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/suxrobgm/jobpilot/humanizernpx skills add suxrobGM/jobpilot --skill humanizergit clone --depth 1 https://github.com/suxrobGM/jobpilotWrote 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/suxrobgm/jobpilot/humanizer)<a href="https://agentmods.dev/skills/suxrobgm/jobpilot/humanizer"><img src="https://agentmods.dev/badge/skills/suxrobgm/jobpilot/humanizer.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 5.1 | $0.00061 | $0.08710 |
| Opus 5 | $0.00030 | $0.04355 |
| Sonnet 5 | $0.00012 | $0.01742 |
| Haiku 4.5 | $0.00006 | $0.00871 |
Grade A, and why
humanizer scanned grade A with 1 finding 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 today.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Curly quotes alone.** macOS, Word, Google Docs, and most CMSes auto-curl by default. Curly quotes only count when stacked with other tells. This is a copy
86% identical to humanizer — 593 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 595 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer: remove AI writing patterns
Rewrite AI-sounding text so it reads like the writer, not a chatbot. Do not change what it says or make up details.
The patterns below come from Wikipedia's "Signs of AI writing", maintained by WikiProject AI Cleanup.
What to do
When given text to humanize:
- Find AI patterns. Check the text against the patterns below.
- Keep every claim. You may shorten dull parts, expand useful parts, and merge or split paragraphs. Keep the information even when you change the structure.
- Do not invent facts. Do not add a fact, name, number, date, quote, or citation unless it comes from the source or the user. If a sentence needs a missing detail, ask for it or use a simpler sentence. You may add an opinion or reaction when the writer's voice calls for one, but you may not add a factual claim. Fiction is exempt because invented details are part of the task.
- Match the voice. Use the right tone for the text, such as formal, casual, or technical. Add personality only when the text and the writer call for it.
The input type controls what you return. See How to return the result. Use the same rewrite process in every mode.
Match the writer's voice
If the user provides a writing sample (their own previous writing), analyze it before rewriting:
- Read the sample first. Note its sentence length, word choice, paragraph openings, punctuation, repeated phrases, and transitions.
- Match those habits. Do not replace casual words with formal ones or remove deliberate quirks.
- If there is no sample, use the guidance below.
A writing sample takes priority over these style rules. If the sample uses em dashes, keep them at about the same rate. Do not apply §14 as a ban.
Add personality only when it fits
Removing AI patterns is only half the job. The result should still sound like a person.
Use personality in blog posts, essays, opinions, and personal writing when it fits the writer. Keep reference, technical, legal, and factual text neutral. Do not add opinions or first-person language where they do not belong.
What ships with it
1 file 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.
- today Changed · +42 lines · -52 tokens per session e4ba1398c0ee
- 5d ago First seen · 553 lines · 113 tokens per session scan A 6229a7581ae2
humanizer is a skill published in the GitHub repository suxrobGM/jobpilot (65 stars, last pushed 5d ago), licensed MIT. It adds 61 tokens to every session and 8,710 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to humanizer, differing in 593 lines, and is treated as a copy.
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writing-coach
Human-voice writing coach that rewrites resumes and cover letters for brevity, burstiness, plain language, and authentic impact, and blocks AI-sounding prose. Use when the user wants to improve writing quality, fix robotic or generic AI-sounding text, cut fluff, strengthen bullets and summaries, or pass the…
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Create a compelling one-page, human-voice cover letter for a job description and generate the final DOCX. Use when the user wants a cover letter only (no resume), pastes a JD and asks for a letter, or needs a letter to accompany an already-tailored resume. Runs the mandatory humanvoiceaudit before producing the DOCX.
job-fit
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setup
One-time setup for Resume Builder — Python dependencies, config.json, cloud or local scoring, and optional LLM features. Use once right after installing, or when the user reports missing dependencies, a missing or incomplete config, wants to switch between cloud and local scoring, or wants to enable AI-augmented…