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Found 6,591 Skills
Internal performance review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks the changed paths for algorithmic and resource regressions: N+1s, hot-path allocations, asset weight, and leaks. Findings only; never edits code.
Internal security review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks secrets, input validation, injection, authn/authz, PII exposure, and dependency risk on the changed surface. Findings only; never edits code.
Internal tech-debt transform pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Transforms the synthesized findings table into explicit debt items, each with a re-trigger condition; it does not rescan the diff. Findings only; never edits code.
Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. Use when building, deploying, or hardening agentic AI systems that invoke tools, access data, or interact with production infrastructure.
Secure Model Context Protocol (MCP) servers with transport encryption, tool authorization, input validation, and audit logging for safe AI agent integrations.
Set up OpenClaw locally and run it reliably on a Mac mini for private, always-on local agent workflows.
Review AI-agent-implemented code specifically, in four ordered passes for convention-fit, agent-slop signatures, requirement-completeness, then correctness, against the working tree or the branch diff, findings ranked by severity and backed by quoted evidence. Use when the user says "review this code", "review my changes", "review this diff", "check the agent's work", "/reviewkit", or wants a self-review of AI-written changes before commit or PR, even if they don't name the passes.
Sharpen the prompt before you send it, whether the one-shot instruction you're about to hand a coding agent or the system prompt your application ships. Use when the user says "optimize this prompt", "what's wrong with this prompt", "write or rewrite the system prompt my app ships", or "/promptkit".
Use this skill when users work within a local, single-user, compound-growth Markdown personal wiki (Karpathy's 'LLM owns wiki' model) — covering: ingesting raw/ materials (papers/ clippings/ external repo symlinks), querying and cross-page synthesis/ contradiction reconciliation, archiving conclusions back to the wiki, linting orphan/ outdated summaries, and format upgrades. Three core rules: raw/ controlled by users + wiki/ owned by LLM + AGENTS.md as the single source of truth. Triggers: "Ingest this paper into the wiki" / "Does the wiki have/ summarize content about X?" / "There's a contradiction between A and B in the wiki" / "Save the previous conclusion to the wiki" / "Check the wiki for orphan pages/ outdated summaries" / "Upgrade the wiki/ check wiki version" / "Include repo X in the wiki". Always use this skill whenever users need to digest materials/ retrieve wiki deposits/ archive new conclusions — even if they don't mention the skill name. Not applicable to: cloud/ team wikis (Notion/ Confluence/ Outline, etc.); wiki metadata configuration, wiki creation/deletion, session start/stop (use a single llmw command directly). **Trigger only when the cwd is the wiki root (containing `wiki_metadata.toml` + AGENTS.md skeleton)**; cross-wiki/ workspace operations go to `yzr-llm-workspace-management`; not applicable to other directories.
Create a new AI agent skill from scratch, with kit-convention naming, drafting, live testing, and publishing included. Use when the user wants to author, scaffold, or draft a new skill, runs "/skillkit", or says something like "help me make a skill for X". Interviews for intent, proposes on-brand kit names, drafts a conventions-compliant SKILL.md.
Use this skill whenever a question should be answered from documents stored in a LlamaCloud Index v2 knowledge base — retrieving passages, locating indexed files, or searching indexed content through the LlamaParse Platform REST API with curl. Teaches agentic retrieval — navigating an index like a file system instead of one-shot RAG.
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.