Autorun

# Seamless-RAG Autonomous Development Loop

You are an autonomous agent building a championship-winning RAG toolkit for the MariaDB Hackathon MY 2026. Execute this loop INDEFINITELY until the user explicitly stops you with Ctrl+C.

**NEVER stop to ask the user anything. NEVER say "shall I continue". Just keep going.**

## Startup Checklist

1. Run `conda run -n seamless-rag python -m pytest tests/unit --co -q 2>&1 | tail -3` to verify env works
2. Run `conda run -n seamless-rag python scripts/score.py` to see current dashboard
3. Read `TODO.md` for what needs doing
4. Read `docs/SPECIFICATION.md` for current state
5. Begin the main loop at Phase 1

## Main Loop (repeat forever)

### Phase 1: ASSESS — What's the next task?

conda run -n seamless-rag python scripts/score.py

Read `TODO.md`. Pick the **first unchecked item** in priority order.
If all are checked, look for quality improvements (coverage gaps, flaky tests, missing edge cases, docs).

### Phase 2: RESEARCH — Understand before coding

**RULE: Never guess. Always verify. Use these tools:**

**For understanding a library API or TOON spec detail:**

Use Agent tool with subagent_type="Explore" to search the reference code: prompt: "Find how the TypeScript TOON encoder handles tabular arrays. Look in /Users/sunfl/Documents/study/MSrag/references/p0-core/toon-official/packages/toon/src/encode/"

**For current best practices or library docs:**

Use WebSearch tool: query: "mariadb-connector-python array.array vector insert example 2026"

**For complex technical questions requiring deep research:**

Use Agent tool (general-purpose, background): prompt: "Research how to implement exponential backoff retry in Python for a database polling loop. Find production-grade patterns. Return code examples."

**For reading specific documentation pages:**

Use WebFetch tool: url: "https://mariadb.com/kb/en/vector-overview/"

**For multi-faceted investigation (spawn a team):**

Launch multiple Agent tools in parallel: Agent 1: "Research TOON v3 spec Section 7.2 quoting rules by reading /references/p0-core/toon-spec/SPEC.md" Agent 2: "Search web for Python regex patterns for TOON numeric detection" Agent 3: "Read the TypeScript reference encoder at /references/p0-core/toon-official/packages/toon/src/encode/primitives.ts"

### Phase 3: IMPLEMENT — TDD cycle

1. **Read the failing test** carefully — understand exactly what it expects
2. **Read reference code** if the implementation requires specific API knowledge
3. **Write the minimum code** to pass the test
4. **Let the PostToolUse hook** run tests automatically after each edit
5. If tests pass: move to next failing test
6. If tests fail: read the error, fix the implementation (NOT the test)
7. Repeat until all tests in the current component pass

### Phase 4: VERIFY — Broader checks

After a component passes its unit tests:
```bash
conda run -n seamless-rag python -m pytest tests/unit -v --tb=short   # all unit tests
conda run -n seamless-rag ruff check src/seamless_rag/                # lint
conda run -n seamless-rag python scripts/score.py                     # score dashboard

Phase 5: CODEX REVIEW — Quality gate

Before any commit of a new feature or significant refactor, get a Codex review:

Use Agent tool with subagent_type="codex:codex-rescue":
  prompt: "Review the TOON v3 tabular encoder implementation at
           /Users/sunfl/Documents/study/MSrag/workspace/src/seamless_rag/toon/encoder.py

           Check for:
           1. Correctness against TOON v3 spec (quoting rules, escape sequences, number canonicalization)
           2. Edge cases: null, empty string, commas in values, newlines, unicode, negative zero
           3. Code quality: type hints, readability, no unnecessary complexity
           4. Performance: no quadratic algorithms for large datasets

           Rate the code A/B/C/D and list specific issues to fix."

Fix ALL issues Codex identifies before committing. If Codex rates B or lower on critical code (TOON encoder, RAG engine), iterate until it's an A.

Phase 6: COMMIT & DOCUMENT

# Stage specific files (NEVER git add . or git add -A)
git add src/seamless_rag/toon/encoder.py tests/unit/test_toon_encoder.py

# Atomic commit with conventional prefix
git commit -m "feat: implement TOON v3 tabular encoder with value quoting and number canonicalization"

# Push
git push origin main

Then update live docs:

• Mark the item as [x] in TODO.md
• Update docs/SPECIFICATION.md with what's now implemented
• Update docs/HANDOFF.md milestones

Phase 7: ANTI-IDLE & RETRY — Never get stuck, never stop

Retry policy per failing test (3 escalating attempts):

1. Read the error message carefully, fix the obvious issue
2. Read reference code (TOON spec, TypeScript encoder, winner projects in /references/)
3. Spawn a research Agent to investigate the root cause with WebSearch

If all 3 attempts fail on one test:

• Log it to docs/ISSUES.md with full context (error, what you tried, what you suspect)
• Mark the TODO item as [~] (blocked)
• IMMEDIATELY move to the next unblocked TODO item — do NOT dwell

Retry policy per blocked task:

• If a task depends on an unfinished prior task, skip it and pick next independent task
• If ALL remaining tasks are blocked, revisit blocked items with a completely different approach
• If still stuck: use Agent(subagent_type="codex:codex-rescue") for a second opinion

Anti-idle rules (CRITICAL):

• NEVER pause to "think about it" without taking action — code, research, or move on
• NEVER output "Let me know if..." or "Would you like me to..." — just do it
• NEVER wait for user input — make the decision yourself
• If you finish P0 items, immediately start P1. If P1 done, start P2. Always forward.
• Between major tasks, ALWAYS run make score to see progress
• If you complete a milestone (e.g., all TOON tests pass): c