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 AcKeskin/contexture --skill humanizegit clone --depth 1 https://github.com/AcKeskin/contextureWrote 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/ackeskin/contexture/humanize)<a href="https://agentmods.dev/skills/ackeskin/contexture/humanize"><img src="https://agentmods.dev/badge/skills/ackeskin/contexture/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/ackeskin/contexture/humanize"><img src="https://agentmods.dev/badge/skills/ackeskin/contexture/humanize.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 | $0.00073 | $0.01498 |
| Opus 5.5 | $0.00029 | $0.00599 |
| Sonnet 5.5 | $0.00015 | $0.00300 |
| Haiku 4.5 | $0.00007 | $0.00150 |
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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize
Overview
Make user-facing prose read like a person wrote it, without changing what it says. Grounded in a verified research base (Wikipedia AI-Cleanup + peer-reviewed detection literature). The catalogue is the source of truth; this skill applies it.
Core principles (all load-bearing):
- Signs, not proof. Scorer, not oracle. Every marker also appears in genuine human prose (LLMs trained on it). Report advisory density/likelihood — never a binary "this is AI". No text-only detector escapes a false-positive floor.
- Density, not instance. One "delve", one em-dash, one rule-of-three is noise. Flag clusters and frequency, never a lone occurrence.
- User-facing track only. This governs READMEs, public docs, email, PR/proposal/issue bodies. It must NOT touch the terse model corpus.
- Collaborator. Findings are proposed; the rewrite is a draft. The catalogue grows via
/capture, never by self-editing this file.
When to use
- User types
/humanize(on a pasted draft, a selection, or a file path). - User asks to "humanize / de-AI / make this human / does this sound like AI / voice-check this".
Refuse (scope-guard): if the target is the terse model corpus — memory bodies (~/.claude/projects/*/memory/), codemap.md, spec/plan artefacts, Claude-facing docs — stop and say:
That's model-facing corpus (compression-disciplined by design). Humanizing it is a regression — it's meant to be terse. /humanize is for user-facing prose only.
Don't use for: commit messages (own hygiene rule), or non-prose copy-editing.
Pipeline
0. Detect register
Classify as tech-doc, email, or project-internal. State the detection; allow override. Load the matching module from references/. If genuinely ambiguous, ask.
1. Length gate
If under ~120 words (classifiers) / ~200 (GPT-4-class), say so and score conservatively or aggregate (e.g. across a sender's recent messages / a PR author's recent descriptions). Never a confident per-message verdict below the floor — it's a plateau, not a cliff, but short text is unreliable.
What ships with it
12 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.
- fixtures/density-high.md 1.1 KB
- fixtures/density-low.md 779 B
- fixtures/negative-model-corpus.md 717 B
- fixtures/sample-email.md 843 B
- fixtures/sample-project-internal.md 942 B
- fixtures/sample-tech-doc.md 857 B
- references/ai-vocabulary.v1.md 4.0 KB
- references/ai-vocabulary.v2.md 4.8 KB
- references/email.md 1.4 KB
- references/false-positives.md 2.6 KB
- references/project-internal.md 1.4 KB
- references/tech-doc.md 1.4 KB
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 · 83 lines · 73 tokens per session scan A 7444ba4c716b
humanize is a skill published in the GitHub repository AcKeskin/contexture (2 stars, last pushed 2mo ago), licensed MIT. It adds 73 tokens to every session and 1,498 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 2026-09-03.
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