Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Syedomershah99/being-humannpx agentmods add commands/syedomershah99/being-human/verifyWrote 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/commands/syedomershah99/being-human/verify)<a href="https://agentmods.dev/commands/syedomershah99/being-human/verify"><img src="https://agentmods.dev/badge/commands/syedomershah99/being-human/verify.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.00408 |
| Opus 5 | $0.00008 | $0.00204 |
| Sonnet 5 | $0.00003 | $0.00082 |
| Haiku 4.5 | $0.00002 | $0.00041 |
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 7d 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.
What it actually says
Run the impostor test on: $ARGUMENTS
python3 scripts/verify.py "$ARGUMENTS" --in .being-human/
If they pasted text rather than a path, write it to a scratch file and test that.
This asks a different question from /being-human:check. That one looks for AI
tells. This one asks whether the text is a plausible sample of this person,
using authorship distance against a null resampled from their own writing at the
same length.
Reading the result:
- under 75th percentile — indistinguishable from their writing
- 75–90 — within range
- 90–97 — unusual for them
- over 97 — reads as a different hand
The percentile is what matters, not the raw delta, which has no absolute scale. A 60th percentile means the draft is more typical of them than 40% of what they actually wrote — that's a pass, not a near-miss.
The what pushed it out list is the actionable part. It names the exact function
words that are over- or under-used against their baseline, with the rates. Fixing
two or three of those usually moves the percentile more than any amount of
rewriting by feel.
Two things to be straight about:
Both checks are needed. A draft can clear this one and still be full of tells,
because authorship distance keys on function-word distribution and generic prose
often has unremarkable grammar. Run /being-human:check too.
And below about 40 words the number is noise — there isn't enough text for word frequencies to mean anything. Say so rather than reporting a percentile as if it were solid.
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.
- 7d ago First seen · 45 lines · 16 tokens per session scan A 4f77211f0156
verify is a command published in the GitHub repository Syedomershah99/being-human (0 stars, last pushed 24d ago), licensed MIT. It adds 16 tokens to every session and 408 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.