Claim: Rich can render flexible tables with unicode box characters.
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Claim: MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines.
Claim: Unlike most agent-skill repos, this one ships a real, zero-dependency detection engine alongside the prompt, with a genuine regression-tested suite (150+ tests, many tied to real GitHub issue numbers) and a self-published PROOF.md that reports its own false-positive research — including a paragraph-level ROC-AUC of 0.501, a coin flip, disclosed without being asked.
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Claim: Claims it never fabricates skills. Held up under real pressure: visa mechanics, ATS billing metadata, country-specific labor law, not just a skills-gap test.
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Claim: Claims "proof, not promises." Fed it a novel scenario and it flagged an unresolved design detail instead of inventing an answer. The installer is pushy though.
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Claim: Claims to be an open-source SEO audit tool. We pointed it at our own site and it caught a real broken link we had missed, with zero false critical alarms.
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Claim: Claims "0% detection rates" for undetected browser automation. Three commands to start; HTML and Markdown extraction both worked. The v2 docs are out of date.
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Claim: Claims fast, high-accuracy PDF to Markdown. Runs locally with no API key and no CUDA. Headers, body text, and tables all extracted correctly on the first run.
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Claim: Claims "100% private local processing." Local mode needs CUDA, so no Apple Silicon. Cloud mode wants Google auth and hit rate limits on a fresh account.
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Claim: Claims you can tell your computer what to do and it gets done. True with a capable cloud model like gpt-4o-mini. Every local model we tried failed.
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Claim: Claims one docker compose up brings up n8n, Qdrant, and Postgres. All three start, the sample agent workflow runs locally, but the demo defaults to OpenAI.
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Claim: Claims self-hosting is one npx n8n command, no Docker, no keys. Confirmed, and the native Ollama node connects to local models out of the box.
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Claim: Claims clean main-content extraction from any web page in three lines of Python, no API key or server. Install was clean and the markdown output was LLM-ready.
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Claim: Claims a local chat UI in one Docker command, no API key, no database. ollama was auto-detected, llama3.2 connected, and one model-tag quirk needed a flag.
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Claim: Claims "high performance, fast to code, ready for production." Auto docs, validation, and hot reload all confirmed in under 10 minutes.
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Claim: Claims you can extract any web data with a plain-English prompt. Works on static pages, no API key. JS-heavy pages are unreliable; needs a 4.9GB local model.
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Claim: Claims multi-agent orchestration with no API key. A two-agent crew ran locally and handed off context. Snags: CLI not on PATH, default model mismatch.
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Claim: Claims LLM-ready markdown from any web page. First pass was PARTIAL; the fault turned out to be a deprecated API path on our end, not the tool. Now SOLID.
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Claim: Claims a working RAG pipeline in about five lines of Python. Verified locally on in-memory docs and real files, accurate answers, no external services needed.
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Claim: Claims you can run any LLM locally with one command. Confirmed, and the REST API works immediately. Cold start can hit 8+ seconds; quality varies by model.
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Claim: Claims a 4-function vector database, fully local, with automatic embedding. Verified in-memory and persistent. First run silently downloads a 79MB model.
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Claim: Claims one interface across 100+ LLM providers. The same code hit Anthropic and OpenAI first try. Gotcha: the response is a Pydantic model, so .keys() throws.
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Claim: Claims a production-grade Claude Code workflow. We ran it end to end on this very site, plan through ship. It held up, with one non-blocking hook error.
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Claim: Claims a big agent harness changes how Claude Code works. Across three configs, the full install measurably shifted output quality; the plugin alone did not.
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Claim: Claims one CLAUDE.md file stops Claude Code from making silent assumptions. Tested and verified, and the install works exactly as documented.
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