s4h-social-incentive-analysis

s4h-social-incentive-analysis is a skill for Claude Code from human-avatar/skills-for-humanity. It costs 65 tokens per session (1,055 once invoked), scanned A, original, MIT.

A framework for finding the real incentives that shape behaviour, rather than relying only on what people say motivates them. Incentives are the rewards, costs, pressures, or consequences that influence choices.

In plain words
What is it for?
Use it to analyse why people or teams act a certain way, compare stated and actual motivations, and identify which system rewards or penalties are driving the behaviour.
Why use it?
It helps explain behaviour that appears irrational when the system's actual rewards differ from its stated goals. Identifying that mismatch points to what might need to change.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool.

Part of the skills-for-humanity plugin — 197 skills, 1 hook shipped together

Good fit Use it to analyse why people or teams act a certain way, compare stated and actual motivations, and identify which system rewards or penalties are driving the behaviour.

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Install with agentmods
npx agentmods add skills/human-avatar/skills-for-humanity/s4h-social-incentive-analysis
Install

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.

Any agent
npx skills add human-avatar/skills-for-humanity --skill s4h-social-incentive-analysis
Clone the repo
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity

Made for: Claude Code.

Or install skills-for-humanity, the plugin that ships this one along with the rest of its 197 skills, 1 hook.

Wrote 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.

agentmods badge for s4h-social-incentive-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-social-incentive-analysis/github.svg)](https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-social-incentive-analysis)
Your own site
<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.

agentmods 80×15 button for s4h-social-incentive-analysis

Your own site · 80×15
<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>
Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,055 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash a8a0dab5db21, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/s4h-social-incentive-analysis/SKILL.md · 100 lines

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.

Read the full file on GitHub · 100 lines

Changes

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.

  1. 9d ago First seen · 100 lines · 65 tokens per session scan A a8a0dab5db21

Subscribe to this mod's changes

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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