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Found 6,719 Skills
Read Cursor / Claude Code / Codex agent logs and call write_knowledge for each durable learning found. Default auto-writes then reports what was cached and expected token savings (analytics-style: rediscovery/generation cost reused on each future read), and opens the HTML report. Dry-run lists the exact write_knowledge payloads (title, content, repo, branch) without writing. Use when the user says "Please bootstrap my knowledge with Dosu", "bootstrap agent knowledge", "/bootstrap-agent-knowledge", "log to dosu knowledge", "mine my sessions into Dosu", "backfill branch notes from my agent logs", "save my agent logs to Dosu", or wants a one-shot pass over local histories.
Use when adding, pruning, archiving, restoring, or reviewing Agent Notes — checking new notes for superseded active records, classifying implemented notes by future decision value, deleting rejected notes that no longer prevent a tempting mistake, and moving low-value notes to the frozen archive.
Emit and maintain a dagr run file — a live, contract-valid JSON description of recursive projects, tasks, attempts, gates, evidence, policies, events, and operator-message resolutions that `dagr view` renders as a DAG. Use when orchestrating agents or tracking multi-step work that a dagr pane should display.
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and cross-page synthesis/ contradiction reconciliation, archiving conclusions back to the wiki, linting orphan/ outdated summaries, and format upgrades.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between A and B in the wiki" / "Save the previous conclusion to the wiki" / "Check the wiki for orphan pages/ outdated summaries" / "Upgrade the wiki/ check wiki version" / "Include repo X in the wiki".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.
Build AI features with the first-party Laravel AI SDK (Laravel 13+); agents, embeddings, images, audio, and tool calling with provider-agnostic APIs
Set up or extend Polygres from either a short request such as "Help me set up Polygres" or a detailed ingestion, memory, graph, embedding, synchronization, or retrieval specification. Also use for questions such as "What can I do with Polygres?" by scanning the accessible current workspace and project read-only and giving a personalized recommendation without changing anything. Ask one short direction question first when a setup request identifies neither a source nor an outcome; otherwise inspect the user's accessible data and application, resolve only critical unknowns, design the smallest useful schema and retrieval setup, generate source-specific ingestion and retrieval code, configure the selected project after one consolidated approval, verify a small vertical slice, and optionally connect capture and recall to the user's agent. Use whenever the user intends to make their data usable through Polygres, even if they do not say "data pipeline."
Create, list, inspect, update, or delete AgentMail inboxes through the connected MCP server. Use for ANY inbox lifecycle request — even a quick list-inboxes or a simple delete; deletion safeguards apply regardless of task size. Also use when the user asks for a new agent email address, wants inbox details changed, or removes an inbox; do not use for sending mail (agentmail-send-email), reading or triage (agentmail-check-email), or MCP connection setup (agentmail-mcp).
Deep-dive into any codebase and produce structured knowledge artifacts that turn a coding agent into a codebase specialist. This skill should be used when the user asks to "study this repo", "understand this codebase", "document this project", "onboard me onto this code", "create codebase knowledge", "map this architecture", or when asked to produce knowledge artifacts for any agent working on an unfamiliar repository.
Approach board for a problem or a decision. Dispatches independent seats — some generating candidate approaches, including one agent outside this process entirely, some attacking the assembled set comparatively — then verifies the surviving objections and reports a ranked recommendation with its trade-offs. An approach already on the table enters as one candidate among several. Autonomous; changes nothing.
Infrastructure deployment for web novel writing toolset. Provides built-in adapters for Claude Code / OpenCode / Codex / ZCode / OpenClaw / Reasonix; Web AI / general Agents can adopt the skills + AGENTS.md file mode. Trigger methods: /story-setup, $story-setup, "Prepare to write a book", "Help me set up the environment", "Configure writing project"
Multi-perspective adversarial review. Full/lean modes spawn in parallel when reviewer agents are deployed; automatically degrade to solo mode when agents are missing/abnormal or spawn fails; use built-in rubric fallback when reference files are unreadable. Trigger methods: /story-review, /审查, "审查一下", "帮我审一下"