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docs: Add OP_08 report generation and update protocol numbering

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- Add OP_08 to cheatsheet task lookup table
- Create Report Generation section in cheatsheet
- Update SYS_16/17/18 numbering (SAT, Insights, Context)
- Create StatusBadge component for dashboard
- Create OP_08_GENERATE_REPORT.md protocol document

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Anto01
2026-01-07 08:55:56 -05:00
parent b8a04c62b8
commit 155e2a1b8e
2 changed files with 157 additions and 6 deletions
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54
.claude/skills/01_CHEATSHEET.md
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@@ -31,10 +31,12 @@ requires_skills:
| Export neural training data | OP_05 | `python run_optimization.py --export-training` |
| Fix an error | OP_06 | Read error log → follow diagnostic tree |
| **Free disk space** | **OP_07** | `archive_study.bat cleanup <study> --execute` |
| **Generate report** | **OP_08** | `python -m optimization_engine.future.report_generator <study>` |
| Add custom physics extractor | EXT_01 | Create in `optimization_engine/extractors/` |
| Add lifecycle hook | EXT_02 | Create in `optimization_engine/plugins/` |
| Generate physics insight | SYS_16 | `python -m optimization_engine.insights generate <study>` |
| **Manage knowledge/playbook** | **SYS_17** | `from optimization_engine.context import AtomizerPlaybook` |
| **Use SAT (Self-Aware Turbo)** | **SYS_16** | SAT v3 for high-efficiency neural-accelerated optimization |
| Generate physics insight | SYS_17 | `python -m optimization_engine.insights generate <study>` |
| **Manage knowledge/playbook** | **SYS_18** | `from optimization_engine.context import AtomizerPlaybook` |
---
@@ -384,12 +386,13 @@ Without it, `UpdateFemodel()` runs but the mesh doesn't change!
| 13 | Dashboard | Real-time tracking and visualization |
| 14 | Neural | Surrogate model acceleration |
| 15 | Method Selector | Recommends optimization strategy |
| 16 | Study Insights | Physics visualizations (Zernike, stress, modal) |
| 17 | Context Engineering | ACE framework - self-improving knowledge system |
| 16 | Self-Aware Turbo | SAT v3 - high-efficiency neural optimization |
| 17 | Study Insights | Physics visualizations (Zernike, stress, modal) |
| 18 | Context Engineering | ACE framework - self-improving knowledge system |
---
## Study Insights Quick Reference (SYS_16)
## Study Insights Quick Reference (SYS_17)
Generate physics-focused visualizations from FEA results.
@@ -572,7 +575,7 @@ convert_custom_to_optuna(db_path, study_name)
---
## Context Engineering Quick Reference (SYS_17)
## Context Engineering Quick Reference (SYS_18)
The ACE (Agentic Context Engineering) framework enables self-improving optimization through structured knowledge capture.
@@ -672,3 +675,42 @@ feedback.process_trial_result(
| Context API | `http://localhost:5000/api/context` | Playbook management |
**Full documentation**: `docs/protocols/system/SYS_18_CONTEXT_ENGINEERING.md`
---
## Report Generation Quick Reference (OP_08)
Generate comprehensive study reports from optimization data.
### Quick Commands
| Task | Command |
|------|---------|
| Generate markdown report | `python -m optimization_engine.future.report_generator <study> --format markdown` |
| Generate HTML report | `python -m optimization_engine.future.report_generator <study> --format html` |
| Generate via API | `POST /api/optimization/studies/{study_id}/generate-report` |
### Python API
```python
from optimization_engine.future.report_generator import generate_study_report
from pathlib import Path
# Generate markdown report
output = generate_study_report(
study_dir=Path("studies/my_study"),
output_format="markdown"
)
print(f"Report saved to: {output}")
```
### Report Contents
| Section | Data Source |
|---------|-------------|
| Executive Summary | Calculated from trial stats |
| Best Result | `study.db` best trial |
| Top 5 Designs | Sorted by objective |
| Optimization Progress | Trial history |
**Full details**: `docs/protocols/operations/OP_08_GENERATE_REPORT.md`