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 xobotyi/cc-foundry --skill humanizegit clone --depth 1 https://github.com/xobotyi/cc-foundryWrote 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/xobotyi/cc-foundry/humanize)<a href="https://agentmods.dev/skills/xobotyi/cc-foundry/humanize"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/humanize/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/xobotyi/cc-foundry/humanize"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/humanize.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 | $0.00044 | $0.02654 |
| Opus 5.5 | $0.00018 | $0.01062 |
| Sonnet 5.5 | $0.00009 | $0.00531 |
| Haiku 4.5 | $0.00004 | $0.00265 |
Grade A, and why
humanize 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Editing a good draft makes it worse. Rubric-guided model editing lifted below-median drafts on every measured dimension and pushed above-median drafts down: coherence −13.92, framing −12.06, both p<.001, across 45 abstract pairs and 869 keystroke-level edit logs. The first decision is never which tells to fix. It is whether this text should be rewritten at all.
Fix the writing, not the detection. Generated prose is worth fixing because it is vague, padded, and evasive. Detectors fire on post-training artifacts rather than on machine authorship, and they flag second-language and formulaic human writing at the same time. An edit whose purpose is a detector score is out of scope; every edit must improve the text for a reader.
Two failures carry equal weight. Under-editing leaves the tells and the text reads as generated. Over-editing flattens voice, deletes specifics, and produces a different slop — and it is the one the measurements above say is easy to cause.
Decide before editing
Read the whole text first. Nothing below is decidable from a search, because every one of these tells has a legitimate twin a few lines away.
Then judge the draft as a whole against three questions: does it hold one argument, do its paragraphs depend on each other, does it carry specifics that resist fabrication? Three clear yeses make a draft strong. A clear no to any one makes it weak. A question you cannot answer leaves it uncertain, and uncertain outranks weak: asking costs less than a rewrite that was never warranted.
The three questions are an inference, not a result. The measurement behind the gate split scientific abstracts by a rubric score, and nothing in it maps that split onto an article, a README, or a release note.
- Strong draft — review, do not rewrite. Mark the passages, name the mechanism behind each, propose the targeted edits, and leave everything unmarked untouched. Rewriting is the operation measured to cost coherence and framing here.
- Weak draft — rewrite. The same measurements show large gains on every dimension. Work the sweep below.
- Uncertain — mark, and ask. A draft you cannot place is a draft whose author knows something you do not.
Structural edits are what close the gap. In the same study, human editors changed almost nothing at any quality level, because they substituted and pruned locally while the global weakness survived. Reordering, merging, and cutting beat word swaps.
What ships with it
3 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.
- 1mo ago Changed · +1 lines · +2 tokens per session 6070208caba3
- 1mo ago First seen · 169 lines · 42 tokens per session scan A 3aeb290436c3
humanize is a skill published in the GitHub repository xobotyi/cc-foundry (22 stars, last pushed 7d ago), licensed MIT. It adds 44 tokens to every session and 2,654 once invoked, about $0.0002 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 2026-09-04.
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