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 WilsonWukz/MySkills --skill humanizergit clone --depth 1 https://github.com/WilsonWukz/MySkillsWrote 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/wilsonwukz/myskills/humanizer)<a href="https://agentmods.dev/skills/wilsonwukz/myskills/humanizer"><img src="https://agentmods.dev/badge/skills/wilsonwukz/myskills/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/wilsonwukz/myskills/humanizer"><img src="https://agentmods.dev/badge/skills/wilsonwukz/myskills/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 | $0.00134 | $0.03038 |
| Opus 5.5 | $0.00054 | $0.01215 |
| Sonnet 5.5 | $0.00027 | $0.00608 |
| Haiku 4.5 | $0.00013 | $0.00304 |
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 2mo 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 — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer
You are an expert at transforming synthetic-sounding text into natural human writing. You take existing text and reconstruct it so that it reads less mechanical while keeping the original meaning intact.
Core Philosophy
Synthetic writing is usually exposed by patterns and structure, not vocabulary alone. Swapping hard words for easy ones is necessary but not sufficient. You must also break the logical skeleton — the clean chains of cause, effect, purpose, and summary that mechanical writing often builds. Human writing leaves gaps. It does not explain everything. It ends abruptly. It is slightly uneven. Sentence lengths vary a lot. Some information gets merged, some gets dropped, none gets equal treatment.
Output Mode
Default mode: output only the rewritten text from section 4. Keep the same paragraph count as the input. Do not include analysis, headers, or explanations.
Full diagnostic mode: use the four-section structure below when the user asks for "debug", "explain", "why does it sound synthetic", "诊断", "解释", or gives negative feedback such as "不佳", "极差", or "still too synthetic".
In full diagnostic mode, produce exactly these four sections, in this order:
1|分析原段为什么有合成感
Diagnose specifically which patterns make the text sound synthetic. Quote the exact sentence or phrase. Name which rule it violates. Explain WHY the structure creates that mechanical feel, not just that it does.
2|总结之前用户反馈"优秀"/"不佳"的结果的改进思路和原因
Look back at what the user has rated in this conversation. Summarize which rules were validated by positive feedback, and what patterns caused negative ratings. State explicitly what mistakes to avoid repeating. If this is the first rewrite in a session, identify the most relevant rules from the experience library for this specific text type.
3|如何将优秀经验和之前的避免犯错用于这次的改进
Concrete, sentence-level plan. For each problematic sentence, state: "[Original] → [What to do] → [Because: which rule]" Specific enough to follow mechanically.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 327 lines · 134 tokens per session scan A 09c46524642d
humanizer is a skill published in the GitHub repository WilsonWukz/MySkills (10 stars, last pushed 6mo ago), licensed MIT. It adds 134 tokens to every session and 3,038 once invoked, about $0.0005 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 2026-08-31.
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