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 kcdjmaxx/HomarUScc --skill verifygit clone --depth 1 https://github.com/kcdjmaxx/HomarUSccWrote 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/kcdjmaxx/homaruscc/verify)<a href="https://agentmods.dev/skills/kcdjmaxx/homaruscc/verify"><img src="https://agentmods.dev/badge/skills/kcdjmaxx/homaruscc/verify/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/kcdjmaxx/homaruscc/verify"><img src="https://agentmods.dev/badge/skills/kcdjmaxx/homaruscc/verify.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 | $0.00083 | $0.01571 |
| Opus 5.5 | $0.00033 | $0.00628 |
| Sonnet 5.5 | $0.00017 | $0.00314 |
| Haiku 4.5 | $0.00008 | $0.00157 |
Grade A, and why
verify 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 1mo 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify
Automated fact-checking loop that verifies claims in a document against source data. Produces an errata report listing what's correct, incorrect, and unverifiable. Optionally applies corrections.
Usage
/verify -- verify target document using ./verify.md config
/verify --report-only -- produce errata report without applying corrections
/verify --apply -- apply corrections from existing errata
/verify <path> -- verify a specific file (looks for verify.md in same dir)
Prerequisites
The current working directory (or the target file's directory) must contain a verify.md file with configuration.
verify.md Format
target: RESULTS.md # Document to fact-check
sources: # Source(s) of truth — prose and/or structured data
- experiment-log.md # Prose source
- .autoresearch/results-data.json # Structured data source (authoritative for numbers)
- eval.py # Code source (methodology verification)
errata: ERRATA.md # Where to write the errata report
claim_types: # What kinds of claims to check
- numbers # Specific numbers, percentages, counts
- names # Names, labels, identifiers
- methodology # Process descriptions, how things were done
- conclusions # Logical conclusions drawn from data
auto_apply: false # If true, apply corrections without asking
Source Priority
When a claim can be checked against multiple sources, use this priority:
- Structured data (JSON) — authoritative for numerical claims. If results-data.json says consistency=0.8 and experiment-log.md says 1.0, the JSON wins.
- Code — authoritative for methodology claims. If the document says "5 runs per experiment" and eval.py shows RUNS=5, the code wins.
- Prose sources — for narrative context, conclusions, and interpretations.
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.
- 1mo ago First seen · 169 lines · 83 tokens per session scan A ee18a2492863
verify is a skill published in the GitHub repository kcdjmaxx/HomarUScc (1 stars, last pushed 5mo ago), licensed MIT. It adds 83 tokens to every session and 1,571 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
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