Vahtian

For LLMs & agents

Do great research-support work with an LLM — without letting it decide.

Point your AI assistant (Claude, or any agent) at Vahtian's open tools and it can search the literature reproducibly, screen it blinded, reconcile reviewers, retrieve full text, and verify a claim before it is cited — every step provenance-stamped and auditable. The human stays the decider; the AI is a fully-identified, advisory second rater.

Get the skill Read llms.txt

The pipeline an agent can run

Each stage is its own small, open tool. The agent orchestrates; the researcher gates the irreducible human acts (judgement, sign-off).

  1. Capture — tap the sentences worth citing in an abstract → reviewable Zotero RIS. MatchVahti-Lite · live
  2. Search — open multi-source query (PubMed/MEDLINE, Europe PMC, Semantic Scholar, OpenAlex) + citation chasing → one frozen, deduped corpus. vahtian_search.py · open tool
  3. Screen, blinded — rate each paper against each claim; the AI is sealed until you commit. MatchVahti · the screening engine
  4. Reconcile — load each reviewer's ballot → per-claim Cohen's κ, PABAK, AC1, Krippendorff's α. ReviewVahti · live
  5. Retrieve — fetch the open-access full text for flagged papers. FullVahti · Zotero plugin / script
  6. Extract + appraise — pull 2×2 / sensitivity / directional reclassification and score risk of bias (QUADAS-2/-C, PROBAST-AI) from the full texts; dual-human, reconciled → tidy CSV for meta-analysis in R. ExtractVahti · roadmap
  7. Verify — check each claim against its source before citing; decision-gated, undoable Zotero write-back; hash-chained audit. CiteVahti · live

The open tool you can run right now

vahtian_search.py turns one question into one reproducible, deduped, provenance-stamped corpus — open APIs only, so anyone can re-run it. Standard library, no install.

python3 tools/vahtian_search.py outdir/
# → outdir/frozen-corpus.jsonl   (deduped, per-source provenance, search-date + content hash)
# → outdir/search-report.md      (per-source: retrieved · relevant · net-new)

Login-gated databases (Embase, Web of Science, Scopus) are excluded by design: a search behind a subscription can't be re-run by your readers. Open APIs + citation chasing keep the whole search reproducible.

Invariants an agent must honour

These are what make the output trustworthy. A tool — or an agent — that breaks them isn't doing Vahtian's job.

The human decides.The AI is consulted only after the human commits a rating — never anchoring, never setting the recorded value.
No silent writes.References enter a library only as an audited, undoable preview → confirm step. Dedupe fails closed.
AI is a fully-identified, separate tier.Every AI rating is labelled with model id + version + prompt version. It never counts as an independent human assessor or fills a consensus floor. Transparency, not anonymity.
Open, reproducible search.Open APIs + citation chasing only. The search date is recorded; currency is bounded by it.
Honest about scope.An abstract sentence is a lead, not evidence. The system documents the workflow — it does not assert scientific truth.

Why a skill, not a chatbot on the page

Vahtian pages never reach out and never embed an AI. The intelligence lives in your agent. The Vahtian research-support skill teaches that agent the pipeline above and the invariants here, so it does the heavy lifting while you keep the decisions — and so the work is reproducible by anyone who runs the same open tools.