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/acaprino/daodan/humanize-docsgit 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 | $0.00073 | $0.00864 |
| Opus 5 | $0.00036 | $0.00432 |
| Sonnet 5 | $0.00015 | $0.00173 |
| Haiku 4.5 | $0.00007 | $0.00086 |
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 2d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize Documentation
CRITICAL RULES
- Path required. The user must provide a path to documentation files or directory.
- Never invent content. Only restructure and rephrase existing content.
- Confirm scope. Show what will be rewritten before starting.
- Never enter plan mode. Execute immediately.
Step 1: Validate Target
Parse $ARGUMENTS for the documentation path.
If no path provided, ask:
Which documentation should I humanize? Provide a path to a file or directory.
Verify the path exists and contains documentation files (.md, .rst, .mdx, .txt).
Step 2: Assess and Confirm Scope
Read the target documentation. Present a brief assessment:
Documentation to humanize: [path]
Files found: [count]
Total lines: ~[count]
Issues detected:
- [X] instances of passive voice / AI boilerplate
- [X] dense paragraphs (> 4 sentences)
- [X] monolithic diagrams
- [X] missing progressive disclosure
- [X] mixed reference / tutorial content
1. Proceed with humanization
2. Narrow scope -- I'll specify which files
3. Cancel
Use AskUserQuestion. Do NOT proceed until the user confirms.
Step 3: Rewrite
Spawn the doc-humanizer agent:
Task:
subagent_type: "codebase-mapper:doc-humanizer"
description: "Humanize documentation at [path]"
prompt: |
Rewrite the following documentation to be human-readable.
## Target
[path and file list]
## Instructions
Read all target files and rewrite them following the codebase-mapper
writing guidelines. Humanize toward the target register (if the user
specified one) or infer it via
${CLAUDE_PLUGIN_ROOT}/skills/codebase-mapper/references/audience-adaptation.md.
Fix anti-patterns (passive voice, AI boilerplate,
dense text, missing structure) while preserving all factual content.
Rewrite files in-place using the Edit tool.
Provide a change summary when done.
Step 4: AI Trace Removal Pass
After the doc-humanizer restructures the content, run a second pass with the text-humanizer agent to catch remaining AI writing patterns in the prose.
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.
- 2d ago First seen · 125 lines · 73 tokens per session scan A c6748f9fd501
humanize-docs is a command published in the GitHub repository acaprino/daodan (8 stars, last pushed 6d ago), licensed MIT. It adds 73 tokens to every session and 864 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.