Install
openclaw skills install @dexio/dexio-wiki-verifyFact-check an LLM wiki against its own sources: pick the pages where an error would do the most harm, pull out the checkable claims, open each cited source and mark every claim supported, outdated, unsupported, unsourced or unreachable, then correct the page and every page that copied the claim. Use on a schedule, before a decision relies on a page, after a large ingest, or when a page's claims are in doubt.
openclaw skills install @dexio/dexio-wiki-verifyThe known weakness of an LLM wiki is that a wrong claim gets written in once and then cited: it stops looking like model output and becomes a line other pages build on. Linting finds broken structure. Verifying finds wrong content, by going back to the sources.
Verification is sampling, not a full audit. A run checks a handful of pages well rather than the whole wiki badly.
Folder wiki: python3 <wiki-lint skill>/scripts/wiki_lint.py <wiki> --verify-queue 10 ranks
pages by how many pages link to them, how long since anyone checked them, and whether they
cite nothing, are contested, have low confidence, or record a decision. Hosted wiki: rank the
same way from the page list and each page's links in.
Also check, whatever the ranking says:
Default run: three to five pages, or about twenty claims.
wiki-conflicts, superseded).wiki-conflicts, corrected).(unverified) and lower confidence, adding the
field if the page has none (medium for one weak source, low for none).(unverified).(vendor-sourced) if the
label is missing.verified: YYYY-MM-DD on each page whose checkable claims you
all checked that day. It means "checked, and the labels on this page show the result",
so a page can carry it with claims still labelled (unverified). Do not set it on a page
you only sampled or only fixed a copy on. Leave a change note listing what you
corrected: the commit message in git, the note field on a hosted wiki. A plain folder
has no history, so there the dated correction lines on the page are the record.A model checking its own synthesis tends to agree with it. Where you can, give each claim and its source, without the rest of the page, to a separate agent or a different model and ask only "does this source support this claim?". Disagreements between the two are the claims to look at closely. If you cannot, the check still counts, but say in the report that every classification is one model's judgment.
verified after spot-checking two claims.