Use when extending pluginpack itself with support for a new AI app or plugin format — i.e. adding a target adapter to the pluginpack codebase. This is for contributors hacking ON pluginpack, not for end users packaging their plugins.
Use when someone wants to ship one set of agent skills/plugins to multiple AI apps (Claude Code, Cursor, Antigravity CLI, GitHub Copilot, OpenAI Codex CLI) from a single source, and needs to create or update a pluginpack.config.ts — choosing targets, source layout, MCP servers, and output directories.
Use when someone has an existing native plugin repo (a Claude Code marketplace, a Cursor plugin, an Antigravity CLI plugin, a GitHub Copilot plugin, or an OpenAI Codex CLI plugin) and wants to manage it from one portable source via pluginpack — generating the source layout and config, then proving the output matches…
Use when working in a repo that uses pluginpack (it has a pluginpack.config.ts) and you need to build the native plugin outputs, validate them, prune stale files, or check whether a generated plugin repo has drifted — i.e. driving the pluginpack CLI and interpreting its exit codes.
Search the public Glean developer documentation for API, SDK, MCP, authentication, indexing, and integration details. Use when the user is building with Glean rather than searching internal company knowledge — trigger phrases include Glean API, Glean SDK, integrate with Glean, indexing API, Glean Python or JavaScript…
MCP server "gleanplugin" as configured in gleanwork/cursor-plugins. Runs ${CLAUDE_PLUGIN_ROOT}/mcp/start.mjs with node. Needs 2 environment variables to run.
Catch up on what you missed while away — meetings, decisions, action items, mentions, and important threads from a defined time window. Trigger phrases include "what did I miss", "catch me up", "I was out", "I'm back from PTO", "summarize while I was away", "what happened last week while I was off", "I just got back"…
Identify who owns, maintains, or has expertise in a specific code area — use when asked who to talk to about a system, who to request a code review from, or who has been actively working in a codebase area.
Gather architectural context about an internal system from code and documentation across the organization — use when asked to understand a system's architecture, find its repos, or get an overview before working on it.
Find usage examples of an API, library, or code pattern across internal repositories — use when looking for how other teams use an internal API, library, or pattern before implementing something new.
Find subject matter experts on a topic, technology, or internal system by combining real expertise signals (code contributions, document authorship, and meeting or discussion activity) rather than relying on job titles. Use this whenever the user wants to know who to ask about something — phrasings such as who knows…