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-emotional-motivation-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-emotional-motivation-mapping)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-emotional-motivation-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-emotional-motivation-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-emotional-motivation-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-emotional-motivation-mapping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
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 →
- low Excessive Agency · line 3 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00074 | $0.01396 |
| Opus 5 | $0.00037 | $0.00698 |
| Sonnet 5 | $0.00015 | $0.00279 |
| Haiku 4.5 | $0.00007 | $0.00140 |
Grade A, and why
s4h-emotional-motivation-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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Emotional Motivation Mapping
People tell you what they think they should want. They behave according to what they actually want. When the two diverge — when stated goals and actual behaviour don't match, when someone ostensibly aligned keeps producing friction — the problem is almost always that the real motivators haven't been surfaced. This skill maps them across three levels: extrinsic, intrinsic, and social.
Your Process
Step 1: List Individuals or Groups Name each person or group whose motivation needs mapping. Be specific — "the engineering team" and "the engineering manager" may have completely different dominant motivators. Split when in doubt.
Framing check: Confirm the specific people or groups being mapped before continuing. State what you've identified — the actual individuals or groups and the situation driving the need for this mapping — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of who is being mapped and what situation is creating the motivation question]. 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: Extrinsic Motivators What external rewards and penalties shape their behaviour? Consider: compensation structure (base, bonus, equity), formal recognition mechanisms (awards, visibility, attribution), advancement paths available, job security, and the specific performance metrics they're formally evaluated against. These are the motivators the system is designed to create — they may or may not match what actually drives behaviour.
Step 3: Intrinsic Motivators What internal drives are they pursuing independently of external reward? Consider: mastery (the desire to get better at something they care about), autonomy (control over how and when they work), purpose (feeling the work connects to something that matters), belonging (being part of a team or mission they identify with). These often operate below the surface but produce the most durable behaviour.
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 · 127 lines · 74 tokens per session scan A 75a323de628d
s4h-emotional-motivation-mapping is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 74 tokens to every session and 1,396 once invoked, about $0.0004 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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