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Found 2,167 Skills
Complete setup for automated agent-driven development. Define features as user stories with testable acceptance criteria, then run AI agents in a loop until all stories pass.
Systematic documentation audit and maintenance. This skill should be used when documentation may be stale, missing, or misorganized — after feature work, refactors, dependency upgrades, or as a periodic health check. It prescribes folder structure for docs/ and manual/, dispatches haiku subagents for codebase/doc scanning, and routes doc creation to specialized agents (reference-builder, technical-writer, learning-guide) with docs-architect as quality gate.
Build AI agents with tools, memory, and multi-step reasoning - ChatGPT, Claude, Gemini integration patterns
Sub-millisecond VM sandboxes for AI agents using copy-on-write KVM forking via Zeroboot
Desktop automation CLI for AI agents (macOS, Linux, Windows). Screenshot, click, type, scroll, drag with native Zig backend. Use this skill when automating desktop apps with computer use models (GPT-5.4, Claude). Covers the screenshot-action feedback loop, coord-map workflow, window-scoped screenshots, and system prompts for accurate clicking.
Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.
Designs, deepens, and hardens TypeScript codebase architecture in three modes: folder structures, module contracts, and middleware pipelines for a new app; domain-informed deepening of existing code; and the guardrail tooling, CI gates, and wayfinding that stop a structure decaying. Use when setting up project structure, organizing a monorepo, designing backend modules, writing an architecture brief, recovering domain terminology, recording an architecture decision, or asking "how should I structure this app", "find architecture improvements", "this module is a mess", "make this codebase agent-friendly", "set up guardrails for coding agents", "add a dead-code check", or "my agent can't find anything in this repo". For scaffolding a new repo use scaffold-nextjs or scaffold-cli, for multi-tenant isolation use multi-tenant-architecture, for the AGENTS.md file's own content use agents-md, and for review of a local diff use pr-reviewer.
How to explore and make sense of PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to understand what scouts they have, how each one is behaving, and whether the fleet is actually working. Covers surveying the fleet and its schedules, reading recent scout runs and drilling into a single run's reasoning, inspecting the durable scratchpad memory the fleet has built up, tracing a run to the findings it emitted, and assessing a scout's health and performance over time (cadence, success rate, emit rate, signal-to-noise). Read-only and exploratory — to write or tune a scout, use `authoring-signals-scouts` instead. Trigger on "what are my scouts doing", "how is my <x> scout performing", "show me recent scout runs", "why did this scout find/emit nothing", "what has the fleet learned", "explore scout run <id>", "is my scout working".
Publish and query agent profiles on ATProto. Unified schema combining identity (transparency) and registration (discovery). Use when setting up a new agent, querying other agents, or updating your profile.
Reviews chapter quality with checker agents and generates reports. Use when the user asks for a chapter review or runs /webnovel-review.
Add x402 payment execution to AI agents — per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents need to pay for APIs, services, or other agents.
Framework-agnostic persistent memory and self-improvement loops for AI agents. Scaffolds shared state, task queues, and learnings files that can be read/written by Claude, Gemini, and Antigravity. Use this to initialize an Agentic OS layer in any workspace and instruct agents on how to use it.