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Found 13,480 Skills
Event-sourced task system for agent collaboration. Use when managing tasks, tracking work, handing off between agents, checking task queues, or reporting progress. Provides both MCP tools and a CLI (ql).
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
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.
Run a two-agent code review: spawn two fresh, clean-context agents that examine the SAME committed branch diff in parallel. One agent runs Codex's native `codex review --base` command, while the other independently reviews the code against Google's "What to look for in a code review" guidance. Merge both outputs into one agreement-ranked report. Use this whenever the user asks for "review-all", a second-opinion review, a dual review, a cross-check before a PR, or a maximum-confidence review of committed branch changes. Do not use it to APPLY fixes; it is review-only.
One-click generation workflow for orchestrating and coordinating major plot points and detailed plot points. Suitable for structured analysis of long stories and complex tasks requiring modular Agent collaboration
LangChain workflows for `create_agent`, LCEL chains, `bind_tools`, middleware, and structured output with production-safe orchestration. Use when implementing or refactoring LangChain application logic in Python or TypeScript.
Dispatch a swain artifact to a GitHub Actions runner for autonomous implementation via Claude Code Action. Creates a GitHub Issue with the artifact content and triggers the workflow for background execution. Use when the user says 'dispatch', 'send to background agent', 'run this autonomously', 'GitHub Actions', or wants to hand off a SPEC for autonomous implementation.
Eval enablement accelerator — help customers think through "what does good look like" for their AI agent, then generate a structured eval plan and test cases they can use immediately. No running agent required. Works from a description, an idea, or even a vague goal. Use when anyone mentions agent evaluation, eval planning, "what should we test", "how do we know if the agent is good", test case generation, or interpreting eval results.
Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation
Search Twitter for trending promotional posts related to coding/AI agent tools, generate reply drafts with the pikiclaw GitHub card, and push the results to Feishu Doc along with bot notifications. Does NOT auto-post to Twitter.
AI-powered crypto trading agent via natural language. Use when the user wants to trade crypto (buy/sell/swap tokens), check portfolio balances, view token prices, transfer crypto, manage NFTs, use leverage, bet on Polymarket, deploy tokens, set up automated trading strategies, submit raw transactions, execute calldata, or send transaction JSON. Supports Base, Ethereum, Polygon, Solana, and Unichain. Comprehensive capabilities include trading, portfolio management, market research, NFT operations, prediction markets, leverage trading, DeFi operations, automation, and arbitrary transaction submission.
Guide for creating effective opencode skills. Use for creating or updating skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. Examples: - user: "Create a skill for git workflows" → define SKILL.md with instructions and examples - user: "Add examples to my skill" → follow the user: "query" → action pattern - user: "Update skill description" → use literal block scalar and trigger contexts - user: "Structure a complex skill" → organize with scripts/ and references/ directories - user: "Validate my skill" → check structure, frontmatter, and discovery triggers