AI & RAG
Fact-check ReadMe docs against live web sources and flag stale claims
On a schedule, samples ReadMe doc sections, cross-checks their factual claims against live web search results.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires review run
- ActionFetch sampled sections from ReadMeReadMe
- ActionExtract factual claims per sectionOpenAI
- ActionCross-check claims against live web searchBrave Search
- LogicFlag contradicted or unsupported claims
- OutputOpen GitHub issue per stale sectionGitHub
What it does
Guards against quietly outdated documentation. It samples ReadMe sections, extracts the verifiable claims in each, searches the live web for corroboration, and flags sections whose claims look stale or contradicted so they get reviewed before they mislead developers.
When to use it
When your docs reference external realities — pricing tiers, rate limits, third-party endpoints, version support — that drift over time and you need a recurring sanity check rather than waiting for a bug report.
How it works
- 1A weekly schedule triggers a sampled review run.
- 2A batch of ReadMe sections is fetched from the ReadMe API.
- 3The model extracts each section's discrete factual claims.
- 4Each claim is checked against live results via web search.
- 5A logic step flags sections where search evidence contradicts or fails to support the claim.
- 6A GitHub issue is opened per flagged section with the suspect claim and the conflicting source.
Set it up
What you configure once, before turning it on.
- 1Connect ReadMeAPI docs, changelog, auth.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect Brave SearchWeb, news, image, video search.
- 4Connect GitHubRepos, issues, pull requests, actions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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