plugins/doc-master/skills/repo-health/SKILL.md
This skill should be used when bootstrapping, auditing, or reviewing the community-health and repository-cornerstone files in a code repository: README, LICENSE, CONTRIBUTING, CODE_OF_CONDUCT, SECURITY, SUPPORT, issue / PR templates, CODEOWNERS, FUNDING, CITATION, and REUSE / SPDX metadata. PROACTIVELY activate on "set up new repo docs", "community health files", "what should my README contain", "do I need a code of conduct", "audit repo bootstrap docs", "repository documentation cornerstones", "pick a license", "CONTRIBUTING.md", "SECURITY.md", "vulnerability reporting", "SUPPORT.md", "CODEOWNERS", "issue templates", "PR templates", "CITATION.cff", "REUSE", "SPDX headers", "Standard Readme", "Contributor Covenant". Provides: four-question diagnostic per cornerstone, Standard Readme structure, license routing (no picking), CONTRIBUTING / CODE_OF_CONDUCT / SECURITY / SUPPORT canons, template files, and REUSE 3.3 / SPDX routing.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace repo-healthInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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The repository-cornerstone skill. Owns the canon for the community-health files that bootstrap any code repository:
README.md — the front door (Standard Readme spec).LICENSE — the legal contract (routed to choosealicense.com; doc-master does not pick).CONTRIBUTING.md — how external contributors propose changes.CODE_OF_CONDUCT.md — behavioral norms (Contributor Covenant 3.0 default; 2.1 fallback).SECURITY.md — vulnerability reporting policy.SUPPORT.md — where users go for help (often optional for small repos)..github/ templates — issue / PR / CODEOWNERS / FUNDING / CITATION.Each cornerstone has a reference file with the structure, common failure modes, and routing notes. This SKILL.md is the lean entry point — load the references on demand.
doc-master applies the four-question diagnostic to every cornerstone before recommending creation:
.github/, docs/? Each cornerstone has a conventional home.If any answer is "no" or "we don't know," do not create the file yet. Most small repos do not need SUPPORT.md, do not need FUNDING.yml, do not need a per-file REUSE header. Cargo-culting cornerstones produces docs nobody reads and nobody updates — the same anti-padding rule that governs ADRs.
For any code repository visible outside the originating team:
| Cornerstone | Default |
|---------------------------|-------------------------------------------------------------------------------------------------------------------------------|
| README.md | Yes — required. Use the Standard Readme structure. See references/readme-canon.md. |
| LICENSE | Yes — required for any code that will be reused. doc-master routes to choosealicense.com; does not pick. See references/license-routing.md. |
| CONTRIBUTING.md | Yes if external contribution is accepted. Skip if the repo is read-only. See references/contributing-canon.md. |
| CODE_OF_CONDUCT.md | Yes if any external participation. Default to Contributor Covenant 3.0; 2.1 is the still-valid older fallback. See references/code-of-conduct-canon.md. |
| SECURITY.md | Yes if the code processes untrusted input, handles secrets, ships to production, or has any user-visible attack surface. See references/security-canon.md. |
| SUPPORT.md | Probably not for most small repos. Add only when the issue tracker is being misused for support questions. See references/support-canon.md. |
| Issue / PR / CODEOWNERS / CITATION / FUNDING | Case by case. Each has a real trigger. See references/templates-canon.md. |
| REUSE 3.3 / SPDX headers | Yes for any repo that aggregates code under multiple licenses, ships to environments that require SBOM, or wants machine-verifiable license metadata. See references/reuse-spdx-canon.md. |
When the user asks to set up repository docs:
README*, LICENSE*, CONTRIBUTING*, CODE_OF_CONDUCT*, SECURITY*, SUPPORT*, .github/).When the user asks to audit repository docs:
SUPPORT.md and FUNDING.yml cases should stay missing.[email protected], community@…, a moderation team — never a personal inbox. Bus factor of one is a refusal condition.security@…). Refuse a single-maintainer email.adr-critique / markdown-style: line-referenced, verbatim quote, named rule, proposed rewrite, per-finding approval.markdown-style (/doc-lint).doc-diagnostic / adr-drafting. "We adopted REUSE" may be an ADR; the per-file REUSE headers themselves are not.../doc-diagnostic/references/changelog-canon.md.../doc-diagnostic/references/{runbook,postmortem,open-questions}-canon.md.../doc-diagnostic/references/agentic-docs-canon.md.references/readme-canon.md — Standard Readme spec; nine canonical sections.references/license-routing.md — common license defaults and the routing rule (doc-master does not pick).references/contributing-canon.md — CONTRIBUTING.md structure, Conventional Commits 1.0, DCO.references/code-of-conduct-canon.md — Contributor Covenant 3.0 default with 2.1 fallback; Enforcement Ladder; shared-alias rule.references/security-canon.md — SECURITY.md sections; private-reporting channels; response-window floor.references/support-canon.md — SUPPORT.md channels; when most repos can skip it.references/templates-canon.md — .github/ISSUE_TEMPLATE/*.yml, PULL_REQUEST_TEMPLATE.md, CODEOWNERS, FUNDING.yml, CITATION.cff.references/reuse-spdx-canon.md — REUSE 3.3 per-file headers, LICENSES/ folder, REUSE.toml, .license sidecars; SPDX identifier catalog.development
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.