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 skills/richfrem/agent-plugins-skills/humanizenpx skills add richfrem/agent-plugins-skills --skill humanizegit clone --depth 1 https://github.com/richfrem/agent-plugins-skillsWrote 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/richfrem/agent-plugins-skills/humanize)<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/humanize"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/humanize.svg" alt="Measured on agentmods" 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 | $0.00151 | $0.01431 |
| Opus 5 | $0.00076 | $0.00715 |
| Sonnet 5 | $0.00030 | $0.00286 |
| Haiku 4.5 | $0.00015 | $0.00143 |
Grade A, and why
humanize 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 yesterday.
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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize
Transform stiff, AI-flavored, or over-polished writing into something that sounds like it came from an actual person -- with a distinct voice, natural rhythm, and the small imperfections that signal authenticity.
Input Context
The user provides text to humanize. They may also provide:
- Voice context: "write like a senior engineer", "this is for my LinkedIn"
- Channel: email, social post, blog, Slack, internal memo
- Tone direction: "warmer", "more direct", "less formal"
If none of these are provided, infer them from the text. Ask only if the target voice is genuinely ambiguous and would change the output significantly.
If the user has placed writing samples in
references/voice-profile/my-voice.md, read that file before rewriting.
It contains their preferred sentence patterns, register, and vocabulary. Apply
those patterns instead of projecting a generic voice.
Phase 1: Diagnose
Before rewriting, read the text as a human editor would. Identify internally:
- AI fingerprints -- which structural patterns are present?
- What's actually being said -- strip the structure, find the real content
- Who should be saying it -- what kind of person, in what context?
- What's missing -- real writing usually has a point of view, a concrete detail, or a light edge that has been smoothed away
Do this quickly and internally. Do not narrate this to the user unless asked.
For a full catalog of AI patterns and their fixes, read:
references/patterns.md
Phase 2: Rewrite
Apply these principles in order:
Voice first, fixes second. Do not just remove bad patterns. Replace them with something that has character. A human does not just avoid em dashes -- they use shorter sentences, or fragments, or a question.
One idea per sentence, usually. Most AI text over-compounds. Break it up. Short sentences are not unsophisticated -- they are confident.
Concrete over abstract. Replace vague abstractions with specifics wherever possible. "Improved performance" becomes "cut load time in half." "Valuable insights" -- name one. If there is no specific to reach for, cut the claim.
What ships with it
7 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.
- yesterday First seen · 156 lines · 151 tokens per session scan A 9c309021b2e1
humanize is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed yesterday), licensed MIT. It adds 151 tokens to every session and 1,431 once invoked, about $0.0008 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 2026-09-03.
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