llm-self-loop
Restructure Web-UI / human-triggered tasks into CLI + file-output loops the LLM can iterate alone, with structured logs and addressable scratchpads. Apply trap-or-abandon: if a step cannot be looped, improve the harness rather than babysit. Trigger on iterative grunt-work, "push a button in a web UI to trigger this", monitoring dashboards, or any workflow whose inner loop requires a human in the middle.
What this skill does
The job: turn workflows that need a human in the inner loop into workflows the LLM closes itself. The two halves are *removing the trigger gate* and *opening observability*. ## Surface the gate first Before proposing changes, name the trigger gate explicitly: - *What action requires a human right now?* (button click, screenshot inspection, terminal interaction, web-form submission) - *What signal does the human provide that the LLM cannot get on its own?* (visual confirmation, copy-paste, secret value, eyeball verdict) - *Where does the result go?* (chat memory, screenshot, mental note) Most loops have one or two gates that, removed, collapse the cycle to seconds. Pick the smallest gate first. ## Structural fixes ### Web-UI trigger → CLI trigger If the workflow is gated by clicking in a web app, find or build the equivalent CLI command. Webhooks, REST endpoints, `gh` / `aws` / `gcloud` CLI subcommands, internal `just` targets — anything programmatically invokable. The LLM can then loop without leaving its session. ### Stdout-only output → file-based output If the workflow's result lives in chat memory or a screenshot, redirect to a file the LLM can read back: structured JSON dumps, markdown reports, append-only logs with addressable offsets. *Why:* file outputs survive compaction, support diff, and are inspectable by future sessions without replaying context. ### Dashboards → structured logs If verification requires eyeballing a Grafana / Datadog dashboard, surface the same metrics through a CLI query (PromQL, Datadog API, log aggregation tail). Anything that produces a `pass`/`fail`/`warn` verdict the LLM can read. ### Eyeball verdicts → contract assertions If the human's role is "looks right to me", encode the criterion as a test, schema, or assertion. The contract becomes the loop's done-criterion (pair with `strict-validation-setup` for the bootstrap of those gates). ## Trap-or-abandon decision After the structural fixes above, some steps still cannot be made autonomous — they involve genuine human judgment, external compliance, or capability the LLM lacks. For each remaining gate, apply this rule: - **Trap** — if the step can be wrapped in a verification-and-iteration loop where the LLM proposes, the human approves once, and the LLM iterates until the contract passes, keep it. The human is at the *outer* loop, not the inner. - **Abandon** — if a step requires the human in the *inner* loop and resists wrapping (e.g., new SOC2 review per iteration, real-time customer chat, hardware-mediated test), do not babysit. Either remove the step from the LLM's loop entirely (escalate to the human as a discrete handoff) or improve the harness so the step disappears (e.g., automate the SOC2 documentation pipeline). Naming the rule: babysitting an unloopable step is the failure mode this skill exists to prevent. Pre-existing chat consensus: "what can't be looped — abandon firmly and improve the harness." ## What this skill does not do - It does not author project rules — defer to `init` for AGENTS.md. - It does not bootstrap strict-mode validation gates — defer to `strict-validation-setup`. - It does not pick the test framework — defer to `test-driven` or the language's idiomatic tester. ## Posture Surgical, not architectural. Remove one gate at a time. After each fix, re-evaluate whether the loop now closes — sometimes one trigger removal is enough. Resist the temptation to redesign the whole system.
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.