Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oaustegard/claude-skills --skill verifying-claimsgit clone --depth 1 https://github.com/oaustegard/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oaustegard/claude-skills/verifying-claims)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/verifying-claims"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/verifying-claims/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oaustegard/claude-skills/verifying-claims"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/verifying-claims.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00147 | $0.01229 |
| Opus 5 | $0.00073 | $0.00615 |
| Sonnet 5 | $0.00029 | $0.00246 |
| Haiku 4.5 | $0.00015 | $0.00123 |
Grade A, and why
verifying-claims scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What changed (v0.1 → v0.2)
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.)
Division of labor — read this first
This skill does NOT gate merges and is NOT a test framework.
- The test suite (TDD/CI) owns the behavioral contract: deterministic, cheap, auditable, gated. A green check is something you can hold CI to.
- This skill owns the prose layer: does the documentation match reality? 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.
Procedure
- Identify the document(s) to check and the code + tests they describe.
- Gather consistent input: run
scripts/gather_context.py --doc DOC --src SRC --tests TESTS. It ast-parses source (no imports, no execution) and bundles the document text, the public API surface, and the test inventory. - Extract the claims the prose makes — every checkable assertion about the code (signatures, behavior, return shapes, defaults, guarantees, examples). Do this by reading; there are no claim markers.
- Judge each claim against the API surface and the tests:
- Does the code actually do what the prose says?
- Is the claim backed by a test, or merely asserted?
- Does it reference something that no longer exists?
- Report drift, ranked by severity, each finding citing the prose claim and the contradicting reality (file/function). Use the verdicts below.
- Optionally fix: rewrite the prose to match reality, and/or flag claims that need a test (an UNSUPPORTED claim is a missing test, not just a doc bug).
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 101 lines · 147 tokens per session scan A 2be6b85b8fe5
verifying-claims is a skill published in the GitHub repository oaustegard/claude-skills (148 stars, last pushed 2d ago), licensed MIT. It adds 147 tokens to every session and 1,229 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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