Use when adding, reviewing, or troubleshooting an interactive release-note Try This experience, including commands, views, editor samples, Chat drafts, guided spotlights, prerequisites, scoped targets, or Agents-window handoff.
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Analyze V8 heap snapshots to investigate memory leaks and retention issues. Use when given .heapsnapshot files, asked to compare before/after snapshots, asked to find what retains objects, or investigating why objects survive GC. Provides snapshot parsing, comparison, retainer-path helpers, and scratchpad scripts.
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Use whenever adding, modifying, or reviewing any Copilot, agent, LLM, AI, tool, permission, sandbox, MCP, model, telemetry, feature-gate, setting, configuration, or enterprise control—especially anything an organization or administrator may need to manage. Start here to decide whether it belongs in runtime managed settings, a typed SDK contract, VS Code configuration policy, extension policy, or a split implementation. Run on every new Copilot/agent/LLM control and ANY change that adds a `policy:` field.
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Multi-Chat Architecture
> Node runtime service construction is documented separately in [`node/serviceBootstrapping.md`](node/serviceBootstrapping.md). Host startup timings, process-lifetime correlation, and session workload counts are documented in [`PERFORMANCE.md`](PERFORMANCE.md).
> **Status: COMPLETE
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tools registered by Agent Host. This internal debugging setting assumes those values use the documented string shape.
> **Launch-time freeze.** The SDK accepts a system message only at session create/resume
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Perform a code review of the current session's changes. Use when the user requests a code review via the Run Code Review button in the Changes toolbar.
Create or update repository skills and instructions when major learnings are discovered during a session. Use when the user says "learn!", when a significant pattern or pitfall is identified, or when reusable domain knowledge should be captured for future sessions.
file changes (path filters)
- CI clippy job runs without `-D warnings` — weaker than the documented gate; known pre-existing failures recorded in docs/g002/g003 maps
- `claw acp` is a status stub
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awesome-go · LLM Contribution Guide
This document summarizes the project context and the conventions that language models must follow when helping on this repository.
## Project Snapshot
- Purpose: maintain the curated
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Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).
After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.
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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
autogpt_platform/frontend` – Next.js + Typescript frontend.
- `autogpt_platform/docker-compose.yml` – development stack.
See `docs/platform/getting-started.md` for setup instructions.
## Documentation
- `docs/home/`, `docs/platform/` and `docs/integrations/` are the public docs site (agpt.co/docs). Only pages written
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support
- **Frontend** (`frontend`): Next.js React application
- **Shared Libraries** (`autogpt_libs`): Common Python utilities
## Component Documentation
- **Backend**: See @backend/AGENTS.md for backend-specific commands, architecture, and development tasks
- **Frontend**: See @frontend/AGENTS.md
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React Best Practices
**Version 1.0.0**
Vercel Engineering
January 2026
> **Note:**
> This document is mainly for agents and LLMs to follow when maintaining,
> generating, or refactoring React and Next.js codebases
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Plain text files in a repository that tell a coding agent how the project works: commands to run, conventions to follow and things to avoid. CLAUDE.md, AGENTS.md, cursor rules and skills are the common kinds.
CLAUDE.md or AGENTS.md?
CLAUDE.md is read by Claude Code. AGENTS.md is an open format that Codex, Cursor and other agents read. Many projects keep one and point the other at it.
What is a skill?
A folder with a SKILL.md that describes one capability, such as filling PDFs or reviewing code. The agent loads it only when the task calls for it.
Can I search my own team's files too?
Your agents already can, over MCP, limited to the files you're allowed to read. Searching them from this page is coming.