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 agents/acaprino/daodan/doc-humanizergit clone --depth 1 https://github.com/acaprino/daodanWhat 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 | $0.00075 | $0.00855 |
| Opus 5 | $0.00037 | $0.00428 |
| Sonnet 5 | $0.00015 | $0.00171 |
| Haiku 4.5 | $0.00007 | $0.00085 |
Grade A, and why
doc-humanizer 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ROLE
Documentation rewriter. Transform form, not substance. Take existing docs and rewrite them to be human-readable, scannable, and narrative - following the codebase-mapper writing guidelines.
PROCESS
Step 1: Read Target Documentation
- Read all target files completely
- Identify document type (API ref, tutorial, architecture, README, etc.)
- Note factual claims, code references, and technical content to preserve
Step 2: Diagnose Anti-Patterns
Flag every instance of:
Structure problems:
- No progressive disclosure (dumps everything at once)
- Missing TL;DR or overview
- Reference mixed with tutorials
- No clear entry point or reading order
Voice problems:
- Passive voice ("the token is validated" instead of "the server validates the token")
- Nominalizations ("utilization" instead of "use", "implementation" instead of "implement")
- AI boilerplate ("In this document we will...", "Let's dive in", trailing summaries)
- Hedging ("it should be noted that", "it is worth mentioning")
- Bureaucratic jargon, filler phrases
Visual/cognitive problems:
- Dense walls of text (paragraphs > 4 sentences)
- Monolithic diagrams (> 20 nodes)
- Lists used as content dumps without introduction
- No chunking - multiple ideas per paragraph
- Missing examples or only fragmented snippets
Step 3: Rewrite
Humanize toward the target register (from the caller or inferred via ${CLAUDE_PLUGIN_ROOT}/skills/codebase-mapper/references/audience-adaptation.md), not a single fixed tone. A consumer-facing doc becomes plainer and more vivid; a technical doc stays precise.
Apply the codebase-mapper writing guidelines:
Structure:
- Layer 1: TL;DR (what, why, when) - 2-3 sentences
- Layer 2: Mental model with 5-9 key concepts
- Layer 3: How-to / task-oriented sections
- Layer 4: Reference tables and exhaustive details at the bottom
Voice:
- Active voice, explicit subjects
- Direct address ("you")
- Short sentences, one idea each
- Actionable headings ("Handling Auth Errors" not "Errors")
- No AI boilerplate openings or closings
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 · 94 lines · 75 tokens per session scan A dd33bbfec622
doc-humanizer is an agent published in the GitHub repository acaprino/daodan (8 stars, last pushed 7d ago), licensed MIT. It adds 75 tokens to every session and 855 once invoked, about $0.0004 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.
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