted, no raise."""
+    init_db()
+    monkeypatch.setattr(extractor_llm, "_cli_available", lambda: True)
+    monkeypatch.setattr(
+        extractor_llm.subprocess,
+        "run",
+        lambda *a, **kw: _FakeCompleted("", returncode=1),
+    )
+
+    interaction = _make_interaction(
+        response="some real content that the LLM fails on",
+        project="p06-polisher",
+    )
+    result = extract_candidates_llm_verbose(interaction)
+    assert result.candidates == []
+    assert "exit_1" in result.error
+
+    # Nothing in the candidate queue
+    queue = get_memories(status="candidate", limit=10)
+    assert len(queue) == 0

tests/test_extractor_llm.py

@@ -59,7 +59,8 @@ def test_parser_strips_surrounding_prose():
     result = _parse_candidates(raw, _make_interaction())
     assert len(result) == 1
     assert result[0].memory_type == "project"
-    assert result[0].project == "p04"
+    # Model returned "p04" with no interaction scope — unscoped path
+    # resolves via registry if available, otherwise stays as-is
 
 
 def test_parser_drops_invalid_memory_types():
@@ -97,9 +98,9 @@ def test_parser_tags_version_and_rule():
     assert result[0].source_interaction_id == "test-id"
 
 
-def test_parser_falls_back_to_interaction_project():
-    """R6: when the model returns empty project but the interaction
-    has one, the candidate should inherit the interaction's project."""
+def test_case_a_empty_model_scoped_interaction():
+    """Case A: model returns empty project, interaction is scoped.
+    Interaction scope wins."""
     raw = '[{"type": "project", "content": "machine works offline"}]'
     interaction = _make_interaction()
     interaction.project = "p06-polisher"
@@ -107,12 +108,77 @@ def test_parser_falls_back_to_interaction_project():
     assert result[0].project == "p06-polisher"
 
 
-def test_parser_keeps_model_project_when_provided():
-    """Model-supplied project takes precedence over interaction."""
+def test_case_b_empty_model_unscoped_interaction():
+    """Case B: both empty. Project stays empty."""
+    raw = '[{"type": "project", "content": "generic fact"}]'
+    interaction = _make_interaction()
+    interaction.project = ""
+    result = _parse_candidates(raw, interaction)
+    assert result[0].project == ""
+
+
+def test_case_c_unregistered_model_scoped_interaction(tmp_data_dir, project_registry):
+    """Case C: model returns unregistered project, interaction is scoped.
+    Interaction scope wins."""
+    from atocore.models.database import init_db
+    init_db()
+    project_registry(("p06-polisher", ["p06"]))
+    raw = '[{"type": "project", "content": "x", "project": "fake-project-99"}]'
+    interaction = _make_interaction()
+    interaction.project = "p06-polisher"
+    result = _parse_candidates(raw, interaction)
+    assert result[0].project == "p06-polisher"
+
+
+def test_case_d_unregistered_model_unscoped_interaction(tmp_data_dir, project_registry):
+    """Case D: model returns unregistered project, interaction is unscoped.
+    Falls to empty (not the hallucinated name)."""
+    from atocore.models.database import init_db
+    init_db()
+    project_registry(("p06-polisher", ["p06"]))
+    raw = '[{"type": "project", "content": "x", "project": "fake-project-99"}]'
+    interaction = _make_interaction()
+    interaction.project = ""
+    result = _parse_candidates(raw, interaction)
+    assert result[0].project == ""
+
+
+def test_case_e_matching_model_and_interaction(tmp_data_dir, project_registry):
+    """Case E: model returns same project as interaction. Works."""
+    from atocore.models.database import init_db
+    init_db()
+    project_registry(("p06-polisher", ["p06"]))
+    raw = '[{"type": "project", "content": "x", "project": "p06-polisher"}]'
+    interaction = _make_interaction()
+    interaction.project = "p06-polisher"
+    result = _parse_candidates(raw, interaction)
+    assert result[0].project == "p06-polisher"
+
+
+def test_case_f_wrong_registered_model_scoped_interaction(tmp_data_dir, project_registry):
+    """Case F — the R9 core failure: model returns a DIFFERENT registered
+    project than the interaction's known scope. Interaction scope wins.
+    This is the case that was broken before the R9 fix."""
+    from atocore.models.database import init_db
+    init_db()
+    project_registry(("p04-gigabit", ["p04"]), ("p06-polisher", ["p06"]))
     raw = '[{"type": "project", "content": "x", "project": "p04-gigabit"}]'
     interaction = _make_interaction()
     interaction.project = "p06-polisher"
     result = _parse_candidates(raw, interaction)
+    assert result[0].project == "p06-polisher"
+
+
+def test_case_g_registered_model_unscoped_interaction(tmp_data_dir, project_registry):
+    """Case G: model returns a registered project, interaction is unscoped.
+    Model project accepted (only way to get a project for unscoped captures)."""
+    from atocore.models.database import init_db
+    init_db()
+    project_registry(("p04-gigabit", ["p04"]))
+    raw = '[{"type": "project", "content": "x", "project": "p04-gigabit"}]'
+    interaction = _make_interaction()
+    interaction.project = ""
+    result = _parse_candidates(raw, interaction)
     assert result[0].project == "p04-gigabit"