How real projects summarise themselves for language models.
garrytanllms.txtgarrytan/gstack/gstack/llms.txt
Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). - [/qa](qa/SKILL.md): Systematically QA test a web application and fix bugs found. - [/qa-only](qa-only/SKILL.md): Report-only QA testing
koala73llms.txtkoala73/worldmonitor/public/llms-full.txt
# World Monitor > Real-time global intelligence dashboard — AI-powered news aggregation, geopolitical monitoring, and
koala73llms.txtkoala73/worldmonitor/public/llms.txt
feed - [Sandbox](https://www.worldmonitor.app/sandbox/index.json): Deterministic sample responses for representative REST operations — test parsers and integrations with no key and no quota ([guide](https://www.worldmonitor.app/docs/sandbox)) - [Schema Map](https://www.worldmonitor.app
Microsoftllms.txtmicrosoft/ai-agents-for-beginners/15-browser-use/llms.txt
# Check Balance Source: https://docs.browser-use.com/api-reference/accounts/check-balance https://api.browser-use.com/api/v1/openapi.json get /balance Returns the user
mem0aillms.txtmem0ai/mem0/docs/llms.txt
Copilot](https://docs.mem0.ai/platform/copilot) [Platform]: Use when inspecting project memories, reviewing configuration changes, or testing extraction in the dashboard. - [Sign up as an agent](https://docs.mem0.ai/platform/agent-signup) [Platform]: Use when
LangChainllms.txtlangchain-ai/langgraph/docs/llms.txt
/oss/python/langgraph/sql-agent.md) - [Stores](https://docs.langchain.com/oss/python/langgraph/stores.md) - [Streaming](https://docs.langchain.com/oss/python/langgraph/streaming.md) - [LangSmith Studio](https://docs.langchain.com/oss/python/langgraph/studio.md) - [Test](https://docs.langchain.com/oss/python/langgraph/test.md) - [Thinking in LangGraph](https://docs.langchain.com/oss/python/langgraph/thinking-in-langgraph.md) - [Agent Chat UI](https://docs.langchain.com/oss/python/langgraph/ui.md)
OpenAIllms.txtopenai/openai-agents-python/docs/llms-full.txt
openai.github.io/openai-agents-python/visualization/): Demonstrates embeddable visualizations for session timelines, message flows, and tool interactions. - [Testing](https://openai.github.io/openai-agents-python/testing/): Build deterministic, provider-neutral tests for Agent, Sandbox, Realtime, and Voice workflows. ## Coordination
OpenAIllms.txtopenai/openai-agents-python/docs/llms.txt
openai.github.io/openai-agents-python/context/): Control what past messages, attachments, and tool runs are injected into prompts. - [Testing](https://openai.github.io/openai-agents-python/testing/): Test Agent, Sandbox, Realtime, and Voice workflows deterministically without provider requests. ## Coordination
OthmanAdillms.txtOthmanAdi/planning-with-files/llms.txt
pass rate with the skill vs 6.7% without in the formal eval; a 1084-test suite guards the mechanisms. - Differentiators: an opt-in completion gate, multi-agent shared state
alirezarezvanillms.txtalirezarezvani/claude-skills/docs/llms-full.txt
repo deliberately avoids - Dependencies between skills (each stays self-contained) - Complex build systems or test frameworks (portability first) - Generic advice instead of specific frameworks - LLM calls in scripts (defeats portability
enescingozllms.txtenescingoz/awesome-n8n-templates/llms.txt
case (AI/LLM integration, email automation, social media, HR, DevOps, document processing). Review, configure, and test each workflow before use. Created by Enes Cingoz. ## What is this repository? Awesome n8n Templates
NirDiamantllms.txtNirDiamant/GenAI_Agents/llms.txt
Evaluation](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/trace_based_agent_evaluation.ipynb): Evaluate an agent from its execution traces. - [End-to-End Testing Agent](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/e2e_testing_agent.ipynb): An agent that writes and runs end-to-end tests. - [Human
Evil0ctalllms.txtEvil0ctal/Douyin_TikTok_Download_API/llms.txt
console, backed by an identity > pool (cookies + proxies + fingerprints) that mints, tests, cools and retires > its own identities. Open source (Apache-2.0), free, and it runs entirely on > your
emcie-collms.txtemcie-co/parlant/llms.txt
process exits, the context manager handles cleanup automatically. if __name__ == "__main__": asyncio.run(main()) ``` --- ## Testing Framework Parlant includes a testing framework for validating agent behavior using NLP-based assertions. ### Basic Test
tonhowtfllms.txttonhowtf/omniget/llms-full.txt
Code, Codex, Gemini CLI and Ollama Claude Code, run through OmniGet, fixes a failing test in about 15 seconds from one command. It asks permission with the diff on screen
tonhowtfllms.txttonhowtf/omniget/llms.txt
Ollama models, with per-folder permissions, undo, durable jobs, loops that repeat until a test command passes, an embedded MCP server with 49 tools, and an isometric house called
Googlellms.txtgoogle/osv-scanner/llms.txt
extractor in `osv-scalibr/plugin/list/list.go` so it can be enabled by name. * Add thorough tests within `osv-scalibr`. * Get the changes merged into `osv-scalibr`. 2. **Enable in `osv-scanner
AWSllms.txtaws/amazon-sagemaker-examples/llms.txt
process and local-container modes, inference pipelines, multi-model and multi-container endpoints, A/B testing, shadow variants, deployment guardrails, autoscaling with inference components, `optimize()`, batch transform, Bedrock deployment, Marketplace model
Vincentwei1021llms.txtVincentwei1021/video-shotcraft/gallery/llms.txt
gauge-readout-moves](https://vincentwei1021.github.io/video-shotcraft/library.html#gauge-readout-moves): Two instrument reads: a full-range needle self-test and a moving scale that races beneath a fixed pointer. - [hatch-depth](https://vincentwei1021.github.io/video-shotcraft/library.html#hatch-depth):
lastmile-aillms.txtlastmile-ai/mcp-agent/LLMS.txt
flight_mod_agent or lost_baggage_agent swarm = AnthropicSwarm(agent=triage_agent, context_variables={...}) test_input = "My bag was not delivered!" result = await swarm.generate_str(test_input) print("Result:", result
A markdown file at a website's root that gives language models a short guide to the site and links to the pages worth reading.
The same idea with the content included, so an agent can read the documentation in one request.
Agents and tools that fetch documentation on someone's behalf. It's a proposal, not a standard, and support varies.
Start with a one-line summary, then sections of links with a sentence each. The examples here show what real projects do.
