Loading...
Loading...
Found 11,833 Skills
Design failing tests for complex features using Independent Evaluation — dispatches a context-free agent that sees only the requirement spec and code paths (not the implementation approach), then returns executable failing tests. Use when starting TDD for a non-trivial feature, when the requirement is ambiguous enough that biased tests are a risk, or when the user asks for independent test design.
Mainstream Spot Order v1.0 — Multi-chain DEX spot trading system. 6-signal ensemble (Momentum, EMA, RSI, MACD, BB, BTC Overlay) on 15m bars, 6 built-in pairs (SOL, ETH, BTC, BNB, AVAX, DOGE), auto-research strategy optimization, per-pair data collection + backtesting + paper/live trading. onchainos CLI driven, Agentic Wallet TEE signing, zero pip dependencies.
Create and manage scheduled shell tasks. Use when: automating recurring operations. NOT for: sending messages (use cron agent).
Use OpenAI Codex from inside Claude Code for code reviews, adversarial reviews, and delegating tasks to Codex as a subagent.
Claude Code skill (trtllm-agent-toolkit): implement or extend TensorRT-LLM AutoDeploy fusion transforms under transform/library/ in a TensorRT-LLM checkout. Prefer existing kernels and custom ops; use Triton only when no viable existing-kernel path exists. Use ad-graph-dump for AD_DUMP_GRAPHS_DIR workflows. Covers TRT-LLM paths, registry, default.yaml registration, graph validation, tests, and a review checklist — without prescribing profiling tools or throughput targets.
Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running auto-research campaigns, experiments, training jobs, model or dataset downloads, shared cache-heavy commands, log-producing runs, checkpoint generation, W&B or Hugging Face authenticated workflows, or any workflow that may create large files on Brev.
Run large codebase migrations and multi-file refactors. Uses the Composio CLI to coordinate issue tracking, batched PRs, and CI verification while the agent executes the transforms locally across hundreds of files.
Drive terminal sessions, panes, and TUIs from an agent — spawn shells, send keystrokes, snapshot pixel-perfect PNGs of any pane, and extend shux itself with line-delimited JSON-RPC plugins in any language. Use when you need to multiplex terminal work, drive a TUI you'd otherwise control with tmux / screen / iTerm2 / expect / pexpect / asciinema / vhs / termshot, run scripted CLI/REPL interactions, do headless visual regression on a terminal UI, or write a process plugin that subscribes to the shux event bus and calls back through `window.rename`, `pane.send_keys`, `state.apply`, etc. Trigger phrases include "drive terminal", "spawn pty session", "send keys to a TUI", "screenshot a tui", "snapshot pane", "replace tmux", "iTerm2 automation", "expect script", "headless terminal test", "agent multiplexer", "asciinema record", "write a shux plugin", "extend shux", "shux plugin install".
Use when the user asks for a broad codebase review, substantial PR/branch review, architecture audit, tech-debt scan, cleanup assessment, structural sanity check, or design-alignment review. Default workflow: use sub-agents when available unless specifically forbidden; do not require the user to mention sub-agents, council mode, delegation, or parallel review. Focus on cruft, duplication, weak boundaries, missed reuse, lifecycle/concurrency risks, test/roadmap drift, and code aesthetics. Do not use for narrow bug fixes, ordinary small-diff reviews, frontend visual QA, repo-onboarding docs, or OpenAI Agents SDK production-readiness review. Output evidence-backed findings first, then pressure points, design alignment, open questions, and follow-through.
Initialize or migrate a repo into the ai-memory pattern: the .ai-memory.toml routing marker (workspace/project), the recall/write routing snippet in CLAUDE.md/AGENTS.md, and the ai-memory MCP server entry. Includes the qmd→ai-memory migration for repos still on the old wiki/qmd stack. Use when the user asks to set up ai-memory in a project (greenfield or brownfield), wire the MCP, enable auto-capture, or migrate off qmd.
Cross-repo migration swarm — one coordinator + N parallel subagents (one per target repo) that apply the same transformation, open PRs, wait for CI, and report back to a shared JSON ledger. Coordinator handles topology, conflict auto-rebase, and stop-on-novel-failure. Use when bumping a shared dependency, rolling out a workflow change, or applying a codemod across the org. Do NOT use for single-repo work — that's /ork:implement.
React and Next.js performance optimization patterns adapted from Vercel Engineering's React Best Practices (https://github.com/vercel-labs/agent-skills). Organizes 70+ rules across 8 priority categories — waterfalls, bundle size, server-side, client fetching, re-render, rendering, JS micro-perf, advanced. Use when writing, reviewing, or refactoring React/Next.js code for performance.