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Found 12,034 Skills
Code review practices with technical rigor and verification gates. Practices: receiving feedback, requesting reviews, verification gates. Capabilities: technical evaluation, evidence-based claims, PR review, subagent-driven review, completion verification. Actions: review, evaluate, verify, validate code changes. Keywords: code review, PR review, pull request, technical feedback, review feedback, completion claim, verification, evidence-based, code quality, review request, technical rigor, subagent review, code-reviewer, review gate, merge criteria. Use when: receiving code review feedback, completing major features, making completion claims, requesting systematic reviews, validating before merge, preventing false completion claims.
Terminal-Bench integration for Mux agent benchmarking and failure analysis
Inline adversarial plan review — 3 sequential checks (Feasibility, Completeness, Scope & Alignment) performed by the calling LLM in its own context. No subagents spawned. Call after saving a plan. Returns GATE_PASS or GATE_FAIL with blocking issues.
Documentation-driven development specification that requires Agent to consult official documentation and examples before generating code or fixing bugs, including API verification processes, search strategies and MCP invocation rules. It is applicable to scenarios such as accessing third-party libraries, troubleshooting API errors, and version changes.
Ethereum development knowledge for AI agents — from idea to deployed dApp. Fetch real-time docs on gas costs, Solidity patterns, Scaffold-ETH 2, Layer 2s, DeFi composability, security, testing, and production deployment. Use when: (1) building any Ethereum or EVM dApp, (2) writing or reviewing Solidity contracts, (3) deploying to mainnet or L2s, (4) the user asks about gas, tokens, wallets, or smart contracts, (5) any web3/blockchain/onchain development task. NOT for: trading, price checking, or portfolio management — use a trading skill for those.
Run a structured, adversarial multi-agent bug review pipeline on a codebase. Use this skill whenever the user wants to find bugs, audit code quality, review a codebase for issues, or run any kind of bug-finding or code analysis workflow. Also trigger when the user asks to 'review my code for bugs', 'find all issues in this repo', 'audit this codebase', or any similar request. The pipeline uses three sequential phases: a Bug Finder that maximizes issue discovery, a Bug Adversary that challenges false positives, and an Arbiter that issues final verdicts — producing a clean, high-confidence bug report.
Multi-agent workflow orchestration for OpenClaw. Use when user mentions antfarm, asks to run a multi-step workflow (feature dev, bug fix, security audit), or wants to install/uninstall/check status of antfarm workflows.
Bootstrap, install, and operate an external task-management CLI as the source of truth for agent execution tracking (instead of built-in todos). Provides the abstraction layer between spec-management intent (implementation plans and tasks) and concrete CLI commands. MUST be invoked when any implementation-tier artifact (SPEC, STORY, BUG) comes up for implementation — create a tracked plan before writing code. Optional but recommended for complex SPIKEs. For coordination-tier artifacts (EPIC, VISION, JOURNEY), spec-management must decompose into implementable children first — this skill tracks the children, not the container. Also use for standalone tasks that require backend portability, persistent progress across agent runtimes, or external supervision. Use this skill whenever the user asks to track tasks, create an implementation plan, check what to work on next, see task status, manage dependencies between work items, or close/abandon tasks — even if they don't mention "execution tracking" explicitly.
Modern Python 3.12+ patterns your AI agent should use. Type hints, async/await, Pydantic v2, uv, match statements, and project structure.
Linear GraphQL patterns for Symphony agents. Use `linear_graphql` for all operations — comments, state transitions, PR attachments, file uploads, and issue creation. Never use schema introspection.
Register AI agents on Ethereum mainnet using ERC-8004 (Trustless Agents). Use when the user wants to register their agent identity on-chain, create an agent profile, claim an agent NFT, set up agent reputation, or make their agent discoverable. Handles bridging ETH to mainnet, IPFS upload, and on-chain registration.
Use when starting work on any project to produce or update living documentation (TechStack.md, ProjectStructure.md) that bootstraps context for any AI agent session. Run before any feature work, or periodically to keep docs current.