plugins/plugin-master/skills/triggering-reliability/SKILL.md
Catalog of common mistakes that break Claude Code agent and skill triggering, plus marketplace validation rules. PROACTIVELY activate for: (1) plugins installed but never trigger, (2) agents failing to route, (3) skills not appearing in discovery, (4) pre-release triggering audit, (5) migrating deprecated agent true flag, (6) zero-frontmatter SKILL.md files, (7) boilerplate in YAML descriptions, (8) abstract descriptions, (9) missing example blocks on fat agents above 2,500 words, (10) missing PROACTIVELY or Provides sections, (11) running validate_plugins.py, (12) auditing a plugin without contradicting earlier intentional refactors. Provides: anti-patterns, fixes, before/after examples, audit checklist, and validator rule index.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace triggering-reliabilityInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
This skill is the reference catalog of everything that makes Claude Code agents and skills fail to trigger. Every mistake below has been observed in real plugins. Treat this as a checklist before shipping any plugin, and as the first place to look when an existing plugin installed fine but nothing happens.
| Symptom | Most-likely cause |
|---|---|
| Skill directory exists but never loads | Missing YAML frontmatter (file starts with # not ---) |
| Agent file exists but cannot be invoked by name | Deprecated agent: true flag with no name: field |
| Agent rarely triggers despite obvious queries | Missing <example> blocks or abstract-capability description |
| Skill triggers inconsistently | Description describes WHAT it does, not WHEN to use it |
| Multiple skills fight over the same query | Trigger-phrase overlap between descriptions |
| Agent description matches generic unrelated queries | Windows/docs boilerplate inside YAML description: poisons routing |
| Agent uses wrong model | model: field missing or hard-coded instead of inherit |
Full symptom / root cause / fix narrative for each lives in references/anti-patterns-catalog.md. Quick table:
| # | Anti-pattern | Severity | One-line fix |
|---|---|---|---|
| 1 | Zero-frontmatter skill — no --- | P0 | Add canonical YAML frontmatter (name:, description:) |
| 2 | Deprecated agent: true flag | P0 | Replace with name: <kebab> |
| 3 | Abstract "Use this agent for X" description | P1 | Rewrite with PROACTIVELY activate for: (1)... (N)... and Provides: |
| 4 | Description describes WHAT, not WHEN | P1 | Flip to user-intent triggers; lead with PROACTIVELY activate for: |
| 5 | Fat agent (>2,500 words) missing <example> blocks | P1 | Add 3-5 example blocks; lean orchestrators exempt |
| 6 | Windows/docs boilerplate inside YAML description: | P0 | Move boilerplate to a named body section |
| 7 | Missing or hard-coded model: field | P2 | Set model: inherit |
| 8 | Trigger-phrase overlap across skills | P1 | Assign exclusive ownership; add disambiguation in skill activation table |
| 9 | Description over 1024 chars or 15+ diluted triggers | P1 | Trim to 400-1000 chars; front-load triggers; split skill if needed |
The "lean orchestrator with no examples" shape is not anti-pattern 5 — see the tier table in agent-development and the audit caveat in agent-development/references/validation-and-audits.md before re-adding examples during an audit.
Front-load triggers — the front of the description survives any truncation.
Run the audit sweeps from the repo root, in priority order (P0 → P2). Every row of output is a triggering bug. Fix earlier items first — they have larger blast radius.
The full bash and PowerShell sweep scripts (7 audit probes plus the positive-signal validation greps) live in references/audit-greps.md. The quick one-liners are also reproduced under One-line greps for the canonical checks at the bottom of this file.
agent: true agents (invisible/broken).<example> blocks on fat agents (body > 2,500 words) that back multiple skills. Lean orchestrators under the 2,500-word threshold are exempt by design.PROACTIVELY activate for: / Provides: enumeration.model: inherit, color:, tools: tightening).Fix in priority order - do not spend time on P2 while P0 bugs exist.
When reporting findings, use P0 / P1 / P2 tiers (plugin-breaking / routing-unreliable / polish). Description length is governed by three caps: 1024-char API spec hard ceiling, 1536-char listing cap that Claude sees during routing, and a ~1% context-window aggregate budget across all installed skills.
Full tier table with concrete examples for each severity, the description-length cap table with values and meanings, and the authoring targets: see references/severity-and-limits.md.
Two gates run before any content addition to a SKILL.md or reference file:
wc -w (or PowerShell Measure-Object -Word) the target file. If it is in the 2,800-3,000 word band, extract a reference-style section to references/ BEFORE adding. Over 3,000 words = already broken, extract down to under 2,000 before doing anything else.grep -rn (or Select-String) the first distinctive line of the candidate block across skills/, agents/, commands/, README.md. Even one hit elsewhere means the block must be extracted to skills/_shared/ (cross-skill) or references/ (single-skill) instead of pasted.Full decision tables, exact commands for bash and PowerShell, and the combined workflow: see references/size-and-dry-gates.md.
The full 26-item checklist (plugin.json shape, agent and skill frontmatter, description content, DRY-gate grep, code-sample sanity, size ceilings, NOTICES.md, marketplace.json registration) lives in references/pre-publish-checklist.md. Run every item before shipping.
Two defects routinely slip into fenced code blocks and silently break readers:
``` instead of ```bash / ```powershell / ```yaml) disable syntax highlighting and, worse, hide the platform assumption. A bash-only snippet that renders as plain text looks identical to a PowerShell snippet — a Windows reader will copy it and watch it fail with no signal as to why.Code-sample sanity checklist — run before every ship. Each unchecked item is a finding to fix manually.
..., ", ', -.```bash, ```powershell, ```yaml, ```json, ```python, ```markdown). Bare ``` openings are a defect.On bash/macOS/Linux: or On PowerShell (Windows): immediately before the fence, and the fence language tag matches.This repo's primary shell is PowerShell on Windows, so a bash-only snippet without a platform tag is a defect.
Sweeps and full character tables: see references/code-sample-sanity.md for the bash and PowerShell sweep scripts (smart-punctuation probe and fence-language-tag probe), the canonical smart-quote → ASCII fix table, and the list of recognised language tags.
The single source of truth for what counts as a "real" triggering defect in this marketplace is scripts/validate_plugins.py. Manual greps below help locate findings; the validator is what decides whether they ship.
python scripts/validate_plugins.py # whole marketplace
python scripts/validate_plugins.py --plugin my-plugin # one plugin
python scripts/validate_plugins.py --strict # warnings fail the build
Key validator rules with bearing on this skill:
| Validator check | Severity | Maps to |
|---|---|---|
| Skill missing frontmatter | error | Anti-pattern 1 |
| Deprecated agent: true | error | Anti-pattern 2 |
| Skill missing PROACTIVELY / Provides | warning | Anti-patterns 3-4 |
| Agent missing PROACTIVELY / Provides | warning | Anti-patterns 3-4 |
| Agent missing examples | warning | Anti-pattern 5 — fires only when agent body > 2,500 words |
| Agent missing model: inherit | error | Anti-pattern 7 |
| Description / SKILL.md length checks | error/warning | Anti-pattern 9 + size ceiling |
The complete validator rule index (agent, skill, and plugin level) plus known validator gaps lives in agent-development/references/validation-and-audits.md. Keep that table and the validator code in lockstep.
When auditing an existing plugin, an apparent defect is not automatically a real defect — it may be the deliberate output of a prior refactor. The canonical example: example blocks stripped from lean orchestrators during a fat-to-lean refactor. A follow-up audit that recommends re-adding them would undo the refactor.
Before listing any agent or skill finding in an audit report, run the three-question intent check:
scripts/validate_plugins.py flag it? If not, do not invent stricter rules in the audit than the validator enforces.Only when all three return "real defect" does it belong in the remediation list. Full rationale, the broader list of "stripped on purpose" patterns to watch for, and the authoring-vs-auditing contrast: agent-development/references/validation-and-audits.md.
Run these from a plugin directory. Any output is a finding.
On bash/macOS/Linux:
grep -L "^---" skills/*/SKILL.md # zero-frontmatter skills (P0)
grep -l "^agent: true" agents/*.md # deprecated agents (P0)
grep -L "<example>" agents/*.md # agents with no <example> blocks - cross-check body word count before flagging (lean orchestrators under 2,500 words are exempt; see agent-development tier table)
grep -L "PROACTIVELY activate for:" skills/*/SKILL.md # skills missing trigger enumeration (P1)
grep -L "Provides:" skills/*/SKILL.md # skills missing capability list (P1)
grep -L "^model: inherit" agents/*.md # agents not inheriting model (P2)
grep -l "MANDATORY: Always Use Backslashes" agents/*.md skills/*/SKILL.md # Windows boilerplate in YAML (P0)
On PowerShell (Windows), references/audit-greps.md carries the equivalent sweeps (probes 1-7) plus the positive-signal validation queries.
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.