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/marciopuga/cog/humanizergit clone --depth 1 https://github.com/marciopuga/cogWhat 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 | $0.00054 | $0.01507 |
| Opus 5 | $0.00027 | $0.00754 |
| Sonnet 5 | $0.00011 | $0.00301 |
| Haiku 4.5 | $0.00005 | $0.00151 |
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 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use this skill when the user wants to humanize, de-AI, or clean up AI-generated text. Trigger if the conversation involves:
- "Humanize this", "make this sound human", "de-AI this"
- "This sounds too AI", "too ChatGPT", "sounds robotic"
- Reviewing or editing text that reads like AI slop
- Cleaning up drafts for natural voice Do NOT trigger for original writing tasks (use /explainer instead). This skill is for editing existing text to remove AI patterns.
Domain
Writing quality — removing AI artifacts and injecting human voice. Based on Wikipedia's "Signs of AI writing" guide (WikiProject AI Cleanup).
Core Principle
Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop. Good writing has a human behind it.
Process
- Read the input text carefully
- Identify all instances of the patterns below
- Rewrite each problematic section
- Ensure the revised text sounds natural when read aloud, varies sentence structure, uses specific details over vague claims, and uses simple constructions (is/are/has) where appropriate
- Present a draft humanized version
- Self-audit: "What makes the below so obviously AI generated?" — answer briefly with remaining tells
- Revise: "Now make it not obviously AI generated." — present the final version
- Brief summary of changes made
Output Format
Provide:
- Draft rewrite
- "What still sounds AI?" (brief bullets)
- Final rewrite
- Summary of changes
PATTERN REFERENCE
Signs of Soulless Writing (even if technically "clean")
- Every sentence is the same length and structure
- No opinions, just neutral reporting
- No acknowledgment of uncertainty or mixed feelings
- No first-person perspective when appropriate
- No humor, no edge, no personality
- Reads like a Wikipedia article or press release
How to Add Voice
- Have opinions. React to facts. "I genuinely don't know how to feel about this" beats neutral pros-and-cons.
- Vary rhythm. Short punchy sentences. Then longer ones that take their time. Mix it up.
- Acknowledge complexity. Real humans have mixed feelings.
- Use "I" when it fits. First person isn't unprofessional — it's honest.
- Let some mess in. Perfect structure feels algorithmic. Tangents and half-formed thoughts are human.
- Be specific about feelings. Not "this is concerning" but name what actually unsettles you.
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 · 167 lines · 54 tokens per session scan A c8d4896d844e
humanizer is a command published in the GitHub repository marciopuga/cog (376 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 1,507 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 2026-08-30.
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.