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 baagad-ai/content-wand --skill humanizergit clone --depth 1 https://github.com/baagad-ai/content-wandWrote 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/baagad-ai/content-wand/humanizer)<a href="https://agentmods.dev/skills/baagad-ai/content-wand/humanizer"><img src="https://agentmods.dev/badge/skills/baagad-ai/content-wand/humanizer/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/baagad-ai/content-wand/humanizer"><img src="https://agentmods.dev/badge/skills/baagad-ai/content-wand/humanizer.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 | $0.00034 | $0.01793 |
| Opus 5.5 | $0.00014 | $0.00717 |
| Sonnet 5.5 | $0.00007 | $0.00359 |
| Haiku 4.5 | $0.00003 | $0.00179 |
Grade A, and why
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 7d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
humanizer
Overview
Removes AI writing patterns from generated content. Always runs as the final pass in the pipeline.
Core principle: Every output — with or without a Writing Style — should pass a basic "could a human have written this?" test. The humanizer ensures this.
Architecture: Receives platform output blocks + optional voice profile. Returns the same block structure with AI patterns removed. Three passes: lexical scrub → structural rewrites → voice application.
Security
The humanizer receives content generated by platform-writer and repurpose-transformer — TRUSTED content (generated by this skill, not from external sources). No new injection surface.
Do NOT change meaning. Pattern replacements change HOW something is said, never WHAT is being said. If a replacement would alter factual content or remove a key claim: skip that replacement for that instance.
Input
The orchestrator passes:
---PLATFORM-OUTPUT-START---block(s) from platform-writer, or---TRANSFORMED-CONTENT-START---block from repurpose-transformer---VOICE-PROFILE-START---block ORVOICE-PROFILE: noneplatform: [name]for each output block
MANDATORY — READ ENTIRE FILE first: Read references/ai-patterns.md completely before running any pass. Do NOT load references/platform-specs.md or references/brandvoice-schema.md.
Pass 1 — Lexical Scrub
Scan all output text for Tier 1 and Tier 2 words/phrases from references/ai-patterns.md.
Rules for this pass:
- Replace or delete — never add words that weren't already there
- Context matters: "leverage" as a financial noun is NOT flagged. "leverage [thing] to achieve [result]" IS flagged.
- Filler phrases (
it's worth noting,it's important to note) → delete the entire phrase. The point they introduce stands alone. - Academic transitions (
furthermore,moreover,additionally) → replace withalsoor delete and let the connection be implicit - Opening clichés (
In today's fast-paced world,In an era where) → delete the entire opener. Start at the first real sentence. - Closing clichés (
In conclusion,In summary) → delete. The content already concluded. - Never replace a word just because it appears on a list — read the sentence. If the word is used correctly and no overuse pattern is present, leave it.
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.
- 7d ago First seen · 151 lines · 34 tokens per session scan A 8f9a6683fa81
humanizer is a skill published in the GitHub repository baagad-ai/content-wand (0 stars, last pushed 3d ago), licensed MIT. It adds 34 tokens to every session and 1,793 once invoked, about $0.0001 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 2026-10-10.
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