Check that a document's claims about code are actually true by reading the prose, the code, and the tests and reporting (or fixing) where they disagree. Use whenever the user wants to verify a README, guide, spec, or docstring still matches the code; whenever they mention documentation drift, doc-code sync, "is this still accurate", stale docs, or keeping docs/tests/code consistent; before publishing or merging a docs change; or as a periodic doc-accuracy sweep. The agent reads the prose's meaning directly — there is no claim-comment DSL to maintain. Pairs with TDD — the test suite is the deterministic behavioral gate, this skill is the semantic prose-vs-reality review.
npx skills add https://github.com/oaustegard/claude-skills --skill verifying-claims
Check that what a document *says* about code is true, by reading the document,
the code, and the tests together and reporting where they disagree.
v0.1 was a comment-DSL: you hand-wrote <!-- claim: ... --> next to prose and
a script checked the *comment* against the code. That had a fatal gap — the
comment and the prose were two artifacts stapled together, and only the comment
was checked, while humans read the prose. The prose could lie with a green run.
v0.2 drops the DSL. The reviewer is the agent: it reads the prose's *meaning*
directly and compares it to what the code does and what the tests assert. No
shadow copy, because the thing being checked is the thing the human reads.
(Existing tools already own the alternatives — Gherkin binds executable
scenarios, Lean's Verso transcludes facts into prose, TDD couples code to
tests. This fills the remaining slot: free-prose documentation, judged.)
This skill does NOT gate merges and is NOT a test framework.
cheap, auditable, gated. A green check is something you can hold CI to.
That needs semantic judgment across artifacts, which is non-deterministic and
fallible — so it runs as a *triggered review* (before docs ship, on request,
as a sweep), not as a per-commit gate. "The agent said the docs match" is not
a guarantee you gate a merge on; it's a review you act on.
Tests are the anchor. The docs are correct when they agree with what the tests
assert about the code. So write/keep good tests first; this skill keeps the
prose pinned to them.
SRC --tests TESTS`. It ast-parses source (no imports, no execution) and
bundles the document text, the public API surface, and the test inventory.
code (signatures, behavior, return shapes, defaults, guarantees, examples).
Do this by reading; there are no claim markers.
the contradicting reality (file/function). Use the verdicts below.
that need a test (an UNSUPPORTED claim is a missing test, not just a doc bug).
regressed and the doc caught it).
nothing protects it from future drift. Surface as a missing test.
docs/api.md still match pkg/?"Run it at moments that matter — pre-publish, post-refactor, on a docs PR — not
on every commit. The deterministic gate is the test suite; this is the layer
tests can't reach.
output as a careful review, not a proof.
test run. Don't wire it where a cheap deterministic check belongs.
are correct. Garbage tests → confident-but-wrong PASS. TDD discipline upstream
still matters.
scripts/gather_context.py — deterministic input bundler (doc + API surface +test inventory), ast-only, no imports.
references/drift-report-example.md — what a review report looks like.Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take oaustegard/verifying-claims from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.