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Found 11,954 Skills
#1 on DeepResearch Bench (Feb 2026). Any-to-Any AI for agents. Combines deep reasoning with all modalities through sophisticated multi-agent orchestration. Research, videos, images, audio, dashboards, presentations, spreadsheets, and more.
Real-time sports & events data for AI agents via Shipp. Use when the user wants live scores, schedules, or game events for NBA, NFL, NCAA Football, MLB, or Soccer — especially to power prediction market trading strategies on Polymarket or Kalshi using a MoonPay wallet.
Interact with the learning system: show stats, list/search accumulated knowledge, and graduate mature entries into agents/skills. Backed by learning.db (SQLite + FTS5). Use when user says "retro", "retro list", "retro search", "retro graduate", "check knowledge", "what have we learned", "knowledge health", "graduate knowledge".
Collaborative coding with enforced micro-steps: announce, show diff, wait for confirmation, apply, verify. User controls pace with commands. Works with any domain agent as the executor. Use when: "pair program", "pair with me", "let's code together", "step by step coding", "walk me through implementing", "code with me"
Execute wave-ordered implementation plan by dispatching tasks to domain agents. Use after /feature-plan produces a plan. Use for "implement feature", "execute plan", "start building", or "/feature-implement". Do NOT use without a plan or for ad-hoc coding tasks.
Operate `superise market-sustain` for SupeRISE Market self-supervision. Use when the user asks the agent to keep itself alive, monitor balance or runway, inspect market pricing, top up a market account, retry pending top-up orders, clear market auth state, or change sustain guardrails and config.
Chain patterns for CC 2.1.71 pipelines — MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building multi-phase pipeline skills. Loaded via skills: field by pipeline skills (fix-issue, implement, brainstorm, verify). Not user-invocable.
Comprehensive verification with parallel test agents. Use when verifying implementations or validating changes.
End-to-end testing patterns with Playwright — page objects, AI agent testing, visual regression, accessibility testing with axe-core, and CI integration. Use when writing E2E tests, setting up Playwright, implementing visual regression, or testing accessibility.
Multi-agent swarm coordination for complex tasks. Uses hierarchical topology with specialized agents to break down and execute complex work across multiple files and modules. Use when: 3+ files need changes, new feature implementation, cross-module refactoring, API changes with tests, security-related changes, performance optimization across codebase, database schema changes. Skip when: single file edits, simple bug fixes (1-2 lines), documentation updates, configuration changes, quick exploration.
Overview The Messari Tracker Agent serves as a direct bridge to Messari’s institutional-grade data sources, allowing users to extract BTC and ETH data without manual searching or fragmented data sourc
MCP Server connecting AI agents to 28 Brazilian public APIs covering economy, legislation, transparency, judiciary, elections, environment, health, and more