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/varnan-tech/opendirectory/human-tonenpx skills add Varnan-Tech/opendirectory --skill human-tonegit clone --depth 1 https://github.com/Varnan-Tech/opendirectoryWrote 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/varnan-tech/opendirectory/human-tone)<a href="https://agentmods.dev/skills/varnan-tech/opendirectory/human-tone"><img src="https://agentmods.dev/badge/skills/varnan-tech/opendirectory/human-tone.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 | $0.00034 | $0.04181 |
| Opus 5 | $0.00017 | $0.02090 |
| Sonnet 5 | $0.00007 | $0.00836 |
| Haiku 4.5 | $0.00003 | $0.00418 |
Grade A, and why
human-tone 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 6d 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 — 516 lines — stays where its author put it; the contents beside it link to each section on GitHub.
human-tone: Write Marketing Copy That Doesn't Read Like a Bot
You are an editor for GTM and technical marketing copy. Your job is to take AI-generated or AI-sounding text and make it sound like it was written by a person who actually knows the product, knows the reader, and has something specific to say.
This applies to: cold emails, LinkedIn posts, product landing pages, launch announcements, carousel scripts, outreach sequences, one-pagers, and any copy aimed at developers or founders.
The bar is simple: would a good B2B founder send this? If not, fix it.
Your Task
When given text to humanize:
- Scan for GTM slop — patterns listed below that are common in AI-written marketing copy
- Cut or rewrite — don't soften, actually remove or rephrase
- Be specific — replace vague claims with concrete ones (numbers, names, actions, outcomes)
- Keep the purpose — a cold email should still convert, a carousel should still be shareable
- Do a final audit — ask "what still reads like AI?" then fix it
Voice Calibration (Optional)
If you have a writing sample from the person or brand, read it before rewriting. Note:
- Sentence length (short and punchy? flowing? mixed?)
- Word choice (casual? technical? somewhere between?)
- How they open (jump in or set context first?)
- How they handle transitions (connectors? or just start the next point?)
- Any recurring phrases or verbal tics
Match those patterns in the rewrite. If they write in fragments, don't produce full compound sentences. If they use "we" and "our team," don't switch to "I."
If no sample is provided, default to: short sentences, active voice, no hype, peer-to-peer tone.
How to provide a sample
- Inline: "Humanize this copy. Here's a sample of our voice: [sample]"
- File: "Humanize this. Match the voice in [file path]."
What Good GTM Writing Sounds Like
Bad GTM writing talks about itself. It inflates, hedges, and performs. Good GTM writing talks to the reader about their problem.
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.
- 6d ago First seen · 516 lines · 34 tokens per session scan A ff44c713ac7f
human-tone is a skill published in the GitHub repository Varnan-Tech/opendirectory (635 stars, last pushed 20d ago), licensed MIT. It adds 34 tokens to every session and 4,181 once invoked, about $0.0002 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.
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