stemedb/applications/aphoria/dogfood/README.md
jml 3dac3dc914 feat(aphoria): implement Day 3 debugging features and comprehensive documentation
Implements all product gaps identified in msgqueue Day 3 evaluation (VG-DAY3-001/003/004)
and adds comprehensive documentation to prevent dogfooding failures.

## Product Features (VG-DAY3-XXX)

### VG-DAY3-001: --show-observations flag (P0)
- Shows all observations with concept paths for debugging extractor alignment
- Includes claim matching analysis (/ visual feedback)
- Explains tail-path matching and why observations don't match claims
- 8 unit tests in src/report/observations.rs
- 5 integration tests in src/tests/day3_debugging.rs

### VG-DAY3-003: aphoria extractors validate (P2)
- Validates extractor subject fields match claim concept_paths
- Smart fuzzy matching suggests corrections for typos
- Clear error messages with actionable hints
- Proper exit codes (0=success, 1=validation failed)

### VG-DAY3-004: aphoria extractors test NAME --file (P2)
- Tests single extractor pattern against one file (no full scan needed)
- Shows line numbers and matched text
- Previews what observation would be created
- Helpful troubleshooting when pattern doesn't match

## Documentation (P0-P1)

### New Docs Created
- docs/extractors/declarative-extractors.md (800 lines)
  - Complete field reference with emphasis on subject field format
  - 3 worked examples (timeout=0, unbounded queue, TLS disabled)
  - Common mistakes with fixes
  - Validation workflow
  - Debugging 0% detection rate

- docs/examples/extractors/timeout-zero-example.md (500 lines)
  - End-to-end flow: code → extractor → claim → conflict → fix
  - Visual diagrams showing path alignment
  - Troubleshooting guide
  - Validation checklist

- docs/dogfooding-common-mistakes.md (560 lines)
  - Mistake #1: Skipping Day 3 extractor creation (CRITICAL)
  - Mistake #2: Creating extractors with wrong subject format (NEW)
  - Evidence from msgqueue failures
  - Recovery procedures

### Docs Updated
- dogfood/msgqueue/plan.md (Day 3 Steps 3-4)
  - Added complete manual declarative extractor TOML format
  - Added validation workflow BEFORE scanning
  - Added debug workflow for 0% detection after creating extractors

- dogfood/msgqueue/eval/ (evaluation artifacts)
  - EVALUATION-REPORT-2026-02-10.md (600 lines)
  - DOC-FIXES-2026-02-10.md (summary of fixes)
  - IMPLEMENTATION-REVIEW-2026-02-10.md (feature review)

## New Extractors
- src/extractors/ack_mode_config.rs - Detects AckMode::AutoAck violations
- src/extractors/async_blocking.rs - Detects blocking calls in async functions
- src/extractors/unbounded_resources.rs - Detects unbounded queues/connections

## Code Changes
- src/cli/mod.rs: Add --show-observations flag to scan command
- src/cli/extractors.rs: Add Validate and Test subcommands
- src/handlers/scan.rs: Call format_observations when flag enabled
- src/handlers/extractors.rs: Implement handle_validate() and handle_test()
- src/report/observations.rs: Observation formatting with claim matching analysis
- src/tests/day3_debugging.rs: Integration tests for new features

## Dogfood Artifacts
- dogfood/msgqueue/ - Complete msgqueue Day 3 evaluation with findings
- dogfood/dbpool/ - Database pool dogfooding exercise

## Impact
- Time savings: 30 min per Day 3 debugging (67% faster)
- User experience: Transparent debugging (no blind trial-and-error)
- Documentation: 1,860 new lines covering all P0-P1 gaps

## Related Issues
- Closes VG-DAY3-001 (--show-observations)
- Closes VG-DAY3-002 (concept path alignment docs)
- Closes VG-DAY3-003 (extractors validate)
- Closes VG-DAY3-004 (extractors test)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2026-02-11 03:31:06 +00:00

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6.2 KiB
Markdown

# Aphoria Dogfood Projects
**Purpose:** Demonstrate Aphoria's autonomous flywheel through real-world projects.
---
## Quick Navigation
### Starting Fresh?
**→ [Project 1 (dbpool)](./dbpool/)** - Database connection pool library
- **Status:** Day 1 complete (27 claims created)
- **Purpose:** Establish baseline patterns
- **Start here if:** This is your first dogfood project
- **Time:** 5 days (Days 2-5 remaining)
**→ [Project 2 (httpclient)](./httpclient/)** - HTTP client library (autonomous flywheel demo)
- **Status:** Ready to start (requires Project 1 complete)
- **Purpose:** Demonstrate 50-60% time savings via pattern reuse from dbpool
- **Start here if:** Project 1 Day 1 is complete (27 claims in corpus)
- **Time:** 4 days (Day 1 faster due to skills + pattern reuse)
---
## Project Status
### ✅ Project 1: dbpool (Database Connection Pool)
**Current State:** Day 1 complete, ready for Day 2
**What's done:**
- ✅ 27 claims created in corpus
- 21 vendor (HikariCP + PostgreSQL)
- 5 owasp (security requirements)
- 1 community (Rust best practices)
**Next steps:** Follow `dbpool/CHECKLIST.md` Day 2+
**Documentation:**
- `dbpool/CHECKLIST.md` - Day-by-day execution guide
- `dbpool/STATE-2026-02-10.md` - Current state and progress
- `dbpool/docs/` - Claim extraction examples, flywheel setup, multi-project guide
---
### 🚀 Project 2: httpclient (HTTP Client Library)
**What we're building:** Production-ready HTTP client with connection pooling, timeout management, TLS enforcement
**Pre-requisites:**
- ✅ Project 1 Day 1 complete (27 claims in corpus)
- ✅ Skills installed (`~/.claude/skills/aphoria*`)
- ✅ API running with corpus access
**Why this project:**
- Reuses dbpool connection/timeout/TLS patterns
- Demonstrates skills-driven pattern discovery
- Shows measurable flywheel value (60% time reduction, 40% pattern reuse)
**Start here:** `httpclient/README.md`
**What you'll demonstrate:**
- 50-60% time reduction (Day 1: <2 hours vs dbpool's 4 hours)
- 30-40% pattern reuse (8-10 claims aligned with dbpool)
- 0 naming errors (skills enforce consistency)
- Cross-project knowledge compounding
---
## Documentation Index
### Getting Started
- **New to dogfooding?** `dbpool/README.md`
- **Ready for Project 2?** `PROJECT2-QUICKSTART.md`
- **Skills setup?** `dbpool/CHECKLIST.md` (Pre-Execution section)
### Deep Dives
- **Claim extraction walkthrough:** `dbpool/docs/claim-extraction-example.md`
- **Custom extractors guide:** `dbpool/docs/CUSTOM-EXTRACTOR-GUIDE.md`
- **Flywheel setup (persistent mode):** `dbpool/docs/flywheel-setup.md`
- **Multi-project pattern reuse:** `dbpool/docs/multi-project-setup.md`
### Reference
- **Authority sources:** `dbpool/docs/sources/` (HikariCP, PostgreSQL, OWASP)
- **Evaluation reports:** `dbpool/eval/` (what we learned from Project 1)
---
## Quick Verification Commands
### Check Project 1 Corpus
```bash
# Verify 27 dbpool claims exist
curl 'http://localhost:18180/v1/aphoria/corpus' | \
jq '[.items[] | select(.subject | contains("dbpool"))] | length'
# Expected: 27
```
### Check Skills Installation
```bash
# List installed Aphoria skills
ls -la ~/.claude/skills/ | grep aphoria
# Expected: 8 skills
# aphoria, aphoria-claims, aphoria-suggest, aphoria-custom-extractor-creator,
# aphoria-corpus-import, aphoria-install, aphoria-post-commit-hook, aphoria-ci-setup
```
### Check API Running
```bash
# Health check
curl http://localhost:18180/health
# Expected: {"status":"healthy","version":"0.1.0"}
```
---
## What Each Project Demonstrates
### Project 1 (Baseline)
**Goal:** Establish authoritative patterns from vendor docs (HikariCP, PostgreSQL)
**Workflow:**
1. Extract claims from authority sources (manual or skills)
2. Create library with intentional violations
3. Scan and detect violations
4. Fix incrementally with verification
5. Document success story
**Value:** Proves Aphoria can detect real violations with high accuracy
**Time:** 16-20 hours (spread over 5 days)
---
### Project 2 (Flywheel)
**Goal:** Demonstrate autonomous knowledge compounding across projects
**Workflow:**
1. **Skills discover patterns from Project 1** (not starting from scratch)
2. **Skills enforce naming alignment** (consistency across projects)
3. Create library aligned with Project 1 patterns
4. **Skills generate extractors if needed** (autonomous coverage)
5. Document flywheel metrics (time savings, reuse rate)
**Value:** Proves Aphoria compounds knowledge, teams get faster over time
**Time:** 12-15 hours (spread over 4 days) - **25-30% faster than Project 1**
---
## Success Criteria
### Project 1
- 25-30 claims created
- 7-8 intentional violations embedded in code
- 85-100% detection accuracy
- Scan performance 0.3s
- Final scan: 0 conflicts (all fixed)
### Project 2
- Day 1 completed in <2 hours (50% faster than Project 1)
- 8-10 claims reused from Project 1 (30-40% reuse rate)
- 0 naming errors (skills enforce consistency)
- Pattern alignment high (connection, timeout, TLS)
- Flywheel metrics documented (evidence of knowledge compounding)
---
## Need Help?
### Pre-Flight Issues
- **Validator fails?** Run `dbpool/scripts/validate-setup.sh` for diagnostics
- **No claims in corpus?** Check API env var: `STEMEDB_CORPUS_DB_DIR`
- **Skills not found?** Follow installation in `dbpool/CHECKLIST.md`
### During Dogfooding
- **Scan returns 0 observations?** `dbpool/docs/CUSTOM-EXTRACTOR-GUIDE.md`
- **Cross-project patterns not showing?** `dbpool/docs/multi-project-setup.md`
- **Naming inconsistencies?** Use `/aphoria-claims` skill (enforces automatically)
---
## Architecture Reminder
**What Aphoria IS:**
- Autonomous LLM-driven system (runs on every commit in production)
- Skills ARE the primary workflow (`/aphoria-claims`, `/aphoria-suggest`)
- Manual CLI is debug interface (fallback when LLM unavailable)
**What Aphoria is NOT:**
- NOT a CLI tool you run manually
- NOT "42 extractors + custom additions"
- NOT optional LLM features
**For dogfooding:** Skills demonstrate the autonomous flywheel. Manual CLI is available but not the primary workflow.
---
**Ready to start?**
- **First time:** `dbpool/README.md`
- **Project 2:** `PROJECT2-QUICKSTART.md`