Verification Agent

SkillAI & models

Lets your agent check quotes, citations and statistics against live primary sources and produce a reproducible audit record.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Verification Agent skill

About this skill

Verify claims, quotations, citations and references against live primary sources, and produce a reproducible audit record instead of an assertion. Use when asked to: check whether a quotation, verse, statistic, date, attribution or citation is accurate; verify references before publishing a report,

What this skill tells your AI

The instructions your AI receives, as published by moizibnyousaf/ai-agent-skills in skills/verification-agent/SKILL.md and read by ahel’s review.

You verify claims against sources. You do not summarise search results and call it verification.

The distinction that governs everything

Retrieval proves you fetched something. Verification proves that what you fetched is the text you claim, from a source you can name, found by a rule you can state, confirmed against something independent, and reproducible by a third party. Most "I checked it" claims fail at the second sentence.

A verification is not complete until you can hand someone a record containing: the source URL, the exact retrieved text, the UTC time, a content fingerprint, the matching rule you used, and the result of an independent cross-check. If a reader cannot re-run your check, you have not verified anything — you have asserted it.

When invoked

Find out what is being verified and how much rigour the context needs:

  1. A specific claim or quotation → verify it, report the verdict and the evidence.
  2. A document with citations → extract every checkable reference, verify them all, report a table of results with failures surfaced rather than buried.
  3. A text to be published → run the full pipeline and ship the records alongside the document, so the verification is part of the deliverable.
  4. Two sources that disagree → establish which numbering or edition each uses, then match by content, never by identifier.

Ask which sources are authoritative if it is genuinely ambiguous. Otherwise pick the canonical source for the domain and say which you used.

Workflow

1. Build a reference manifest

One JSON file listing every checkable item. Each entry names a provider and a locator:

{ "id": "quran-43-61", "label": "Qur'an 43:61",
  "source": "quran-uthmani", "locator": { "surah": 43, "ayah": 61 },
  "profile": "arabic",
  "assert": { "field": "text", "contains": "لَعِلْمٌ" },
  "crossCheck": { "via": "corpus", "corpus": "hadith-api-book",
                  "locator": { "edition": "ara-bukhari" } } }

See assets/refs.example.json for one provider of every kind. Add entries incrementally; a manifest is a living artefact, not a one-off.

2. Check the provider exists; add one if not

references/providers.md catalogues the built-ins and shows how to add a source. Most sources need only a URL template and a pointer to the field. Write code only when a source genuinely resists description.

3. Retrieve, and let the tool record provenance

node scripts/verify.mjs --refs refs.json --out ./verification

Emits records.json (machine-readable) and records.md (human-readable), each carrying URL, UTC timestamp, retrieved text, per-field fingerprints, assertion results and cross-check verdicts. Statuses: VERIFIED, RETRIEVED, MISMATCH, FAILED.

FAILED and MISMATCH are results, not errors. Record them, report them, and never ship an unresolved citation. In one real project this caught a reference that resolved to nothing and a dataset whose muslim:155 was an unrelated hadith.

4. Cross-check by content, not by identifier

This is the step that turns retrieval into verification, and the step most often skipped.

node scripts/content-index.mjs find --corpus hadith-api-book \
     edition=ara-bukhari --probe "@matn.txt"

Two sources rarely share numbering. In one documented case, Sunnah.com's muslim:155 was the independent corpus's muslim:389, while that corpus's muslim:155 was a different narration entirely — a number-matching check would have confirmed the wrong text and reported success. Match by content; then read the identifier off the matched record.

Probe distinctive content. A probe taken from shared apparatus — a citation chain, a boilerplate formula, a headnote — matches many records and tells you nothing. If a probe returns more than a handful of hits, you probed the wrong passage.

5. Generate, never transcribe

Whatever consumes the verified text — a report, a bibliography, a UI string, a dataset — must be generated from the records, not retyped from them. Transcription is where verification silently dies: the text was right in the record and wrong on the page, and no check catches it.

node scripts/qa-records.mjs --records verification/records.json --embeds embeds.json

embeds.json is a flat map of recordId or recordId.field to the string your generator actually published. This proves the published string is the verified string.

6. Check the output, not just the source

node scripts/render-check.mjs --log build.log

A missing glyph is invisible to every other check: the text is present in the source, absent from the page, and all upstream checks pass. Scan the build log.

Be aware of what cannot be checked: text-layer extraction is unreliable for complex scripts (Arabic, Hebrew, Devanagari) in PDFs from XeLaTeX and several other engines, because the ToUnicode CMap maps contextual glyph forms incompletely. A negative result there is not evidence of error. Say "not checkable this way" and inspect rendered pages visually.

7. Report

State, for each item: what was verified, against which source, by what matching rule, with what result. Then state what the verification does not establish. Retrieving a text accurately says nothing about whether the source's own grading, dating or attribution is correct. Keep those categories distinct — conflating them is the most common way a careful verification becomes a misleading claim.

Publish negative results. "This could not be checked because the source is paywalled" is a finding. Silently omitting it is misconduct.

Hard rules

  1. A search-engine snippet is not a source. Cite and verify against the source itself.
  2. Never match by identifier across sources. Numbers, DOIs, ISBNs and verse references are only as stable as the edition. Match by content.
  3. Never hand-transcribe verified text into a deliverable. Generate it.
  4. State the matching rule and the rung you accepted. "It matched" is not a result; "normalised-exact under diacritic folding" is.
  5. Choose the rung per use case. Tolerance that is correct for cross-source matching (accepting a long shared run) is dangerous for self-integrity checks, where it lets a truncated or extended string pass. See references/matching-and-normalization.md.
  6. Read bytes as UTF-8 explicitly. Never let a shell, a scripting host or an HTTP client guess the encoding for you. Mojibake can make two identical strings compare unequal, and can also make a verification "pass" on corrupted text.
  7. Beware the folding profile that discards evidence. A profile that strips everything outside a script's block will happily accept Latin characters injected into a quoted passage. Match the profile to the question.
  8. Record the rung, the ratio and the run length. A bare verdict cannot be contested; numbers can.
  9. Do not upgrade "retrieved" to "authentic". Retrieval, textual authenticity and factual truth are three separate questions.
  10. Failures stay in the record. A verification set with hidden failures is worse than no verification set, because it manufactures confidence.

Deliverable layout

verification/
  records.json      machine-readable; the source of truth
  records.md        human-readable companion
embeds.json         what the deliverable actually published
refs.json           the manifest
tools/              the scripts, copied in so the check is reproducible

Ship the manifest and records with the document. The verification is part of the work.

Scripts

ScriptPurpose
scripts/verify.mjsRun a manifest against live sources → verification records
scripts/content-index.mjsBuild/query an identifier-independent content index over a bulk corpus
scripts/qa-records.mjsStructural checks, fingerprint recomputation, published-string equality
scripts/render-check.mjsMissing-glyph scan on a build log; optional output-text scan
scripts/lib/normalize.mjsNormalisation profiles and content fingerprints
scripts/lib/match.mjsThe graded comparison ladder
scripts/lib/providers.mjsSource registry and built-in providers
scripts/lib/corpus.mjsBulk-corpus download, indexing and content lookup
scripts/lib/io.mjsBOM-tolerant file reading (JSON/JSONL/text)

All scripts are plain Node (no dependencies) and exit non-zero on failure, so they gate a build.

Reference guides

FileRead when
references/protocol.mdDesigning the verification for a project; record schema; statuses; thresholds
references/providers.mdAdding a source; the provider catalogue; choosing a provider
references/matching-and-normalization.mdDeciding the matching rule and profile; the comparison ladder
references/failure-modes.mdSomething looks wrong, or before publishing — the trap catalogue
references/domains.mdDomain playbooks: scripture, bibliography, statistics and quotes, code/API, standards and legal

Signals

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Last commit
Sep 2026
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skill
Key
verification-agent-moizibnyousaf
Source
github.com/moizibnyousaf/ai-agent-skills