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 skills/wrg32786/aigent-os/humanize-docsnpx skills add wrg32786/aigent-os --skill humanize-docsgit clone --depth 1 https://github.com/wrg32786/aigent-osWrote 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/wrg32786/aigent-os/humanize-docs)<a href="https://agentmods.dev/skills/wrg32786/aigent-os/humanize-docs"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/humanize-docs.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 | $0.00125 | $0.01259 |
| Opus 5 | $0.00063 | $0.00629 |
| Sonnet 5 | $0.00025 | $0.00252 |
| Haiku 4.5 | $0.00013 | $0.00126 |
Grade A, and why
humanize-docs 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 3d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize Docs
Transforms robotic AI-generated documentation into prose that sounds like an actual human wrote it. The approach is aggressive deformalization — not just tweaking tone, but restructuring how information flows to break AI's predictable patterns.
This is a different tool than the humanizer skill. Humanizer removes AI writing tells at the sentence/phrase level (em-dashes, rule-of-three, vague attributions, sycophantic openers). Humanize-docs attacks structural AI-ness: checkbox spam, template overload, perfect symmetry, emotional flatness across an entire document.
When to use this
Clear signals you're dealing with AI-generated docs:
- Checkbox overload:
- [x] Task 1: Do the thing (AC: #1) - CAPS LOCK for CRITICAL IMPORTANT NOTES
- 8+ code templates embedded in one document
- Every section has exactly 3 perfectly balanced paragraphs
- Headers like "LLM Developer Guardrails"
- Phrases: "Certainly, here is...", "It's important to note that...", "Furthermore..."
Transformation workflow
Step 1: Detect
Quick test — does the doc have 5+ of these?
- Perfect structural symmetry (every section same length)
- Zero sentence length variation (all 15-20 words)
- Checkbox addiction
- Template embedding mania
- Emotional flatness (no "wait, why?" or "honestly...")
If yes, proceed. If no, it might already be human-written — don't over-transform.
Step 2: Apply the five core transformations
Execute in order. Don't apply all of them everywhere — humans are inconsistent. Some sections stay formal, others get playful. That asymmetry is the point.
1. Burstiness injection Mix 5-word punches with 30-word reflections. AI writes at one rhythm. Humans don't.
Before:
The system processes requests through a standardized pipeline that validates inputs and returns structured responses to the calling service.
After:
It's a pipeline. Request comes in, gets validated, structured response goes back out. Nothing exotic — but the validation step is where things quietly break.
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.
- 3d ago First seen · 144 lines · 125 tokens per session scan A 5393d68e04bc
humanize-docs is a skill published in the GitHub repository wrg32786/aigent-os (17 stars, last pushed today), licensed MIT. It adds 125 tokens to every session and 1,259 once invoked, about $0.0006 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 2026-09-01.
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