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 agentmods add commands/ackeskin/contexture/humanizegit clone --depth 1 https://github.com/AcKeskin/contextureWhat 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 | $0.00050 | $0.00287 |
| Opus 5 | $0.00025 | $0.00143 |
| Sonnet 5 | $0.00010 | $0.00057 |
| Haiku 4.5 | $0.00005 | $0.00029 |
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 yesterday.
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 humanize skill for the current context.
Any text or file path after /humanize is treated as the draft to humanize. Examples:
/humanize— humanize the draft in the recent conversation / current selection; detect the register, ask if ambiguous./humanize README.md— humanize that file (tech-doc register)./humanize <pasted email>— detect email register, flag template scaffolding, rewrite to a real human voice.
The skill auto-detects the register (tech-doc / email / project-internal), checks the length gate, flags AI-texture by aggregate density with exact quotes (never single instances), scores on four evidence-based dimensions, and — given a writing sample for this run — returns a voice-calibrated rewrite that preserves every argument. It reports advisory likelihood, never a binary AI/human verdict, and refuses the terse model corpus (memory, codemap, specs). Mode A only — never auto-fires.
See ~/.claude/skills/humanize/SKILL.md for the full procedure.
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.
- yesterday First seen · 16 lines · 50 tokens per session scan A 59aaec3b6db2
humanize is a command published in the GitHub repository AcKeskin/contexture (2 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 287 once invoked, about $0.0003 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
translate-review-to-single-human
Translate a multi-reviewer code review into a single human-voice GitHub PR comment.
address
Address code review feedback — corroborate, validate, and implement changes from a review's final.md.
map
Generate a Code Review Map to help navigate large, complex changesets.
review
Run an AI-powered multi-agent code review on your changes.
sync-reviewers
Sync reviewer metadata from markdown files to reviewers-meta.json for the dashboard.
post
Post the current OCR review to a GitHub PR.