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 deciqAI/knowledge-skills --skill incentive-designgit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote 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/deciqai/knowledge-skills/incentive-design)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/incentive-design"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/incentive-design/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/deciqai/knowledge-skills/incentive-design"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/incentive-design.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 | $0.00115 | $0.01738 |
| Opus 5 | $0.00057 | $0.00869 |
| Sonnet 5 | $0.00023 | $0.00348 |
| Haiku 4.5 | $0.00012 | $0.00174 |
Grade A, and why
incentive-design 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incentive Design
Overview
Behavior follows incentives more reliably than character, intent, or training. Get the incentives right and mediocre operators produce excellent results; get them wrong and talented teams produce dysfunction. This is Charlie Munger's "Reward and Punishment Superresponse Tendency" — his first and most important of 25 psychological tendencies (1995 Harvard Law School lecture). The operational question: when behavior is undesirable, ask "what incentive makes this rational?" before asking "what's wrong with these people?"
Composes with principal-agent, goodharts-law, signaling-games, okr-goal-setting, prisoners-dilemma.
When to Use
- Designing compensation, bonuses, commissions, OKRs, or performance management
- Diagnosing why a team is producing undesirable behavior despite training or management
- Drafting contracts, regulations, or platform rules where behavior must be shaped
- Evaluating an existing system for hidden perverse incentives
- Designing reward signals or pricing in AI-native products (RLHF/reward hacking, usage-based vs outcome-based pricing, scarce AI-talent comp amid heavy AI capex and fast AI adoption)
Not when: clearly individual misconduct unrelated to systemic incentives; using incentive framing to excuse deliberate bad-faith behavior.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete incentive design challenge → run The Process directly.
- Coach mode: user is new to the framework → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line: rational actors produce the behavior incentives favor, regardless of stated intent — check incentives before character.
- Check fit. If the behavior is clearly individual misconduct, this framework adds less value. Otherwise, apply.
- Elicit the goal and current incentives. What behavior do you want? What incentives exist now? What are those incentives producing?
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 123 lines · 115 tokens per session scan A 90fd5df3740c
incentive-design is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 115 tokens to every session and 1,738 once invoked, about $0.0006 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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