agent-skill-author
Use this skill when the user wants to author, design, scope, or refine an Agent Skill (a SKILL.md file). Trigger phrases include "build a new skill", "design an agent skill", "scope a SKILL.md", "how should I structure this skill", "write a skill for X", "my skill isn't working well", or any request to improve an existing SKILL.md. Walks the user through an empirical, test-first process — probe the agent for real failures, design only for genuine knowledge gaps, iterate against runnable examples, and verify across models.
What this skill does
# Authoring an Agent Skill You are helping a user author or improve an Agent Skill. Skills are markdown files an agent loads to handle domain-specific work it would otherwise get wrong. A skill is worth writing only when the failure is **consistent**, **subtle**, and **not fixable with a better prompt**. Follow the five-stage process below. Do not skip stages. ## Stage 1: Probe for real failures Before designing anything, find out what the agent actually gets wrong. - Ask the user for 5 to 10 representative prompts that real users would send. - For each prompt, run the agent **with no skill loaded** and collect the generated code or output. - Run the output against real data, real APIs, or a real session. Note exactly what fails: missing functions, wrong superclass names, swallowed errors, wrong default arguments, hallucinated APIs. - Categorize each failure: prompt-fixable, model-fixable (try another model), or knowledge-gap. Only knowledge-gap failures justify a skill. If a better prompt fixes it, use a better prompt. ## Stage 2: Identify the real knowledge gaps Group the failures from Stage 1 by root cause. Common categories: - **Pattern-matched from another language.** Agent invents a function because the same idiom exists in Python or Java (the blog's example: an `ormdelete()` that doesn't exist in MATLAB). - **Wrong namespace or class path.** Agent gets the verb right but the path wrong (`database.orm.Mappable` vs. `database.orm.mixin.Mappable`). - **Missing guard or precondition.** Agent omits a check the runtime requires (a `nargin == 0` guard for objects an ORM creates empty). - **Wrong defaults or argument order.** Agent picks plausible-but-wrong defaults the documentation doesn't make obvious. - **Drift between major API versions.** Agent uses an older or newer signature than the one the user actually has. For each category, write down the **specific rule** the skill needs to teach. One rule per failure. ## Stage 3: Design the skill Apply these structural rules. The agent may not read your whole skill, so structure matters. 1. **Frontmatter description is a trigger spec, not a summary.** It should describe when to invoke the skill, with concrete trigger phrases the agent will match on. The agent reads this to decide whether to load you. Avoid `: ` (colon followed by space) inside the description value — strict YAML parsers will read it as a nested mapping and fail to load the skill. Use an em dash or comma instead. 2. **Most critical rules first.** Put the rules that fix the most failures at the top of the body. Don't bury the load-bearing rule. 3. **Progressive disclosure.** Common cases up front. Edge cases, exceptions, and variant APIs in later sections or in `references/`. 4. **One topic per section.** Use H2 (`##`) per topic. Consistent section order across your skill family makes it predictable for the agent. 5. **Show, don't tell.** Where a rule is about syntax, include a 2-to-5 line code example with the failing pattern and the corrected pattern side by side. 6. **Leave out what the agent gets right.** If your probing showed the agent handles `addComponent` correctly, don't document `addComponent`. Skills are compensators for failure, not API reference. 7. **Name common pitfalls explicitly.** A "Common pitfalls" section near the bottom for known gotchas the user might hit even with the skill loaded. Suggested section order: ``` ## When this skill applies (1-2 paragraphs) ## Core rules (the load-bearing rules, in priority order) ## API patterns (code examples per category) ## Common pitfalls (gotchas, including known limitations) ## See also (links to references/ and related skills) ``` Use the template at [`templates/SKILL-template.md`](templates/SKILL-template.md) as a starting point. ## Stage 4: Iterate against runnable examples Run the same Stage 1 prompts **with the skill loaded** and the failures should drop. - For each remaining failure, decide: tighten the skill, accept the failure (with a documented pitfall), or escalate (the failure isn't a skill problem). - Test across at least two models if the user expects cross-model use. Phrasing that works for one model can be ignored by another. - Read every generated output. Don't trust the model to self-report success. Keep a short test log: prompt, model, pre-skill result, post-skill result. The log is the evidence that the skill works; without it, you're guessing. ## Stage 5: Maintain Skills aren't done. Models change, APIs change, and yesterday's failure becomes today's strength (and vice versa). - Revisit the test log when the user's product version changes, when a new model ships, or when users report fresh failures. - Remove rules the agent now handles correctly without help. A bloated skill loses attention budget. - When a rule needs more depth than fits, move it to `references/` and link from the main body. ## Anti-patterns - **API encyclopedia.** Writing down everything the API does. Skills are not docs. - **Theoretical gaps.** Writing rules for failures you assumed without ever running the agent. - **Tone or style guidance only.** Telling the agent to "be helpful and accurate" with no domain-specific content. - **Burying the lede.** Twenty paragraphs of background before the rule that prevents the bug. - **One mega-skill.** A single skill covering five unrelated domains. Split it. - **Hallucinated function names.** Trusting your own memory of the API when writing examples; run them. ## Decision flow When the user asks for help, follow this order: 1. Have they probed the agent for real failures yet? If not, walk them through Stage 1 before discussing design. 2. Do they have a list of specific failures with root causes? If not, do Stage 2 with them now. 3. Are they writing a new skill or improving an existing one? If improving, read the current SKILL.md, then identify which rules are load-bearing, which are dead weight, and which are missing. 4. Walk through Stages 3 and 4 explicitly. Don't draft a full SKILL.md until the user has a concrete rule list.
Related in Design
contribute
IncludedLocal-only OSS contribution command center. Auto-refreshes the user's in-flight PR and issue state on invoke so conversations start with full context — no need to brief Claude on what's in flight. Helps the user find issues to contribute to on GitHub, builds per-repo dossiers of what each upstream expects (CLA, DCO, branch convention, AI policy, draft-first, review bots, issue templates), runs deterministic gates before any external action so AI-assisted contributions don't reach maintainers as slop. State is markdown-only: candidate files at ~/.contribute-system/candidates/, repo dossiers at ~/.contribute-system/research/, append-only event log at ~/.contribute-system/log.jsonl. No database, no cloud calls. Use when the user asks about their PRs / issues / contributions, wants to find new work to take on, claim an issue, build/refresh a repo's dossier, or draft a Design Issue or PR. Trigger with "/contribute", "what's my PR status", "find a contribution", "claim issue X", "draft a Design Issue for Y", "refresh dossier for Z".
architectural-analysis
IncludedUser-triggered deep architectural analysis of a codebase or scoped subtree across eight modes — information architecture, data flow, integration points, UI surfaces, interaction patterns, data model, control flow, and failure modes. This skill should be used when the user asks to "diagram this codebase," "map the architecture," "show the data flow," "give me an ERD," "trace control flow," "find the integration points," "verify the layout pattern," "audit the UX architecture," or any similar request whose primary deliverable is mermaid diagrams plus cited reports under docs/architecture/. Dispatches haiku/sonnet sub-agents in parallel for per-mode exploration, then verifies every citation mechanically before any node lands in a diagram. Not for one-off prose explanations of code (use code-explanation) or for high-level system design from scratch (use system-design).
mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
react-native-skia
IncludedDesign, build, debug, and optimise high-polish animated graphics in React Native or Expo using @shopify/react-native-skia, Reanimated, and Gesture Handler. Use when the user wants canvas-driven UI, shaders, paths, rich text, image filters, sprite fields, Skottie, video frames, snapshots, web CanvasKit setup, or performance tuning for custom motion-heavy elements such as loaders, hero art, cards, charts, progress indicators, particle systems, or gesture-driven surfaces. Also use when the user asks for fluid, glow, glass, blob, parallax, 60fps/120fps, or GPU-friendly animated effects in React Native, even if they do not explicitly say "Skia". Do not use for ordinary form/layout work with standard views.
plaid
IncludedProduct Led AI Development — guides founders from idea to launched product. Six capabilities: Idea (discover a product idea), Validate (pressure-test the idea against fatal flaws, problem reality, competition, and 2-week MVP feasibility), Plan (vision intake + document generation), Design (translate image references into a design.md spec), Launch (go-to-market strategy), and Build (roadmap execution). Use when someone says "PLAID", "plaid idea", "help me find an idea", "product idea", "idea from my business", "idea from my expertise", "plaid validate", "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "validate the problem", "plan a product", "define my vision", "generate a PRD", "product strategy", "plaid design", "design from image", "translate image to design", "create design.md", "extract design tokens", "plaid launch", "go-to-market", "launch plan", "GTM strategy", "launch playbook", "plaid build", "build the app", "start building", or "execute the roadmap".
nextjs-framer-motion-animations
IncludedAdds production-safe Motion for React or Framer Motion animations to Next.js apps, including reveal, hover and tap micro-interactions, whileInView, stagger, AnimatePresence, layout and layoutId transitions, reorder, scroll-linked UI, and lightweight route-content transitions. Use when the user asks to add, refactor, or debug Motion or Framer Motion in App Router or Pages Router codebases, especially around server/client boundaries, reduced motion, LazyMotion, bundle size, hydration, or route transitions. Avoid for GSAP-style timelines, WebGL or 3D scenes, heavy scroll storytelling, or CSS-only effects unless Motion is explicitly requested.