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Adaptive evaluation loop for AI skill collections, inspired by Karpathy's AutoResearch.
Orchestrates repeated score-improve-verify cycles using skill-creator as the engine
and mandatory peer review as an adversarial check (cross-model when a secondary harness
is available, fresh-context self-review as the minimum fallback).
When to use
Batch-improving the entire skill collection after a period of manual edits
Targeted improvement of one named skill when the user explicitly asks for skill-refiner,
multiple iterations, a score target, or "no ceiling" polish
Running quality sweeps before a release or publish
Triggering a self-improvement cycle where skills bootstrap each other
After adding several new skills that need polish and consistency alignment
When cross-model perspective would catch single-model blind spots
Periodic maintenance: scheduled improvement runs to keep skills current
When NOT to use
Creating a new skill from scratch - use skill-creator (Mode 1)
Single-pass review of one skill without iteration, scoring, or peer review - use skill-creator (Mode 2)
One-off collection audit without iteration - use skill-creator (Mode 3)
Full codebase review (code, not skills) - use full-review
Style/slop audit on application code - use anti-slop
Focus threshold - skip skills scoring above this (user can override max)
--plateau
2
Minimum score delta to keep iterating
Environment override:SKILL_REFINER_SECONDARY=<harness> (CLI flag takes precedence)
Checkpoint Modes
circuit-breaker (default): runs autonomously, auto-pauses on score regression,
contested major flags, or plateau. Always pauses before phase 2.
auto: fully autonomous through phase 1. Still pauses before phase 2 and on
contested major flags (non-configurable).
step: pauses after every iteration for manual review. Best for first run or learning.
Performance
Batch similar edits and validations to reduce repeated full-collection scans.
Prioritize low-scoring or recently changed skills before polishing already-healthy ones.
Use focused diffs and line-count checks after each batch to avoid late cleanup churn.
Best Practices
Snapshot evaluation criteria before editing the skills that define the criteria.
Revert changes that add complexity without improving behavior.
Keep run history factual and free of unverifiable score inflation.
Treat composite deltas as noisy when different scorer instances run across
iterations: a few points of swing is judge variance, not real change. Anchor
keep/revert decisions on the structural gate, on whether the specific targeted
weakness was fixed, and on cross-model NO_FLAGS - not on small composite moves.
Use a consistent scoring approach within a single before/after comparison.
Leave externally-maintained version, CVE, and EOL pins out of scope. When a
collection has a freshness routine (or equivalent) that owns version currency,
do not edit those pins during a run and do not score them as "unverifiable"
failures; flag only internal contradictions.
Audit offensive or security skills (privilege escalation, exploit research)
in-loop rather than through web-researching subagents, which can trip platform
safeguards. Keep their scoring and edits in the main session.
Workflow
Phase 0: Setup
Create feature branch: skill-refiner/YYYY-MM-DD-HHMMSS from current HEAD.
Preserve dirty worktrees; branching isolates the run, it does not imply cleanup. If already
on a run branch for this sweep, record it instead of nesting another branch. Do not mix
unrelated dirty files into refiner commits; isolate them with a path-limited stash only
when authorized.
Load run history: read .refiner-runs.json from the repository root, beside
.refiner-ledger.md (if it exists). Repository root, not the skills/ directory: a
second history file in skills/ splits the log and hides prior baselines.
Use previous run data for: baseline score comparison (detect regressions from external
changes), model/harness change detection (flag if the primary or secondary model changed
since last run - new model = new baseline, not a comparable delta), and skip analysis
(don't re-attempt improvements that were already tried and reverted in a recent run).
Build skill inventory: list all skills, exclude phase-2 targets (skill-creator,
skill-refiner) from the improvement pool
Detect primary harness: check environment to identify which AI CLI is running
this session
Probe for secondary harness: run three-step validation (PATH check, config check,
smoke test) per references/harness-detection.md. Announce result.
If no secondary found: always fall back to self-review. Spawn a fresh agent on
the current harness with the review prompt template from references/harness-detection.md.
Label as "same-model fresh-context review" in scoring, weight at 3% instead of 5%
(composite becomes gate/40/55/3, renormalize the missing 2% proportionally to AI Self-Check
and Behavioral). This catches confirmation bias but shares the primary model's blind spots.
Skipping review entirely is not an option - a fresh-context self-review is the minimum bar.
If the harness doesn't support subagents, run the review prompt as a separate CLI
invocation (claude -p, codex exec, gemini -p, etc.).
Phase 1: Regular Iterations
Iteration 1 - full sweep: score every skill in the pool using the four-component
model from references/evaluation-criteria.md
Structural: run lint-skills.sh + validate-spec.sh
AI Self-Check: invoke skill-creator review mode on each skill
Behavioral: run test prompts from references/test-cases.md. For skills without
pre-written test cases, auto-generate 2-3 test prompts from the skill's "When to use"
section and quality signals from its AI Self-Check. Log a warning that generated tests
are lower quality than hand-written ones. Optionally save generated tests to a
references/test-cases-local.md file alongside references/test-cases.md so they accumulate across runs.
Cross-model: skip on first iteration (no diff to review yet)
Log baseline scores: record per-skill and aggregate scores
in a score ledger before any edits. The ledger must include structural gate (G),
AI Self-Check (A), behavioral score (B), cross-model review (X), composite score,
test source, reviewer source, and timestamp. After this step, if the ledger is
missing, incomplete, or only records lint/spec status, pause and backfill scoring before
applying changes. In headless mode, halt the run and report the missing score data.
Iteration 2+: enter adaptive focus mode. For a user-requested single-skill run,
treat that skill as the whole phase-1 pool, run at least the requested iteration count,
and keep iterating until the explicit score target is reached, quality plateaus, or a
circuit breaker fires.
Select targets: identify skills scoring below the focus threshold
For each targeted skill, run the improvement cycle:
a. Read current SKILL.md and all reference files
b. Invoke skill-creator review mode - collect findings
c. Run behavioral test - score current output quality
d. Propose targeted improvements based on findings (not random changes)
e. Apply changes to SKILL.md (and references if needed)
f. Re-score: run lint + AI Self-Check + behavioral test
g. Karpathy gate: if score improved, keep. If not, revert. No exceptions.
h. If cross-model review available, send the diff to secondary harness
i. Process flags per references/harness-detection.md verification protocol
j. If secondary flags major issue and primary agrees: revert
k. If secondary flags major issue and primary disagrees: escalate to circuit breaker
Commit iteration: one commit with all improvements from this iteration
Format: refactor(skill-refiner): iteration N - skill1(+X), skill2(+Y)
Log iteration summary:
Also append the same data to the score ledger. Keep/reject decisions must point to
numeric before/after scores, not reviewer impressions or passing lint/spec checks.
Phase 2: Meta-Improvement
Announce: "Entering phase 2 - meta-improvement. This always requires human review."
Snapshot evaluation criteria:
Copy skill-creator's AI Self-Check section to a temp location
Copy references/evaluation-criteria.md to a temp location
Copy skill-creator's conventions.md reference to a temp location
These snapshots are the evaluation baseline for phase 2.
Improve skill-creator: run the improvement cycle (steps 10a-10k) using the
snapshot as the evaluation criteria, not skill-creator's live version
Improve skill-refiner: same process, using the snapshot
Compare every public skills/*/SKILL.md directory with the canonical ### <skill-name>
headings in references/test-cases.md. Exclude the format-template heading.
Promote stable generated or local cases into the canonical catalog for every gap, then
verify there are no missing, duplicate, or orphan headings. This edit is phase-2-only.
Capture baseline: run both scripts, save full output
Propose improvements
Apply changes
Run regression: compare output to baseline
If false positives or false negatives introduced: revert
If clean: keep
Commit phase 2: one commit per target
Format: refactor(skill-refiner): meta - improve <target> (+N)
Pause for human review: display phase 2 changes, wait for approval.
This checkpoint is non-configurable - it fires even in --mode auto.
A direct user approval such as "continue" or "proceed" counts as approval to resume.
Phase 3: Summary
Final report: write a human-readable report first, then machine-readable run history.
Include branch, pool, config, every changed skill, score before/after, delta, files changed,
verification commands, peer-review flags, reverted changes, private-skill handling, and
skipped checks. If scoring was reconstructed after the fact, label it retroactive and state
which components were not captured during the live loop. Do not output only JSON.
=== skill-refiner run complete ===================================
Branch: skill-refiner/YYYY-MM-DD-HHMMSS
Primary: <harness> <version> (<model>, effort: <level>)
Secondary: <harness> <version> (<model>, effort: <level>) | none
Pool: N skills (skill-creator, skill-refiner excluded)
Config: iterations=M, threshold=T, mode=MODE, plateau=P
Iterations: N (of max M)
Terminated: plateau / threshold / cap / user
Score changes:
skill1: 62 > 88 (+26) [G:pass A:84 B:86 X:90]
skill2: 71 > 85 (+14) [G:pass A:82 B:79 X:100]
...
skill-creator: 80 > 84 (+4) [G:pass A:82 B:81 X:100] [meta]
skill-refiner: 78 > 83 (+5) [G:pass A:80 B:79 X:100] [meta]
Aggregate: avg X.X | min X.X | max X.X
Reverted: X changes across Y iterations
Contested: Z flags escalated to human
=================================================================
Write run history: append this run's metadata to .refiner-runs.json at the
repository root, the same file read in Phase 0 step 2. Include: run_id, branch, date, primary/secondary harness+model+effort,
config, pool size, termination reason, cross-model flag counts, before/after per-skill
scores (component breakdown + composite, or clearly labeled estimates if the run used a
targeted manual rubric instead of the full automated sweep), and a changes summary. When
updating an existing history file, append the new object without reserializing the whole
file; do not normalize or rewrite old entries just because a JSON writer changes escaping,
commas, or whitespace. Commit with the phase 3 summary.
Announce branch: remind user to review and merge when ready
AI Self-Check
Before committing any skill modification, verify:
Lint passes: lint-skills.sh exits 0 for the modified skill
Spec valid: validate-spec.sh exits 0 for the modified skill
Score improved: composite score is strictly higher than before the change
No content regression: change does not remove critical sections, warnings,
or cross-references without replacement
Simplicity maintained: change does not add unnecessary complexity for marginal gains
Cross-references intact: all skill names in bold still resolve to existing skills
Target ~500 lines: modified SKILL.md stays near 500 lines. Hard max 600
ASCII only: no non-ASCII characters introduced (except allowed emoji indicators)
Immutability respected: no phase-1 modification to evaluation criteria,
test cases, lint scripts, skill-creator, or skill-refiner
Current source checked: dated versions, CLI flags, API names, and support windows are verified against primary docs before repeating them
Hidden state identified: local config, credentials, caches, contexts, branches, cluster targets, or previous runs are made explicit before acting
Verification is real: final checks exercise the actual runtime, parser, service, or integration point instead of only linting prose or happy paths
Score discipline kept: changes are kept only when they improve measured quality or fix a verified defect
Score ledger present: baseline, iteration, and final component scores exist before reporting completion
Canonical test coverage complete: phase 2 compares public skill directories with the
canonical test headings and leaves no missing, duplicate, or orphan skill section
Output Contract
See references/output-contract.md for the full contract.
Skill name: SKILL-REFINER
Deliverable bucket:audits
Mode: conditional. When invoked to analyze, review, audit, or improve existing repo content outside the refiner workflow, emit the full contract - boxed inline header, body summary inline plus per-finding detail in the deliverable file, boxed conclusion, conclusion table - and write the deliverable to docs/local/audits/skill-refiner/<YYYY-MM-DD>-<slug>.md. When invoked to run the refiner workflow (its primary mode), use the existing Phase 3 "Final report" format described in the workflow; that build-mode output is unchanged by this contract.
Severity scale:P0 | P1 | P2 | P3 | info (see shared contract; only used in audit/review mode).
Related Skills
skill-creator - the evaluation and improvement engine. skill-refiner invokes
skill-creator's review mode (Mode 2) for scoring and its improve mode for
generating changes. skill-creator handles individual skill quality; skill-refiner
handles iteration, prioritization, and orchestration. Primary dependency.
full-review - one-off collection audit across code-review, anti-slop,
security-audit, and update-docs. Use full-review for a single pass over
application code; use skill-refiner for iterative improvement of skill files.
anti-slop - code quality patterns. skill-refiner may invoke anti-slop
principles through skill-creator during improvement, but does not call anti-slop
directly. Different domain: anti-slop audits application code, skill-refiner
audits skill files.
Rules
Immutability in phase 1: never modify references/evaluation-criteria.md,
references/test-cases.md, lint-skills.sh, validate-spec.sh, skill-creator,
or skill-refiner during phase 1. Violation = abort the run.
Karpathy gate: only directional improvements survive. If a change does not
improve the composite score, revert it. No exceptions, no "it looks better."
Verify flags: never take cross-model flags at face value. Primary reviews
every flag independently. Disagreements on major flags go to human.
Snapshot before meta: always snapshot evaluation criteria before phase 2.
Evaluate against the snapshot, never the live version being modified.
Phase 2 always pauses: even in --mode auto. Non-configurable.
Contested major flags always pause: even in --mode auto. Non-configurable.
Simplicity criterion: all else being equal, simpler is better. Deletions that
maintain score are preferred over additions that marginally improve it.
One commit per iteration: bundle improvements, include score deltas in message.
Branch isolation: all work on a feature branch. Never modify main directly.
Human-readable report required: every run ends with a report that names changes,
before/after scores, verification, peer-review flags, skipped checks, and next action.
Read before edit: always read the full skill before proposing changes.
Never edit from memory or assumption.
No score laundering: do not call a run scored unless component scores were recorded.
Retroactive scoring is allowed only when clearly labeled.
All skills above focus threshold? Bump threshold by 5 and continue. If threshold
is already at max (95) and all skills still clear it, terminate phase 1.
Iteration cap reached? Terminate phase 1.
Circuit breaker triggered? Pause for user input.
Repeat from step 9 until terminated
Local-only scope respected: public and private skills are separated before commits or release notes
