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-social-incentive-analysisgit 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-social-incentive-analysis)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-social-incentive-analysis"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-social-incentive-analysis/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-social-incentive-analysis"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-social-incentive-analysis.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 | $0.00065 | $0.01055 |
| Opus 5 | $0.00032 | $0.00528 |
| Sonnet 5 | $0.00013 | $0.00211 |
| Haiku 4.5 | $0.00006 | $0.00105 |
Grade A, and why
s4h-social-incentive-analysis 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incentive Analysis
Most behaviour that looks irrational is perfectly rational given the actual incentives. The problem is that systems are designed around intended incentives while people respond to actual ones. Finding the gap between the two explains what is happening and points to what would change it.
Your Process
Step 1: Describe the Behaviour Name the specific behaviour to explain or change. Be concrete — not "people aren't engaged" but "engineers don't attend architecture reviews and don't comment on RFCs."
Framing check: Confirm the specific behaviour and its context before continuing. State what you've identified — the actual behaviour being analysed, who is exhibiting it, and the system or setting it occurs in — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific behaviour, the actors involved, and the context]. 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: Map What the System Actually Rewards Not what it is supposed to reward — what actually gets people promoted, praised, defended, or protected? Look at recent promotions, public praise, and what leadership visibly prioritises.
Step 3: Map What the System Actually Punishes What leads to criticism, risk, political cost, or reduced standing? What do people avoid doing, even when they believe it is the right thing?
Step 4: Rationality Check Given the actual rewards and punishments identified in Steps 2–3: is the observed behaviour rational? In most cases it is. If it is rational, that is important — it means you cannot change the behaviour without changing the incentives.
Step 5: Identify the Incentive-Behaviour Gap Where do the intended incentives (what the system claims to reward) diverge from the actual incentives (what it truly rewards)? This gap is where dysfunction lives.
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 · 100 lines · 65 tokens per session scan A a8a0dab5db21
s4h-social-incentive-analysis is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 1,055 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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