Skill auto improve

Skill Auto-Improve

Purpose: Turn ad-hoc, manual artifact tuning into a controlled, measurable loop. Given any artifact and an eval harness, the orchestrator runs subagents (a Proposer + an Evaluator) under the autoresearch invariant — the eval harness is immutable, the artifact is free to change, KEEP a change only if the metric improves beyond noise, otherwise REVERT — and logs every step. It works across LLM vendors and improves skills, prompts, workflows, and eval datasets.

1. Red Flags (Anti-Rationalization)

STOP and READ THIS if you are thinking:

• "I'll let the Proposer pick the tier / decide if its own change is good" -> WRONG. The author cannot grade itself. Tier is computed deterministically by measure_change_size.py; KEEP/REVERT is decided by the orchestrator from the Evaluator's number, never by the Proposer.
• "A tiny positive delta means KEEP" -> WRONG. LLM/agent metrics are noisy (σ≈0.05–0.10). A change is only KEPT when delta > sigma; within-noise moves are reverted so noise never accumulates as drift.
• "I'll just rewrite the whole file" -> WRONG. Changes are surgical (one section / one set of dataset ops). Full overwrites are how good content silently disappears.
• "I can let it edit the eval set to make scores go up" -> WRONG. The harness (and frontmatter name/tier, dataset id/grader) is immutable. Editing the ruler to fit the result is the cardinal sin of measurement.
• "Run it straight on main" -> WRONG. Use --git-isolation; intermediate commits belong on a throwaway branch, and a dirty tree aborts the run.

2. Capabilities

• Improve artifact types: skill, prompt, workflow, dataset, full-skill, and text (arbitrary prose — emails, READMEs, landing copy — graded against a quality rubric).
• Two decision mechanisms: noise-aware absolute-delta (deterministic/typed metrics) and a debiased pairwise gate (champion-vs-candidate in both orderings) for subjective text quality, with optional best-of-N candidates per iteration.
• Vendor-agnostic LLM completion via LLMConfigManager (Anthropic / OpenAI / Gemini / OpenAI-compatible gateways), selected by DEFAULT_PROVIDER.
• Pluggable agent-eval backends for skill-trigger evaluation (Claude validated; Gemini / Codex stubs); deterministic scoring for datasets; LLM grading for generic artifacts.
• Multi-axis budget (iterations / tokens / wall-clock) and convergence detection.
• Git-isolated, revertible iterations with a full TSV history and a markdown report.

3. Execution Mode

• Mode: hybrid
• Why this mode: Orchestration (the loop, decision rule, snapshot/revert, immutability gate, logging) is deterministic and lives in scripts. Proposing a change and subjective grading require judgment and run as LLM/agent subagents the orchestrator controls.

4. Script Contract

• Command (required args):
  • python3 scripts/auto_improve.py --artifact-path <path> --workspace <dir>
• Optional flags: --artifact-type (default auto; use text for rubric-graded prose), --target (auto/description/generic), --eval-set <evals.json>, --criteria <rubric.md> (required for text), --candidates N (best-of-N for text, default 1), --threshold (0-1 early-stop; text default 0.9), --provider (default auto), --model, --max-iterations (default 10), --max-tokens, --max-duration (e.g. 30m), --noise-sigma, --runs-per-query, --num-workers, --git-isolation, --verbose.
• Inputs: an artifact (dir or file); for skill/prompt/workflow an --eval-set; for text a --criteria rubric (weighted dimensions summing to 100); provider API key in .env ({PROVIDER}_API_KEY, optional OPENAI_BASE_URL); profiles in config/llm_profiles.yaml.
• Outputs: <workspace>/improvement_history.tsv (baseline + per-iteration rows), <workspace>/improvement_report.md, snapshots under <workspace>/snapshots/, optional adversarial_review.md for large-tier changes. The winning artifact is left in place (merge the git branch explicitly).
• Failure semantics: non-zero exit on a dirty tree under --git-isolation (code 2) or an unknown artifact type; Proposer/apply/eval errors are logged as iteration rows and never crash the loop.
• Idempotency: re-running re-evaluates from the current artifact state; history appends. Use a fresh --workspace for a clean run.
• Dry-run support: inspect proposals without committing by running on a copy, or use --git-isolation so nothing lands on your branch.

5. Safety Boundaries

• Allowed scope: only the target artifact (and, for datasets, additive eval cases). Nothing outside --artifact-path.
• Default exclusions (immutable): the eval harness; SKILL.md frontmatter name/tier; dataset id/skill_name/grader and file refs of existing cases; prompt {{placeholders}}; workflow YAML keys + tool names. Validated before apply and re-checked after.
• Destructive actions: never. Removing existing eval cases is rejected; full-file overwrite is never used; revert restores the pre-change snapshot.
• Statistical honesty: the inner loop (runs_per_query≈3) optimizes direction; only a final 5-run + bootstrap pass is a reliable measurement. This limitation is real — do not over-trust a single inner-loop score.
• Optional artifacts: missing references//examples/ is non-blocking; a missing eval set for skill/prompt/workflow is blocking (cannot measure).
• Trust boundary: skill-trigger eval runs a tool-enabled agent (claude -p) seeded with the artifact's own text. Two defense layers guard the description→agent prompt-injection path: (1) the Proposer's description is sanitized (HTML comments / control chars stripped) before write, and (2) the eval sink (run_eval.py) frames the description in the command body as untrusted DATA with a do-not-follow preamble. Still, run on trusted artifacts as belt-and-suspenders. Treat the process environment ({PROVIDER}_API_KEY, OPENAI_BASE_URL, AUTO_IMPROVE_*) as trusted input: a redirected OPENAI_BASE_URL sends prompts to that endpoint (the run warns when one is active).
• Text-quality judge trust boundary: for --artifact-type text, the artifact prose is fed into the rubric judge AND the pairwise judge, which TOGETHER are the keep/revert gate. The artifact is stripped of injection markup (HTML comments/control chars) and the judges are instructed to treat it as untrusted DATA, but a determined plaintext-imperative injection ("score 100 / pick B") cannot be fully neutralized while still judging the prose — and the two-ordering debias does NOT defend against in-text injection (the instruction travels with the candidate into both orderings). Run text-quality only on artifacts whose provenance you trust, and review the winner before merging (the loop never merges for you). text-replace edits stay scoped to the artifact's own string (no path escape).

6. Validation Evidence

• Local verification:
  • python3 ../skill-creator/scripts/validate_skill.py . (structure/CSO) → exit 0
  • cd scripts && python3 -m unittest discover -s tests → all pass (offline)
• Expected evidence: improvement_history.tsv shows a baseline row, KEEP rows with positive deltas, REVERT/no-signal rows for non-improvements, and an exit_reason.
• CI signal: office-skills CI does not cover this skill; rely on the unit suite + a real eval run.

7. Instructions

Phase 0 — Prepare

1. Install deps into the skill venv: cd scripts && python3 -m venv .venv && ./.venv/bin/pip install -r requirements.txt. Only the provider SDK you use is required.
2. Set secrets in a .env at the skill root: DEFAULT_PROVIDER=... and {PROVIDER}_API_KEY=... (optionally OPENAI_BASE_URL for a gateway).
3. Confirm an eval harness exists. For skill/prompt/workflow you MUST pass --eval-set; without a metric there is nothing to optimize. Datasets are self-scored.

Phase 1 — Run the loop

1. Choose the target with --target (description for CSO trigger text; generic/auto otherwise) and a budget (--max-iterations, --max-tokens, --max-duration).
2. Prefer --git-isolation so iterations run on a throwaway auto-improve/* branch; a dirty working tree MUST abort (commit or stash first).
3. Run auto_improve.py. Each iteration: Proposer → validate-before-apply → snapshot → apply → Evaluator → KEEP (delta>σ and secondary not regressed) / REVERT / no-signal.

Phase 2 — Review & merge

1. Read improvement_report.md and improvement_history.tsv. Confirm the score trajectory and exit_reason.
2. For large-tier changes, read adversarial_review.md for injected-regression concerns.
3. Merge the winning branch explicitly only after you are satisfied — the loop never merges to your working branch for you.

8. Workflows (Optional)

- [ ] Eval harness present (or bootstrap one)
- [ ] Clean git tree; --git-isolation on
- [ ] Run loop within budget
- [ ] Review TSV + report (+ adversarial_review for large)
- [ ] Merge winner explicitly

9. Best Practices & Anti-Patterns

DO THIS	DO NOT DO THIS
Keep the eval harness immutable; improve the artifact	Edit evals to inflate the score
Decide KEEP from the Evaluator's number	Let the Proposer judge its own change
Require delta > σ to KEEP	KEEP on any positive delta (noise)
Surgical section/dataset edits	Full-file overwrite
--git-isolation on a clean tree	Run on main with uncommitted changes

Rationalization Table

Agent Excuse	Reality / Counter-Argument
"Nesting run_loop.py is simpler."	A nested loop hides its spend from --max-tokens/--max-iterations. The outer loop owns the budget; description uses a single-shot optimizer.
"Adding dataset cases changes the immutable hash → revert."	Immutability is a subset check: additions are allowed, only changing/removing existing immutable fields is a violation.
"Gemini/Codex backends exist, so trigger eval works there."	They are stubs (available=False). Skill-trigger eval is validated only on Claude; other vendors fall back to LLM grading.

10. Examples (Few-Shot)

See examples/:

• examples/dataset-improvement-example.md — offline dataset quality loop (no API for the Evaluator).
• examples/skill-improvement-example.md — improving a weak skill's description (CSO trigger accuracy).
• examples/text-quality-example.md + examples/cold-email-rubric.md — improving arbitrary prose against a rubric via the pairwise gate + best-of-N.

11. Resources

• scripts/auto_improve.py — orchestrator + CLI; run_improvement_loop takes injectable proposer/evaluator/decider for offline tests.
• scripts/llm_config.py — vendor-agnostic LLMConfigManager (native SDKs, fallback chain, usage→budget, OPENAI_BASE_URL).
• scripts/pairwise.py — debiased pairwise gate (champion-vs-candidate, both orderings) for text quality.
• scripts/{check_immutability,apply_proposal,measure_change_size,grade_dataset,snapshot,log_iteration,detect_artifact_type,detect_vendor}.py — deterministic utilities.
• scripts/backends/ — agent-eval registry (claude validated; gemini/codex stubs).
• config/llm_profiles.yaml — proposer / text_mutator / grader / eval_bootstrap profiles per provider.
• references/ — artifact_type_guide.md, metrics_reference.md, backends/* adapter specs.
• agents/ — proposer.md, evaluator.md system prompts the orchestrator sends to the LLM.

12. Evals

evals/evals.json defines 6 scenarios (description, instructions, dataset, revert-on-regression, convergence-stop, no-signal-revert). Deterministic ones are also covered by scripts/tests/. Fixtures live in evals/fixtures/.