Skills by am-will

Only to be triggered by explicit /parallel-task commands.

Create comprehensive, phased implementation plans with sprints and atomic tasks. Use when user says: "make a plan", "create a plan", "plan this out", "plan the implementation", "help me plan", "design a plan", "draft a plan", "write a plan", "outline the steps", "break this down into tasks", "what's the plan for", or any similar planning request. Also triggers on explicit "/planner" or "/plan" commands.

Fetch up-to-date library documentation via Context7 API. Use PROACTIVELY when: (1) Working with ANY external library (React, Next.js, Supabase, etc.) (2) User asks about library APIs, patterns, or best practices (3) Implementing features that rely on third-party packages (4) Debugging library-specific issues (5) Need current documentation beyond training data cutoff (6) AND MOST IMPORTANTLY, when you are installing dependencies, libraries, or frameworks you should ALWAYS check the docs to see what the latest versions are. Do not rely on outdated knowledge. Always prefer this over guessing library APIs or using outdated knowledge.

This skill provides a CLI (`scripts/gitmcp.py`) to read and search GitHub repo documentation via the `gitmcp.io` MCP service. It executes an MCP client (`["npx", "-y", "mcp-remote", mcp_url]`) and connects to `gitmcp.io`/other URLs to fetch tools and content.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

Use when user specfically says 'plan harder'.

Only to be triggered by explicit /parallel-task commands.

[EXPLICIT INVOCATION ONLY] Creates dependency-aware implementation plans optimized for parallel multi-agent execution.

This skill orchestrates local CLI LLM planners and a judge to produce merged plans and saves run artifacts under `./llm-council/runs/`. It spawns subprocesses (`subprocess.Popen`) to run commands like `codex`, `claude`, `gemini`, `opencode`, or a user `command`, reads/writes `XDG_CONFIG_HOME/llm-council/agents.json`, and exposes a local UI at `http://127.0.0.1:.../?token=...`, creating high risk from arbitrary command execution and external model network calls.

A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.

This skill is a Vercel-authored React/Next.js performance guide with rules and example files (e.g. `AGENTS.md`, `rules/async-parallel.md`, `README.md`). Example code reads client-side storage (`localStorage`), `document.cookie`, and a cookie named `session-id`, demonstrating credential access in examples.

Query the OpenAI developer documentation via the OpenAI Docs MCP server using CLI (curl/jq). Use whenever a task involves the OpenAI API (Responses, Chat Completions, Realtime, etc.), OpenAI SDKs, ChatGPT Apps SDK, Codex, MCP integrations, endpoint schemas, parameters, limits, or migrations and you need up-to-date official guidance.