nirholasllms.txtthree.ws
nirholas/three.ws/public/llms.txt
# three.ws > three.ws is the AI-agent layer for the open web: build, embed, monetize
llms.txt examples
How real projects summarise themselves for language models.
Best matches · from page 7Worked for most · soon
nirholasllms.txt
mihneaptullms.txtopencode-fusion / site
mihneaptu/opencode-fusion/site/llms.txt
them. Skills are discovered on demand; installing one needs no restart. 5. Cross-vendor review is emergent: pick main and sidekick from different model families. 6. Only the permission layer
joshuaswarrenllms.txtremnic
joshuaswarren/remnic/llms.txt
deepSleep off by default. - Coding graph: code-symbol knowledge graph for coding agents. Verify any default in packages/remnic-core/src/config.ts or the openclaw.plugin.json configSchema before relying on it. ACCESS LAYER HTTP
garagonllms.txtnanostack
garagon/nanostack/llms.txt
work. Not a saved phase; the artifact appears on review. - /review: Two-pass code review (structural + adversarial). Detects scope drift against the plan and conflict-precedence against prior /security. - /security
KbWenllms.txtagentic-os
KbWen/agentic-os/llms.txt
agents. It gives agents — Claude Code, OpenAI Codex, Cursor, GitHub Copilot, Google Antigravity, and any Markdown-reading LLM agent — a repeatable plan → build → review → test → ship workflow with enforced quality
jenticllms.txtjentic-one
jentic/jentic-one/llms.txt
only the operations it has been approved for, and asking for more is a reviewable request rather than a silent widening. - Leave a trail. Every brokered call is recorded
nikolai-vysotskyillms.txttrace-mcp
nikolai-vysotskyi/trace-mcp/docs/llms-full.txt
requires external binaries and complex backend choices. [trace-mcp vs CodeGraphContext](/vs/codegraphcontext.html) | | **Review graph** | [code-review-graph](https://github.com/code-review-graph/code-review-graph) | Tracks incremental changes with empty-result uncertainty explanations. | Specialised
nikolai-vysotskyillms.txttrace-mcp
nikolai-vysotskyi/trace-mcp/docs/llms.txt
calls return vs making structural questions about code cheap to ask. - [trace-mcp vs code-review-graph](https://trace-mcp.com/vs/code-review-graph.html): Head-to-head against the incremental SQLite graph peer — blast
dylanroscoverllms.txtEmbody / web
dylanroscover/Embody/web/llms-full.txt
bloom on the final output. ``` You're not getting a screenshot or a code snippet to paste. You're getting the **actual network**, in front of you, ready to play
softsparkllms.txtai-toolkit
softspark/ai-toolkit/llms-full.txt
diffs for regression risk and blast radius, generates risk-scored impact report. Triggers: PR review, code change risk, breaking change, blast radius, regression check. - **prepare-test-env**: Prepare or verify
willynikes2llms.txtknowledge-base-server
willynikes2/knowledge-base-server/llms.txt
src/safety/review.js | Destructive action review with multi-model consensus. | | src/promotion/promoter.js | Knowledge promotion from sources to structured notes. | | src/synthesis/weekly-review.js | Weekly cross-source synthesis generator. | ## Integration Patterns ### Claude Code (local, stdio) Register with
KyaniteLabsllms.txtkinocut
KyaniteLabs/kinocut/llms.txt
/KyaniteLabs/kinocut/blob/master/docs/360_ASSEMBLY.md - AI-video review (dev tip): https://github.com/KyaniteLabs/kinocut/blob/master/docs/AI_VIDEO_REVIEW_AND_SALVAGE.md - FAQ: https://github.com/KyaniteLabs/kinocut/blob/master/docs/faq.md - Agent skill: https://github.com/KyaniteLabs/kinocut/blob/master/skills/kinocut/SKILL.md - Changelog: https://github.com/KyaniteLabs/kinocut/blob/master/CHANGELOG.md ## Use cases - Claude Code / Cursor / Codex-style
tubecreatellms.txttubecli
tubecreate/tubecli/docs/llms.txt
with no API key. Representative operations, all executed by agents rather than suggested as code: describe a site in plain language and the website agent runs `git clone` → `npm install
PsiACEllms.txtskills / skills
PsiACE/skills/skills/llms.txt
Reuse and composition](https://raw.githubusercontent.com/psiace/skills/main/skills/friendly-python/references/reuse-composition.md): Composition-oriented design. - [Review checklist](https://raw.githubusercontent.com/psiace/skills/main/skills/friendly-python/references/review-checklist.md): Code review focus areas. ## Piglet references (Markdown) - [Variables and naming](https://raw.githubusercontent.com/psiace/skills/main/skills/piglet/references/variables-and-naming.md): Naming, scope
alfadur7llms.txtllm-wiki-newsroom
alfadur7/llm-wiki-newsroom/docs/llms.txt
Claude Code that turns a folder of documents into a cross-linked, human-readable markdown wiki. The agent that writes a page is never the one that reviews
ihuzaifashoukatllms.txtx-use
ihuzaifashoukat/x-use/llms.txt
Agent Skills support. - Draft mode is on by default. Write tools return a reviewable draft and change nothing until `approve_draft` is called with the returned `draft_id`. - Interactive
paolobietolinillms.txtgtm-mcp-server
paolobietolini/gtm-mcp-server/llms.txt
template details including .tpl code | | `create_template` | Create a custom template from .tpl code | | `update_template` | Modify an existing template | | `delete_template` | Remove a template (requires `confirm: true`) | | `import_gallery
alexei-ledllms.txtspotinfo
alexei-led/spotinfo/llms.txt
docs/claude-desktop-setup.md) - [CLI and MCP surface review](docs/reviews/cli-and-mcp-surface-review.md) - [Surface validation](docs/reviews/surface-validation.md) - the built binary across the command x cloud x format matrix - [CLAUDE.md](CLAUDE.md) - AI coding guidelines for this project ## Commands
oliver-zehentleitnerllms.txtkeep-the-why
oliver-zehentleitner/keep-the-why/llms.txt
framework already governs how the work gets done (brainstorming, planning, systematic debugging, TDD, code review), that workflow runs first; Keep the Why doesn't compete for that role, and preserves
GuyMannDudellms.txtmnemo-cortex
GuyMannDude/mnemo-cortex/llms.txt
facade, and ships drop-in integrations for the major MCP hosts (Claude Desktop, Claude Code, OpenClaw, LM Studio, AnythingLLM, Agent Zero, Hermes Agent, Open WebUI, llama.cpp, LobeChat, Jan). v3 (Bridge
About llms.txt
What is llms.txt?
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.
What's llms-full.txt?
The same idea with the content included, so an agent can read the documentation in one request.
Who reads it?
Agents and tools that fetch documentation on someone's behalf. It's a proposal, not a standard, and support varies.
How do I write one?
Start with a one-line summary, then sections of links with a sentence each. The examples here show what real projects do.