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 swan-gtm/gtm-skills --skill humanize-contentgit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/humanize-content)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/humanize-content"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/humanize-content/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/swan-gtm/gtm-skills/humanize-content"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/humanize-content.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 | $0.00135 | $0.01222 |
| Opus 5.5 | $0.00054 | $0.00489 |
| Sonnet 5.5 | $0.00027 | $0.00244 |
| Haiku 4.5 | $0.00014 | $0.00122 |
Grade A, and why
humanize-content 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 5d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use when a draft is correct but sounds produced rather than written. Produces a rewrite in plain, connected prose with claims first, evidence attached and a person doing the acting, plus a short list of what changed and what still needs a source.
Load first: the draft, where it will appear (page, email, post, article) and who reads it. Ask whether a brand voice guide or house style exists and follow it where it conflicts with the defaults in references/house-style-options.md. Confirm the few switches that vary by company: English variant, em dashes, banned vocabulary, and whether byline or headline wording is already agreed. The purpose is clarity and voice. It is not to defeat AI detectors, and it never changes who the author is.
The play
- Diagnose. Run
scripts/lint_style.pyon the draft when code execution is available (references/using-the-checker.md). Otherwise test the draft by hand against the six patterns below. Also read for sentences with no verb, which the checker only partly catches. - Rewrite pattern by pattern. Give every sentence a subject and a finite verb, keep every fact and number, and use the fixes in
references/retired-patterns.md. Studyreferences/rewrite-bank.mdfor before-and-after pairs. - Re-check and read aloud. Rerun the checker until no
RULEhits remain, decide eachCHECKhit, then read the draft once as speech. If it sounds like a stack of statements, join them into continuous prose. - Report. List the patterns removed, the sentences that changed meaning even slightly, and every figure that has no unit, source or date. Ask for those instead of inventing them.
For voice, sentence length and structure, read references/voice-and-structure.md.
The six patterns to retire
| Pattern | Looks like | Fix |
|---|---|---|
| 1. Fragment with a tag | "Four layers, built in order." | Give it a subject and a verb: "The stack has four layers, and each rests on the one beneath." |
| 2. The "so" splice | "Pages that explain the category, so the buyer reads yours first." | Make the effect the main verb, put the condition first, or use "because". |
| 3. "Worth" and other editorial nudges | "worth noting", "it is important to note", "keep in mind" | State the thing and, if needed, the reason it matters. |
| 4. "Long before" and "long after" | "long before anyone books a call" | Give the interval or name the event: "months before". |
| 5. The "none of it" closer | "None of that makes the decision less human." | State the positive fact, or use a precise negative. |
| 6. The sentence that announces its point | "The part that compounds is deciding what to say." | Let the actor do the thing: "Content compounds when a company decides what to say." |
What ships with it
6 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.
- 5d ago First seen · 56 lines · 135 tokens per session scan A 42eac5afe558
humanize-content is a skill published in the GitHub repository swan-gtm/gtm-skills (181 stars, last pushed 2d ago), licensed MIT. It adds 135 tokens to every session and 1,222 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 2026-10-12.
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