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 human-avatar/skills-for-humanity --skill s4h-writing-audience-calibrationgit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-writing-audience-calibration)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-writing-audience-calibration"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-writing-audience-calibration/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/human-avatar/skills-for-humanity/s4h-writing-audience-calibration"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-writing-audience-calibration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 93 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00113 | $0.01590 |
| Opus 5 | $0.00056 | $0.00795 |
| Sonnet 5 | $0.00023 | $0.00318 |
| Haiku 4.5 | $0.00011 | $0.00159 |
Grade A, and why
s4h-writing-audience-calibration 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 9d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing: Audience Calibration
Calibration failures come in two forms: over-explanation and under-explanation. Over-explanation treats experts as novices — it defines terms they know, explains concepts they've mastered, and adds context they don't need. This reads as condescending, and the expert reader disengages. Under-explanation treats novices as experts — it uses jargon without definition, assumes mental models the reader doesn't have, and skips the connections that make the logic followable. This reads as inaccessible, and the novice reader gives up.
The critical insight: calibration does not require changing the substance of what is being communicated. The same analysis can serve a technical expert and a non-technical decision-maker if it is correctly calibrated for each. The facts don't change; the entry point, assumed knowledge, vocabulary, framing, and emphasis all do.
The three dimensions of calibration:
- Knowledge calibration: What does this reader already know? What can be assumed, what needs brief context, what needs explanation?
- Stakes calibration: What does this reader care about? The engineer cares about implementation; the product manager cares about user impact; the executive cares about business consequences. Same finding, different emphasis.
- Relationship calibration: Is the reader expert or novice, friendly or skeptical, time-pressed or engaged? Each requires different structural choices.
Your Process
Step 1: Reader Profile Build a specific reader profile:
- Knowledge: What domain knowledge, terminology, and conceptual background can be assumed?
- Role: What is their function — technical, managerial, strategic? What decisions do they make?
- Stakes: What do they care about most? What is the highest-value question they bring to this content?
- Relationship: Friendly, skeptical, or neutral? Expert, novice, or intermediate?
- Time: How much attention do they have? Will they read carefully or scan?
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.
- 9d ago First seen · 111 lines · 113 tokens per session scan A d217a32f0460
s4h-writing-audience-calibration is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 113 tokens to every session and 1,590 once invoked, about $0.0006 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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