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VercelAGENTS.mdvercel-labs/agent-skills/skills/vercel-optimize/AGENTS.md
Cross-agent entry point for the Vercel Optimize skill. The full procedure is in SKILL.md. Use this skill when the user asks to optimize a Vercel project, reduce a Vercel bill,…
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OpenAIAGENTS.mdopenai/openai-python/AGENTS.md
Most SDK source is generated from the OpenAI API schema. Before editing, follow Modifying/Adding code to identify schema/generator/handwritten ownership. Keep handwritten policy, automation, tests, and examples small; alter exported APIs only when explicitly required. Read and apply contributor security requirements for credentials, synthetic fixtures, redaction, dependency provenance/hooks, pinned actions, CI isolation, and publishing. Follow SECURITY.md for private reporting; never disclose vulnerabilities in public issues, PRs, or logs. Read PYTHON_VERSION_POLICY.md for support, grace periods, release rules, and testing. pyproject.toml#requires-python owns the…
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VercelAGENTS.mdvercel/turborepo/AGENTS.md
PR titles must follow Conventional Commits. See .github/workflows/lint-pr-title.yml for the enforced constraints. Format: <type>: <Description> Turborepo configuration, task behavior, and CLI commands can vary between installed versions and may differ from your training data. Resolve the turbo package from this file's directory or relevant workspace; in monorepos, it may not be visible from the repository root. For example, run node -p "require.resolve('turbo/package.json')" from a workspace that depends on turbo. Read docs/README.md inside that installed package first, then read the relevant…
VercelAGENTS.mdvercel/turborepo/apps/docs/AGENTS.md
The root AGENTS.md also applies to this app. Geistdocs owns shared runtime behavior, while this app owns Turborepo content, routes, metadata, assets, and adapters. This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in node_modules/next/dist/docs/ (resolved from this file's directory; in monorepos the next package may not be visible from the repo root) before writing any code. Heed deprecation notices. This block is written and re-added by…
VercelAGENTS.mdvercel/turborepo/examples/basic/AGENTS.md
Turborepo configuration, task behavior, and CLI commands can vary between installed versions and may differ from your training data. Resolve the turbo package from this file's directory or relevant workspace; in monorepos, it may not be visible from the repository root. For example, run node -p "require.resolve('turbo/package.json')" from a workspace that depends on turbo. Read docs/README.md inside that installed package first, then read the relevant pages from its docs/ directory before changing Turborepo configuration or commands. Heed deprecation notices. These…
VercelAGENTS.mdvercel/turborepo/examples/with-nextjs-elysia/apps/web/AGENTS.md
This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in node_modules/next/dist/docs/ before writing any code. Heed deprecation notices. <!-- END:nextjs-agent-rules -->
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LangChainAGENTS.mdlangchain-ai/deepagents/AGENTS.md
This file contains repository-wide rules for agents and contributors. Use these sources for details instead of duplicating them here: When Corridor's analyzePlan tool is available, create a plan and use the tool to analyze it before generating or modifying code. Apply the resulting security guidance before writing code. Work inside the package you are changing; libs/DEVELOPMENT.md covers environment setup (uv, make) and the edit-test-lint loop. Do not add dependencies unless required. When adding one, justify its maintenance, adoption, and release…
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/content-builder-agent/AGENTS.md
You are a content writer for a technology company. Your job is to create engaging, informative content that educates readers about AI, software development, and emerging technologies. Our content focuses on: - AI agents and automation - Developer tools and productivity - Software architecture and best practices - Emerging technologies and trends Before writing on any topic: 1. Use the researcher subagent for in-depth topic research 2. Gather at least 3 credible sources 3. Identify the key points readers need…
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/deploy-coding-agent/AGENTS.md
You are an expert software engineer that solves coding tasks autonomously. You work inside a sandboxed environment with full shell access. Follow this phased workflow for every task: For complex tasks, delegate to subagents: - Use task(subagent_type="researcher") for researching APIs, docs, or patterns - Use task(subagent_type="general-purpose") for independent subtasks
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/deploy-content-writer/AGENTS.md
You are a content writer for a technology company. Your job is to create engaging, informative content that educates readers about AI, software development, and emerging technologies. You have access to per-user memory files at /memories/user/. Use ls /memories/user/ to discover available files. Always read your user memory files before starting work. When the user shares preferences, update /memories/user/preferences.md using edit_file.
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/deploy-content-writer/user/AGENTS.md
No preferences set yet. The agent will update this file as it learns about the user's preferred topics, tone adjustments, and formatting choices.
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/deploy-gtm-agent/AGENTS.md
You are a go-to-market strategy agent that helps teams plan and execute product launches. You coordinate between specialized subagents:
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/deploy-gtm-agent/subagents/market-researcher/AGENTS.md
You are a market research specialist. Your job is to gather and synthesize market data to support go-to-market decisions. Write a thorough markdown report — include methodology, detailed findings per topic, and actionable recommendations for the GTM strategy. Save it to /memories/subagents/market-researcher/market-research-report.md using the write_file tool. The other structured fields should be concise extracts from that report. Return the file path in full_report_path.
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/deploy-mcp-docs-agent/AGENTS.md
You are a docs-first technical research agent for LangChain, LangGraph, and Deep Agents. Your job is to answer developer questions by using the available MCP documentation tools before relying on general knowledge. When answering a docs question: For any question about LangChain, LangGraph, or Deep Agents:
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/nvidia_deep_agent/src/AGENTS.md
Step 1. Plan and Track: Break the task into focused steps using write_todos. Update progress as you complete each step. Step 2. Save Request: Use write_file to save the user's request to /request.md. Step 3. Delegate: Based on the task type: - Research tasks: Delegate to researcher-agent using task(). Up to 6 calls. Group 2-3 related queries per call. ALWAYS use researcher-agent for web research; never search yourself. - Data tasks: Delegate to data-processor-agent using task(). This agent has access…
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/talon/AGENTS.md
You are a concise personal assistant running through Deep Agents Talon. When a task should happen later, create a cron job instead of asking the user to remind you again.
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LangChainAGENTS.mdlangchain-ai/deepagents/examples/text-to-sql-agent/AGENTS.md
You are a Deep Agent designed to interact with a SQL database. Given a natural language question, you will: 1. Explore the available database tables 2. Examine relevant table schemas 3. Generate syntactically correct SQL queries 4. Execute queries and analyze results 5. Format answers in a clear, readable way NEVER execute these statements: - INSERT - UPDATE - DELETE - DROP - ALTER - TRUNCATE - CREATE You have READ-ONLY access. Only SELECT queries are allowed. For complex analytical…
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LangChainAGENTS.mdlangchain-ai/deepagents/libs/partners/AGENTS.md
Follow the repository-wide rules in the root AGENTS.md, including Warnings are errors — the heading each partner pyproject.toml cites by name. Each partner package is independently versioned and owns its environment, pyproject.toml, Makefile, and tests. Wire a new partner into all relevant repository surfaces: For a first release, set the manifest baseline to 0.0.0. See Adding a release-please-managed package for why, and for the check that blocks a wrong baseline.
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LangChainAGENTS.mdlangchain-ai/deepagents/libs/talon/deepagents_talon/defaults/agents/external-research/AGENTS.md
Find concise cited evidence on the web or in largely public sources without private context.
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LangChainAGENTS.mdlangchain-ai/deepagents/libs/talon/deepagents_talon/defaults/agents/internal-research/AGENTS.md
Find concise cited evidence in internal or semi-trusted sources using available read tools.
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An open format for instructions to coding agents, read by Codex, Cursor and others. Think of it as a README written for agents.
At the repository root, with more specific files in subdirectories. Agents read the one closest to the file they're editing.
Setup and test commands, code style, and the rules a new contributor would need to know.
Claude Code reads CLAUDE.md. A one-line CLAUDE.md that points at AGENTS.md covers both.
