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Found 62 Skills
Install, initialize, verify, and troubleshoot RTK (Rust Token Killer) for AI coding agents. Use when you need to reduce shell-command token output, confirm that the correct `rtk` binary is installed, choose between Homebrew, install.sh, or Cargo installation, wire `rtk init` for Claude Code, Codex, Gemini CLI, Cursor, Copilot, Windsurf, Cline, or OpenCode, or use compact wrappers such as `rtk git status`, `rtk read`, `rtk grep`, `rtk test`, `rtk lint`, and `rtk gain`. Triggers on: rtk, rust token killer, token saver cli, rtk init, rtk gain, codex rtk, gemini rtk, opencode rtk, claude hook token reduction.
Harness engineering for AI coding agents — five subsystems, memory persistence, session continuity, verification workflows, scope control, lifecycle management.
Request coding agents to review code, verify review results and fix confirmed issues
A shared, file-based town square where multiple coding agents talk, coordinate, and debate — no server required. Use whenever more than one agent works the same repo (parallel Claude Code or Codex sessions, separate git worktrees, a fleet splitting a task) and they must stay out of each other's way or think together. TRIGGER on phrasings like "coordinate with the other agent/session", "post to / check the agora", "ask the other agents", "leave a message for whoever's working on X", "announce what files you're touching", "is anyone else editing this?", or any time you're about to edit shared code while other agents are live. Also trigger when an agent is stuck and wants a peer's second opinion, or when several agents each drafted a design (an API, a schema, an architecture) and the group needs to compare the proposals and converge on the best one. Works for any agent that can run a Python script, not just Claude Code.
Fast web browsing and web app testing for AI coding agents via persistent headless Chromium daemon. Browse any URL, read page content, click elements, fill forms, run JavaScript, take screenshots, inspect CSS/DOM, capture console/network logs, and more. Ideal for verifying local dev servers, testing UI changes, and validating web app behavior end-to-end. ~100ms per command after first call. Works with Claude Code, Cursor, Cline, Windsurf, and any agent that can run Bash. No MCP, no Chrome extension — just fast CLI.
Disciplined spec-driven test-driven development workflow for building software with AI coding agents. Transforms ambiguous requests into verified implementations through structured specification, test derivation, and strict TDD. Handles greenfield projects, brownfield enhancements (with or without existing tests), refactors, and complex bug fixes with workflow-specific guidance for each. Use when the user requests a new feature, module, enhancement, refactor, API, data pipeline, CLI tool, or system with multiple requirements, edge cases, or unclear specifications. Also use for complex bug fixes requiring root cause analysis. Triggers on phrases like "add a feature", "implement", "build a new module", "build an API", "build a CLI", "build a data pipeline", "refactor", "fix this bug", "write tests for", "TDD", "test-first", "the requirements are unclear", "characterization tests", or "spec this out". Triggers when modifying code with adjacent test files (`tests/`, `*_test.py`, `*.test.ts`, `*.spec.ts`, `spec/`, `__tests__/`) or test framework config (pytest.ini, jest.config.*, go.mod with testing imports, Cargo.toml with [dev-dependencies], package.json with a test script). Triggers when the user mentions edge cases, invariants, acceptance criteria, EARS notation, or red-green-refactor. Do NOT use for simple one-line fixes, cosmetic changes, formatting, renames, dependency bumps, or tasks where requirements are already fully specified with tests provided.
Turn ordinary text plans into rich interactive visual plans with diagrams, file maps, annotated code, open questions, and UI/prototype review when useful.
ByteRover CLI (brv) - Persistent memory layer for AI coding agents with context trees, knowledge storage, and cloud sync
Local-first AI design tool that turns coding agents into design engines with 31 skills, 129 design systems, and multi-format export
Agent-agnostic visual feedback tool for AI coding agents to identify and annotate UI elements with structured selectors
Run multiple AI coding agent sessions in parallel using git worktrees — each agent isolated in its own worktree, working on a separate branch. Use this skill whenever the user wants to: run two or more AI agents simultaneously on different features or bugs, set up isolated agent workspaces in the same repo, push parallel branches to GitHub and open/update PRs, coordinate between concurrent agent sessions, or clean up after merging. Triggers on: "parallel agents", "multiple agent sessions", "git worktree", "run agents in parallel", "work on two things at once", "isolated agent workspace", "spin up another agent", or any request involving simultaneous AI-assisted development streams.
Spawn and manage parallel AI coding agents via tmux. Use when you need to orchestrate workers, delegate sub-tasks, run multi-agent improvement loops, or manage agent lifecycles with orca CLI commands like spawn, list, kill, steer, logs, and daemon.