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-tension-mappinggit 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-tension-mapping)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-narrative-tension-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-narrative-tension-mapping/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-tension-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-narrative-tension-mapping.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 | $0.00060 | $0.00995 |
| Opus 5 | $0.00030 | $0.00498 |
| Sonnet 5 | $0.00012 | $0.00199 |
| Haiku 4.5 | $0.00006 | $0.00100 |
Grade A, and why
s4h-narrative-tension-mapping 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Narrative Tension Mapping
Without a gap between current state and desired state, communication is noise. Tension is not drama or manufactured urgency — it is the honest articulation of what is wrong, at risk, or missing. Audiences disengage not because a topic is unimportant but because the communication fails to make the gap visible and real. The tension must be felt before the solution can land.
Your Process
Step 1: State the Communication What is the communication — its subject, its argument, its ask? State it plainly before analyzing what's missing.
Framing check: Confirm the specific communication before continuing. State what you've identified — the actual content being analyzed, its intended audience, and its core ask — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the communication, its audience, and its intended ask]. 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: Locate the Tension Ask: what is wrong, at risk, or missing in the world this communication addresses? That is the tension. It should be a gap between where things are and where they need to be. If you can't find it, note that — it's diagnostic.
Step 3: Test for Genuine Tension If no tension is apparent: ask why this communication matters at all. If the honest answer is "it doesn't," that is the real problem to solve — the communication should not exist yet or should be restructured around something that does matter.
Step 4: Test for Audience Relevance Is this tension real to the audience, or only to the sender? A tension that only the sender feels is not yet a tension — it requires first making the audience care about the domain before the gap can register.
Step 5: Surface It Plainly State the tension explicitly, early, without burying it in qualifications. The single most common failure: putting the tension in the middle or end after extensive context-setting. Audiences stop paying attention before they reach it.
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 · 86 lines · 60 tokens per session scan A 0f54cb76ae2f
s4h-narrative-tension-mapping is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 995 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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