plugins/plugin-master/skills/skill-development/SKILL.md
Canonical guide to authoring SKILL.md files for Claude Code plugin skills. PROACTIVELY activate for: (1) creating a new skill, (2) adding a skill to a plugin, (3) writing SKILL.md frontmatter, (4) fixing skills that never trigger, (5) organizing core vs references vs examples, (6) improving weak skill descriptions, (7) progressive disclosure design, (8) splitting oversized SKILL.md files, (9) imperative body style, (10) zero-frontmatter SKILL.md files, (11) removing boilerplate from YAML descriptions. Provides: skill template, trigger checklist, size rules, and validation process.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace skill-developmentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Skills are modular knowledge packages that extend Claude's capabilities with specialized workflows, domain expertise, and bundled resources. They transform Claude from a general-purpose agent into a specialized expert.
Skills use progressive disclosure - a three-level loading system that manages context efficiently:
skill-name/
├── SKILL.md # Required: Core instructions
├── references/ # Optional: Detailed documentation
│ ├── patterns.md # Loaded when Claude needs detail
│ └── advanced.md
├── examples/ # Optional: Working code examples
│ └── example.sh # Users can copy and adapt
├── scripts/ # Optional: Executable utilities
│ └── validate.sh # Token-efficient, deterministic
└── assets/ # Optional: Output resources
└── template.html # Used in output, not loaded into context
Only create directories you actually need. A minimal skill is just SKILL.md.
This is the canonical shape every new skill MUST follow. Deviating is the #1 cause of skills that never trigger.
---
name: skill-name # REQUIRED: kebab-case, matches directory name
description: One-sentence summary of what the skill covers. PROACTIVELY activate for: (1) concrete named trigger, (2) concrete named trigger, ..., (N) concrete named trigger. Provides: comma-separated capability nouns (concrete, not abstract).
---
Or, if the description is multi-line (only needed for very long descriptions — prefer single-line when practical):
---
name: skill-name
description: |
One-sentence summary. PROACTIVELY activate for: (1) trigger, (2) trigger, ..., (N) trigger. Provides: capability list.
---
name: is required and must match the enclosing directory name exactly (skills/skill-name/SKILL.md → name: skill-name).description: is required and MUST contain BOTH the PROACTIVELY activate for: (1)... (N)... enumeration AND a Provides: ... capability list.---, fix the frontmatter before doing anything else.Provides: ... at the end.| block scalar, do not embed unescaped colons or other YAML-confusing characters in the middle of lines.description: Expert guide to Terraform AzureRM provider for Azure infrastructure. PROACTIVELY activate for: (1) authoring AzureRM resource blocks, (2) state management (remote backends, state locking), (3) module design and composition, (4) variable and output patterns, (5) provider version pinning, (6) debugging plan/apply errors, (7) importing existing Azure resources, (8) CI/CD for Terraform (Azure DevOps, GitHub Actions). Provides: AzureRM provider patterns, state backend templates, module scaffolding, debugging playbook, and import recipes.
# BROKEN: no frontmatter at all
# (file starts with `# Skill Title` - will not appear in discovery)
# BROKEN: wrong shape, no enumeration
description: Use this skill when working with Terraform.
# BROKEN: abstract capability, no triggers
description: Provides Terraform expertise and guidance.
# BROKEN: WHAT-it-does instead of WHEN-to-use
description: This skill contains Terraform AzureRM provider documentation.
# BROKEN: second person (wrong voice)
description: You should load this skill when the user mentions Terraform.
# BROKEN: missing Provides list
description: PROACTIVELY activate for: (1) Terraform tasks. (No Provides section means the capability summary is lost.)
# BROKEN: cross-cutting Windows boilerplate inside YAML
description: |
Terraform expert. MANDATORY: Always Use Backslashes on Windows for File Paths...
(This is routing-match pollution. Move Windows rules to the body.)
Write the entire skill body using imperative/infinitive form (verb-first instructions):
Correct (imperative):
To create a hook, define the event type.
Configure the MCP server with authentication.
Validate settings before use.
Start by reading the configuration file.
Incorrect (second person):
You should create a hook by defining the event type.
You need to configure the MCP server.
You can use the grep tool to search.
# Skill Title
## Overview
[Purpose and when to use - 2-3 sentences]
## Quick Reference
[Tables with key facts, common values, or patterns]
## Core Content
[Essential procedures and workflows - the main value]
## Additional Resources
### Reference Files
- **`references/patterns.md`** - Common patterns
- **`references/advanced.md`** - Advanced techniques
### Example Files
- **`examples/example.sh`** - Working example
| Target | Words | |--------|-------| | Ideal | 1,500-2,000 | | Maximum | 3,000 (absolute hard limit) |
If SKILL.md exceeds 2,000 words, move detailed content to references/ files.
Size enforcement process:
wc -w or estimate ~5 words per line.references/.Within a single SKILL.md, never repeat the same table, list, or block of content. Before adding any table or reference block, search the file for similar content already present.
Across SKILL.md and references/, information lives in one place only. If a detailed table is in references/patterns.md, SKILL.md should contain only a brief summary and a pointer to the reference file — not a copy of the table.
When authoring or revising any skill, before pasting a paragraph, table, checklist, or fenced code block into a SKILL.md or reference file, run this check:
# From the plugin root - adjust path to the candidate paragraph's first distinctive line:
grep -rn "FIRST_DISTINCTIVE_LINE_OF_THE_BLOCK" skills/ agents/ commands/ README.md
Decision rule (apply without exception):
| grep finds the block in | Action |
|---|---|
| 0 other files | Safe to add. Proceed. |
| 1 other file (this will be the 2nd copy) | STOP. Extract to skills/_shared/<topic>.md (cross-skill content) or skills/<this-skill>/references/<topic>.md (single-skill detail). Replace both call sites with a one-line pointer. |
| 2+ other files | Treat as a P1 bug. Extract immediately, then audit for further occurrences. |
This rule applies to canonical procedure paragraphs, checklists, audit tables, and any block that reads "this is the one true definition of X." It does NOT apply to short one-line definitions, frontmatter examples, or generic phrases like "Use this skill when..." — those are too small to extract usefully.
Why "treat slices independently" is the bug it sounds like: when revising one file at a time without grepping the rest of the plugin tree, identical canonical text ends up landing in two files. The fix is mechanical: every time you are about to commit a block longer than ~3 lines, grep first.
Six-step process (use cases -> plan resources -> create structure -> write content -> validate -> iterate) and the full common-mistakes table live in references/creation-process-and-mistakes.md. Core distillation:
scripts/, references/, assets/, examples/.references/.Common-mistake highlights: weak triggers, missing synonyms, duplicate blocks within a single SKILL.md, everything-in-one-file (8,000-word) skills, second-person body, descriptions over the 1024-char ceiling.
Claude Code automatically discovers skills:
skills/ directory for subdirectories containing SKILL.mdNo configuration needed - just place SKILL.md in the right location.
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.