u/RunAwayUNerd

hello

The basic idea comes from the fact that more people (including me) use Codex, Claude Code, Cursor, Copilot, MCP tools, etc., but they are still written only for humans. Agents might fail and struggle to use what you build because setup commands are unclear, docs are stale, OpenAPI operations are under-described, MCP tools are ambiguous, or there is no AGENTS.md/CLAUDE.md/llms.txt/benchmark

So my project, AgentLighthouse, tries to to answer "Can an AI coding agent understand and use this project correctly?"

It scans for things like:

• agent instruction files
• README/docs quality
• setup/test/lint command clarity
• OpenAPI operation quality
• MCP tool descriptions/input schemas
• task benchmarks
• SARIF/CI readiness
• baseline comparison and PR regressions

It is local-first and does not call any paid LLM API. It is not an AI agent nor an SaaS. Please don't flame me as I'm making no profit out of this 😄. The goal is to make projects easier for existing agents to use.

Try it:
npx @agentlighthouse/cli scan .

Or generate reports:

npx @agentlighthouse/cli@alpha scan . --report-dir agentlighthouse-reports

This is very much an alpha still, I’m mainly looking for feedback from real devs. Thanks for reading :)

hello

The basic idea comes from the fact that more people (including me) use Codex, Claude Code, Cursor, Copilot, MCP tools, etc., but they are still written only for humans. Agents might fail and struggle to use what you build because setup commands are unclear, docs are stale, OpenAPI operations are under-described, MCP tools are ambiguous, or there is no AGENTS.md/CLAUDE.md/llms.txt/benchmark

So my project, AgentLighthouse, tries to to answer "Can an AI coding agent understand and use this project correctly?"

It scans for things like:

• agent instruction files
• README/docs quality
• setup/test/lint command clarity
• OpenAPI operation quality
• MCP tool descriptions/input schemas
• task benchmarks
• SARIF/CI readiness
• baseline comparison and PR regressions

It is local-first and does not call any paid LLM API. It is not an AI agent nor an SaaS. Please don't flame me as I'm making no profit out of this 😄. The goal is to make projects easier for existing agents to use.

Try it:
npx @agentlighthouse/cli scan .

Or generate reports:

npx @agentlighthouse/cli@alpha scan . --report-dir agentlighthouse-reports

This is very much an alpha still, I’m mainly looking for feedback from real devs. Thanks for reading :)

hello

The basic idea comes from the fact that more people (including me) use Codex, Claude Code, Cursor, Copilot, MCP tools, etc., but they are still written only for humans. Agents might fail and struggle to use what you build because setup commands are unclear, docs are stale, OpenAPI operations are under-described, MCP tools are ambiguous, or there is no AGENTS.md/CLAUDE.md/llms.txt/benchmark

So my project, AgentLighthouse, tries to to answer "Can an AI coding agent understand and use this project correctly?"

It scans for things like:

• agent instruction files
• README/docs quality
• setup/test/lint command clarity
• OpenAPI operation quality
• MCP tool descriptions/input schemas
• task benchmarks
• SARIF/CI readiness
• baseline comparison and PR regressions

It is local-first and does not call any paid LLM API. It is not an AI agent nor an SaaS. Please don't flame me as I'm making no profit out of this 😄. The goal is to make projects easier for existing agents to use.

Try it:
npx @agentlighthouse/cli scan .

Or generate reports:

npx @agentlighthouse/cli@alpha scan . --report-dir agentlighthouse-reports

This is very much an alpha still, I’m mainly looking for feedback from real devs. Thanks for reading :)