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-narrative-audience-modelinggit 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-narrative-audience-modeling)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-narrative-audience-modeling"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-narrative-audience-modeling/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-narrative-audience-modeling"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-narrative-audience-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00067 | $0.00986 |
| Opus 5 | $0.00034 | $0.00493 |
| Sonnet 5 | $0.00013 | $0.00197 |
| Haiku 4.5 | $0.00007 | $0.00099 |
Grade A, and why
s4h-narrative-audience-modeling 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Narrative Audience Modeling
Communication fails at the receiver, not the sender. The most common communication failure is not poor evidence or unclear logic — it is delivering a message the audience was not ready to receive, about a problem they do not recognize, to a goal they do not hold. Modeling the audience before communicating means identifying not what you want to say, but what they are able to hear.
Your Process
Step 1: Name Specific People Resist generic categories. Not "senior leadership" but "the CFO and CTO who approved last quarter's roadmap". The more specific the audience, the more useful the model.
Framing check: Confirm the specific audience and communication context before continuing. State what you've identified — the actual people being modeled and the communication situation — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific audience and the communication they need to receive]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Current Belief What do they already think about this topic? Include their current confidence level. This is the starting point — you are moving them from here, not from zero.
Step 3: Real Goal What do they actually care about — the underlying motivation, not their stated preference? "Wants a decision" often means "wants to not be blamed for a bad outcome". Stated goals are proxies; find the underlying one.
Step 4: Fear What do they need not to lose? Status, control, consistency with a prior decision, a relationship, a budget. Fear shapes reception more than aspiration does.
Step 5: What Moves Them — and What Doesn't What evidence, framing, or messenger would change their mind? What definitely will not work, regardless of quality? Understanding the latter saves time.
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 · 89 lines · 67 tokens per session scan A b76748fd4263
s4h-narrative-audience-modeling is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 986 once invoked, about $0.0003 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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