Total 31,956 skills, AI & Machine Learning has 5157 skills
Showing 12 of 5157 skills
Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.
Universal principles for agentic development when collaborating with AI agents. Defines divide-and-conquer, context management, abstraction level selection, and an automation philosophy. Applicable to all AI coding tools.
AI agent configuration policy and security guide. Project description file writing, Hooks/Skills/Plugins setup, security policy, team shared workflow definition.
Auto-generates an LLM usage monitoring page in a PM admin dashboard. Tokuin CLI-based token/cost/latency tracking + user ranking system + inactive user tracking + data-driven PM insights + Cmd+K global search + per-user drilldown navigation. Supports OpenAI/Anthropic/Gemini/OpenRouter.
Manage AI coding agents on a visual Kanban board. Run parallel agents through a To Do→In Progress→Review→Done flow with automatic git worktree isolation and GitHub PR creation.
Full lifecycle orchestrator - spec/impl/test. Spawn-wait-close pipeline with inline discuss subagent, shared explore cache, fast-advance, and consensus severity routing.
Explore-first wave pipeline. Decomposes requirement into exploration angles, runs wave exploration via spawn_agents_on_csv, synthesizes findings into execution tasks with cross-phase context linking (E*→T*), then wave-executes via spawn_agents_on_csv.
Use when working with Anthropic Claude Agent SDK. Provides architecture guidance, implementation patterns, best practices, and common pitfalls.
Active knowledge intelligence. Runs Mine → Grow → Defrag cycle. Mine extracts signal from git/.agents/code. Grow validates existing learnings against current reality, synthesizes cross-domain insights, traces provenance chains, and identifies knowledge gaps. Defrag cleans up. Triggers: "athena", "knowledge cycle", "mine and grow", "knowledge defrag", "clean flywheel", "grow knowledge".
Create and optimize CLAUDE.md memory files or .claude/rules/ modular rules for Claude Code projects. Comprehensive guidance on file hierarchy, content structure, path-scoped rules, best practices, and anti-patterns. Use when working with CLAUDE.md files, .claude/rules directories, setting up new projects, or improving Claude Code's context awareness.
Manage entity status transitions in Basic Memory: archive completed work, move notes between status folders, update frontmatter, and handle edge cases. Use when marking items complete, archiving old entities, or managing any folder-based status workflow.
Mine LITCOIN — a proof-of-comprehension and proof-of-research cryptocurrency on Base. Use when the user wants to mine crypto with AI, earn tokens through reading comprehension or solving optimization problems, stake LITCOIN, open vaults, mint LITCREDIT (compute-pegged stablecoin), manage mining guilds, run autonomous research experiments, deploy agents, or interact with the LITCOIN DeFi protocol. Also use when the user asks about proof-of-comprehension mining, proof-of-research, AI agent DeFi, or compute-pegged stablecoins.