plugins/plugin-master/skills/advanced-features-2025/SKILL.md
Advanced Claude Code plugin features for hooks, MCP integration, team distribution, and progressive disclosure. PROACTIVELY activate for: (1) progressive disclosure design, (2) hook automation, (3) PreToolUse and PostToolUse hooks, (4) MCP server integration, (5) .mcp.json configuration, (6) team plugin distribution, (7) repository-level plugin layouts, (8) context efficiency, (9) private marketplaces, (10) ${CLAUDE_PLUGIN_ROOT}. Provides: hooks reference, MCP patterns, team distribution, and context-efficiency guidance.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace advanced-features-2025Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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| Feature | Location | Purpose |
|---------|----------|---------|
| Agent Skills | skills/*/SKILL.md | Dynamic knowledge loading |
| Hooks | hooks/hooks.json | Event automation |
| MCP Servers | .mcp.json | External integrations |
| Team Config | .claude/settings.json | Repository plugins |
| Hook Event | When Fired | Use Case | |------------|------------|----------| | PreToolUse | Before tool | Validation | | PostToolUse | After tool | Testing, linting | | SessionStart | Session begins | Logging, setup | | SessionEnd | Session ends | Cleanup | | UserPromptSubmit | Prompt submitted | Preprocessing | | PreCompact | Before compact | State save | | Notification | Notification shown | Custom alerts | | Stop | User stops | Cleanup | | SubagentStop | Subagent ends | Logging |
| Variable | Purpose |
|----------|---------|
| ${CLAUDE_PLUGIN_ROOT} | Plugin installation path |
| ${TOOL_INPUT_*} | Tool input parameters |
Skills are dynamically loaded based on task context, enabling:
skills/
└── skill-name/
├── SKILL.md # Core content
├── references/ # Detailed docs
│ └── deep-dive.md
├── examples/ # Working code
│ └── example.md
└── scripts/ # Utilities
└── tool.sh
---
name: skill-name
description: |
When to activate this skill. Include:
(1) Use case 1
(2) Use case 2
Provides: what it offers
---
# Skill Title
## Quick Reference
[Tables, key points]
## Core Content
[Essential information - keep lean]
## Additional Resources
See `references/` for detailed guidance.
Inline in plugin.json:
{
"hooks": {
"PostToolUse": [{
"matcher": "Write|Edit",
"hooks": [{
"type": "command",
"command": "${CLAUDE_PLUGIN_ROOT}/scripts/lint.sh"
}]
}]
}
}
Separate hooks.json:
{
"PostToolUse": [{
"matcher": "Write",
"hooks": [{
"type": "command",
"command": "${CLAUDE_PLUGIN_ROOT}/scripts/format.sh",
"timeout": 5000
}]
}]
}
Write - File writesEdit - File editsBash - Shell commandsWrite|Edit - Multiple tools.* - Any tool (use sparingly)Auto-test after changes:
{
"PostToolUse": [{
"matcher": "Write|Edit",
"hooks": [{
"type": "command",
"command": "${CLAUDE_PLUGIN_ROOT}/scripts/run-tests.sh"
}]
}]
}
Validate before Bash:
{
"PreToolUse": [{
"matcher": "Bash",
"hooks": [{
"type": "command",
"command": "${CLAUDE_PLUGIN_ROOT}/scripts/validate-cmd.sh"
}]
}]
}
{
"mcpServers": {
"server-name": {
"command": "node",
"args": ["${CLAUDE_PLUGIN_ROOT}/mcp/server.js"],
"env": {
"API_KEY": "${API_KEY}"
}
}
}
}
{
"mcpServers": {
"stripe": {
"command": "npx",
"args": ["-y", "@stripe/mcp-server"],
"env": {
"STRIPE_API_KEY": "${STRIPE_API_KEY}"
}
}
}
}
Create .claude/settings.json at repo root:
{
"extraKnownMarketplaces": [
"company/internal-plugins"
],
"plugins": {
"enabled": [
"deployment-helper@company",
"code-standards@company"
]
}
}
.claude/settings.jsonreferences/examples/ for working code.*)${CLAUDE_PLUGIN_ROOT} for pathsFor detailed patterns, see:
references/hooks-advanced.md - Complete hook patternsreferences/mcp-patterns.md - MCP integration examplesreferences/team-distribution.md - Repository configurationexamples/hook-scripts.md - Working hook scriptsdevelopment
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.