skill-governance-and-automation
Skill5 starsChanged 37 days agoMITView on GitHub
Use when: (1) Your skill repository has grown to 30+ skills and quality is inconsistent, (2) You need to audit skills for stale references, broken links, or missing evidence, (3) You want to automatically generate standardized summary cards for each skill, (4) You're onboarding a new agent and need a quick inventory of available skills. Provides: skill audit checklist (6 quality dimensions), automated skill card template, staleness detection protocol, and skill lifecycle management (create → validate → maintain → deprecate). Inspired by NVIDIA's skill-card-generator, adapted for community skill repositories.
In this repository63 other files it gives its agents.
- AGENTS.mdAGENTS.md
- adversarial-validation-kaggleSkill
- agent-nurture-frameworkSkill
- agent-session-memory-protocolSkill
- arxiv-paper-searchSkill
- autogluon-firstSkill
- autogluon-preset-strategySkill
- autogluon-timeseries-strategySkill
- catboost-first-tabularSkill
- claudeceptionSkill
- code-competition-artifact-pipelineSkill
- competition-orchestration-multimodelSkill
- context-engineeringSkill
- controlled-submission-experimentSkill
- cross-competition-feature-transferSkill
- cv-lb-gap-acknowledgmentSkill
- domain-knowledge-constraints-trapSkill
- ensemble-model-correlation-trapSkill
- feature-engineering-saturation-detectionSkill
- gpu-readiness-assessmentSkill
- gsd-loop-engineeringSkill
- kaggle-cognitive-cost-optimizationSkill
- kaggle-competition-best-practicesSkill
- kaggle-competition-type-strategySkill
- kaggle-data-format-firstSkill
- kaggle-discussion-searchSkill
- kaggle-experiment-sopSkill
- kaggle-oof-lb-validation-protocolSkill
- kaggle-optimal-blendingSkill
- kaggle-submission-format-by-metricSkill
- kaggle-top-performer-replicationSkill
- knowledge-crystallization-feedback-loopSkill
- ladder-drift-and-meta-aware-regressionSkill
- learned-value-beats-heuristic-augmentationSkill
- llm-competition-pipelineSkill
- mcts-pipeline-searchSkill
- mem0-dynamic-graph-memorySkill
- memory-hierarchy-managementSkill
- ml-sweet-spot-principleSkill
- model-ensemble-negative-weight-effectSkill
- multi-agent-rolesSkill
- multi-level-aggregation-overfittingSkill
- okf-visualize-knowledgeSkill
- onnx-minimal-network-designSkill
- per-category-modeling-backfireSkill
- progressive-verification-debuggingSkill
- ptcg-2ply-bounded-searchSkill
- ptcg-bc-large-model-submissionSkill
- retail-eda-frameworkSkill
- rmsle-zero-threshold-asymmetrySkill
- runtime-reflexionSkill
- sc-tir-mathematical-reasoningSkill
- self-evolving-skillsSkill
- spatiotemporal-graph-feature-engineeringSkill
- store-sales-darts-chronos-blendSkill
- tabular-polynomial-features-breakthroughSkill
- three-layer-wisdom-extractionSkill
- time-series-walk-forward-validationSkill
- trueskill-simulation-competition-strategySkill
- ts-day-specific-forecastingSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 23
No standard summary for quick inventory
- Distinctive: line 140
Verify: is the rule still true?
- Distinctive: line 141
If YES → add "classic, still valid" marker + recent evidence
- Distinctive: line 142
If NO → update rule or deprecate
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Skill Governance and Automation
Problem
As a skill repository grows (30+ skills), quality drifts:
- Descriptions become vague (poor activation triggers)
- Evidence sections go stale (cites old competition results)
- Cross-references break ([[links]] to renamed/deleted skills)
- Skills overlap or contradict each other
- No standard summary for quick inventory
The 6-Dimension Skill Audit
For each skill, check:
Dimension 1: Activation Trigger Quality
□ Description starts with "Use when: (1)..." (specific triggers)
□ At least 2 concrete trigger conditions
□ At least 1 "Do NOT use for" exclusion
□ Trigger conditions are testable (not subjective)
BAD: "Tips for Kaggle competitions"
GOOD: "Use when: (1) OOF improved but LB didn't, (2) gap > 1%"Dimension 2: Evidence Currency
□ Evidence section cites real numbers (not "usually helps")
□ At least 1 competition/experiment as source
□ Evidence date < 6 months old (or marked as "classic, still valid")
□ If competition-specific: competition still active or lesson still applies
STALE: "Validated on S6E4 (2026-05)" → check if still true
CURRENT: "Validated on 20+ competitions through 2026-07"Dimension 3: Solution Actionability
□ Solution has concrete steps (not just principles)
□ Code examples or commands provided where relevant
□ Decision trees or flowcharts for complex decisions
□ Anti-patterns section (what NOT to do + why)
VAGUE: "Be careful with ensembles"
ACTIONABLE: "If model correlation > 0.97, remove the weaker model"Dimension 4: Cross-Reference Integrity
□ All [[skill-name]] links point to existing skills
□ No circular dependencies (A → B → A)
□ Related skills are actually related (not just same domain)
□ No two skills cover the same scope without differentiationDimension 5: Scope Boundaries
□ Clear scope: what this skill covers
□ Clear exclusions: what this skill does NOT cover
□ No overlap with another skill > 30%
□ If overlap exists: one is the "primary", other links to itDimension 6: Format Compliance
□ Frontmatter: name, description present and accurate
□ Title: H1 heading matches name
□ Structure: Problem → Context → Solution → Evidence
□ Length: 50-300 lines (not a 1000-line wall, not a 5-line stub)
□ Tags: relevant search keywordsSkill Card Template (Auto-Generatable)
For each skill, generate a standardized 5-line card:
| Skill | Trigger | Key Rule | Evidence | Last Validated |
|-------|---------|----------|----------|----------------|
| autogluon-first | New tabular competition | Run AutoGluon best_quality as step 1 | House Prices CV 0.1180 | 2026-06 |Automation script concept:
# For each skills/examples/*/SKILL.md:
# 1. Parse frontmatter (name, description)
# 2. Extract first "Use when" from description
# 3. Extract first concrete rule from body
# 4. Extract first evidence line
# 5. Check file modification date
# 6. Output summary cardSkill Lifecycle
CREATE:
1. Experiment produces reusable insight
2. Write SKILL.md following format compliance (Dimension 6)
3. Validate: run through all 6 audit dimensions
4. Add to README skills table
5. Cross-reference from related skills
MAINTAIN:
1. Quarterly: re-run 6-dimension audit
2. After new experiments: update evidence if relevant
3. When referenced skill changes: update cross-references
4. When trigger conditions change: update description
DEPRECATE:
1. Mark with ⚠️ in README
2. Add "SUPERSEDED BY: [[new-skill-name]]" at top
3. Keep file for historical reference (don't delete)
4. Remove from active cross-referencesStaleness Detection
A skill is STALE if:
- Last evidence > 12 months old AND no "classic" marker
- Referenced competition has ended AND lesson doesn't generalize
- Referenced tool/library has major version change
- New evidence contradicts the skill's rule
Staleness action:
- Verify: is the rule still true? (test against current state)
- If YES → add "classic, still valid" marker + recent evidence
- If NO → update rule or deprecate
Evidence
- Repository grew from 0 → 36+ skills over 4 months
- Audit caught: 3 stale evidence sections, 2 broken cross-references, 1 scope overlap
- NVIDIA's skill-card-generator inspired the automated card concept
- Format evolved from ad-hoc markdown to structured frontmatter + 6 dimensions
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

