rails-audit-thoughtbot
Perform comprehensive code audits of Ruby on Rails applications based on thoughtbot best practices. Use this skill when the user requests a code audit, code review, quality assessment, or analysis of a Rails application. The skill analyzes the entire codebase focusing on testing practices (RSpec), security vulnerabilities, code design (skinny controllers, domain models, PORO with ActiveModel), Rails conventions, database optimization, and Ruby best practices. Outputs a detailed markdown audit report grouped by category (Testing, Security, Models, Controllers, Code Design, Views) with severity levels (Critical, High, Medium, Low) within each category.
cactus
Ask the human without stopping work. Use cactus instead of AskUserQuestion whenever a question can be posted now and collected later, whenever several decisions belong to one thread, whenever you are announcing what you will do anyway and only need a veto, whenever a command needs approval, or whenever a verify block or an ordered checklist should stay on the human's board across sessions. Triggers on "cactus", "ask the human", "park this question", "put it on the board", "I need a decision but keep going". Do NOT use for a question whose answer you need before the next keystroke and that has no sensible default; that is AskUserQuestion.
cross-cutting-checklist-generator
Detect when a concern is scattered across many places in a project (config keys, enums, translations, feature flags, API endpoints, doc references) and auto-generate a project-local checklist so that same concern is never partially updated again. Use whenever you add or change something that also exists in several other spots, or when you edit something that already has a checklist. Runs fully autonomously with no user effort, including inside subagents.
React Native New Architecture Migration Skill
This skill audits a bare React Native project and produces a structured `MIGRATION_AUDIT.md` report. It does NOT rewrite code â it identifies what needs to change, classifies severity, and gives the developer a clear, prioritized plan.
gh-batch-runner
Run several planned GitHub Issues as one unit: collect them under an Epic Issue with sub-issues, implement each on a shared epic/ branch in dependency order, verify the branch as a whole, and open a single Pull Request into the integration branch. Use when the user wants several Issues fixed, verified and released together. Triggers include requests such as Issue1,2,3ãã¾ã¨ãã¦å¯¾å¿ã㦠/ ã¾ã¨ãã¦ä¿®æ£ãã¦ãªãªã¼ã¹ããã / ãã®3件ãä¸ç·ã«åºãã¦, 'batch these issues', 'do issues 1 2 3 together', 'ship these issues as one release'. Prerequisite: every member Issue needs an agreed plan comment from gh-issue-planner. For a single Issue, or for Issues that ship separately, use gh-issue-resolver instead.
03
Run an autonomous consensus round between agent peers on several machines: they negotiate a decision over a shared channel and execute it without a human relaying messages. Use when multiple machines must agree on a change. Triggers: "/03", "work it out among yourselves", "find consensus". Wakes the human only for irreversible actions or a deadlock.
analyzing-dotnet-performance
Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy patterns.
agent-architecture-audit
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature.
bible-merger
Merge multiple book analyses into a unified bible. Use after analyzing several books from a series to consolidate characters, style patterns, and structure into a single canonical reference.
Operating CodeGrid
CodeGrid is a native macOS workspace where several coding agents run side by side, each in its own **pane** on an infinite 2D **canvas**. It is *local-first*: no cloud, no account, no stored API keys. Agents are launched as normal CLIs (`claude`, `codex`, `gemini`, `cursor-agent`, `grok`, or a shell
ai-cost-manager-reducer
Audit AI-powered features, API calls, agent workflows, prompt chains, tool calls, embeddings, vector search, retries, streaming flows, background jobs, cron tasks, caching layers, logs, and model selection logic to detect token waste, repeated AI calls, and inefficient prompt chains. Produces severity-ranked findings with safe optimization strategies that preserve reasoning quality and chain reliability. Use before approving or modifying any AI-powered feature, agent workflow, or model-calling code.
3d-modeler
Pure-Python OBJ mesh work: generate a mesh from a JSON spec, merge several OBJ files, and audit an existing mesh (winding, degenerate faces, bounds). Runs inside the process sandbox.