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 Infrasity-Labs/dev-gtm-claude-skills --skill blog-factcheckgit clone --depth 1 https://github.com/Infrasity-Labs/dev-gtm-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/infrasity-labs/dev-gtm-claude-skills/blog-factcheck)<a href="https://agentmods.dev/skills/infrasity-labs/dev-gtm-claude-skills/blog-factcheck"><img src="https://agentmods.dev/badge/skills/infrasity-labs/dev-gtm-claude-skills/blog-factcheck/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/infrasity-labs/dev-gtm-claude-skills/blog-factcheck"><img src="https://agentmods.dev/badge/skills/infrasity-labs/dev-gtm-claude-skills/blog-factcheck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00118 | $0.01422 |
| Opus 5 | $0.00059 | $0.00711 |
| Sonnet 5 | $0.00024 | $0.00284 |
| Haiku 4.5 | $0.00012 | $0.00142 |
Grade A, and why
blog-factcheck 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 9d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blog Fact-Check
Verify statistics, claims, and source attributions in blog posts. Pure Claude pipeline with no external NLP dependencies.
Workflow
Step 1: Read the Blog Post
Read the target file and identify all sections containing data claims.
Step 2: Extract Statistical Claims
Scan the full text for every claim that includes a number, percentage, dollar amount, or named source. Build a claims list with these fields:
| Field | Description |
|---|---|
| claim_text | The exact sentence or phrase containing the statistic |
| value | The numeric value (e.g., "42%", "$1.2M", "3x") |
| attribution | Named source if present (e.g., "HubSpot", "Gartner 2025") |
| url | Cited URL if present (from markdown link or parenthetical) |
| location | Heading or line number where the claim appears |
Step 3: Verify Cited Claims
For each claim that includes a URL:
- Fetch the source page via WebFetch
- Search the returned content for the specific numeric value
- If exact value found, check surrounding context matches the claim topic
- Assign a confidence score (see Verification Scoring below)
Process claims sequentially to avoid rate-limiting source sites.
Step 4: Flag Uncited Claims
For claims without a URL:
- Mark status as UNVERIFIED
- Suggest a search query the user can run to find a source
- If the attribution names a specific organization, suggest their domain
Step 5: Generate Verification Report
Output the full results table, summary statistics, and recommended actions.
Claim Extraction Patterns
Identify claims matching these structures:
Fully cited (highest priority):
[Number]% [claim] ([Source], [Year])- parenthetical citation[claim] [Number]% ... [markdown link to source]- inline linkAccording to [Source], [Number]...- attribution lead
Uncited statistics (flag for sourcing):
[Number]% of [noun phrase]- standalone percentage[Number]x more/less/higher/lower- multiplier claims$[Number] [claim]- dollar figures without attribution
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.
- 9d ago First seen · 140 lines · 118 tokens per session scan A 53a4f70977d1
blog-factcheck is a skill published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 118 tokens to every session and 1,422 once invoked, about $0.0006 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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