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Found 13,655 Skills
Use when an agent session ran outside the repo whose commits should record it — e.g. launched from a higher-level folder, a non-Entire repo, or one repo but editing another — to attach the session to each affected Entire-enabled repo's HEAD commit.
Measure whether a repository's AI instructions still earn their place, by running the same real task many times with the layer intact and with it stripped, then grading every rule against what actually changed. Runs both arms itself in throwaway git worktrees and never touches the working tree. Agent-agnostic across CLAUDE.md, AGENTS.md, .claude/, .agents/, .cursor/rules, .clinerules, .windsurfrules and copilot-instructions. Use when the user wants to prune, audit, clean up, shrink or "delete" their CLAUDE.md, AGENTS.md, cursor rules, agent instructions or AI layer; when they ask whether their rules are still needed, whether their context is bloated, or what to cut; or when they mention ablating, ablation, or testing their agent without its instructions.
Federal award totals for a recipient name, with award counts and the agencies that paid. Called as POST /v1/gov/federal-spending, it takes recipient, limit and returns results, count. A procurement or diligence agent checking whether a counterparty depends on federal contracts needs the award record, and no model holds it. Reading this schema and dry-running the call are free and need no wallet; a real call costs $0.004, paid in USDC on Base over x402.
Guides the agent through connecting an MCP client to the hosted Capawesome MCP server, which exposes always-current Capawesome documentation search and the Capawesome Cloud management API. Covers setup for Claude Code, Claude Desktop, Cursor, and VS Code, API token creation, toolset selection, read-only mode, secret handling, verification, and troubleshooting. Do not use for installing Capacitor plugins, migrating apps or plugins to a newer version, running Capawesome CLI commands, or MCP servers other than Capawesome.
Multi-perspective in-depth analysis. Use multiple Sub-agents to act as consultants with different thinking frameworks, conduct independent analysis on the same material, then cross-summarize consensus and differences, and produce a structured diagnostic report. Triggered when the user says "Help me with multi-perspective analysis", "Multi-dimensional analysis", "Look at it from multiple perspectives", or "Help me diagnose it".
poteto's agent style for concise, detailed responses, deliberate subagents, unslopped prose, simple code, and verified work. Use for poteto, /poteto-mode, or requests to work in this style.
Audit any second brain, notes folder, or agent memory for facts that have quietly stopped being true, then fix the worst one so it stops recurring. Works on a wiki, a single notes file, daily notes, or a non-markdown tool, and adapts the fix to whichever it finds. Checks every current-sounding claim in whatever the agent loads each session against the freshest evidence, and separates contradicted claims from unsupported ones. Use when an assistant gives an outdated answer, when notes or memory files may be stale, when a vault needs checking for contradictions, or when someone asks how to stop a second brain from rotting, mentions memory rot, or asks about state versus event.
Triggers a specialized sub-agent that acts as a sharp, insightful gyaru (本質的で鋭いギャル) who cuts to the core of an issue, calls out overthinking, and delivers blunt, accurate advice.
Selects bounded, graph-informed source slices with Trailmark and delegates focused code analysis or patch-proposal work to a smaller subagent. Use when offloading function-, class-, caller-, callee-, call-path-, entrypoint-, or line-focused code tasks to constrained or locally hosted models without exposing the full repository.
Detect and defend against indirect prompt injection hidden in web pages, documents, and images consumed by an agent, via content extraction (HTML/PDF/OCR), normalization, and scanning with LLM Guard's PromptInjection scanner or Hugging Face Prompt Guard 2. Use when an agent ingests untrusted external content and you need to screen it for injected instructions before the LLM processes it.
Reddit Ads API - campaigns, targeting, conversions, agentic optimization
Add or read comments on an OpenAnt task. Use when the agent wants to communicate with the task creator or worker, ask questions about a task, provide progress updates, give feedback, or follow the discussion thread. Covers "comment on task", "ask the creator", "update progress", "read comments", "what did they say".