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