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 daddia/claude-for-strategy --skill align-rewards-and-incentivesgit clone --depth 1 https://github.com/daddia/claude-for-strategyWrote 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/daddia/claude-for-strategy/align-rewards-and-incentives)<a href="https://agentmods.dev/skills/daddia/claude-for-strategy/align-rewards-and-incentives"><img src="https://agentmods.dev/badge/skills/daddia/claude-for-strategy/align-rewards-and-incentives/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/daddia/claude-for-strategy/align-rewards-and-incentives"><img src="https://agentmods.dev/badge/skills/daddia/claude-for-strategy/align-rewards-and-incentives.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00076 | $0.01057 |
| Opus 5 | $0.00038 | $0.00528 |
| Sonnet 5 | $0.00015 | $0.00211 |
| Haiku 4.5 | $0.00008 | $0.00106 |
Grade A, and why
align-rewards-and-incentives 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 11d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Align Rewards and Incentives
When to use
Star Model integration check — whether rewards, processes, and capabilities reinforce structure intent, not just the org chart.
What this skill does not do
- Does not redesign structure — route to
/operating-model:diagnose-structure-fit. - Does not map external incentives — route to
/market-intelligence:map-incentivesfor competitors/partners. - Does not implement comp changes — recommends specific changes for HR/leadership.
Preconditions
| Input | If missing |
|---|---|
| Structure intent (current or proposed) | Ask what behavior the structure should produce |
| Reward mechanism (practice profile or user) | Ask how people are measured/paid; flag [PROVISIONAL] |
| Process and capability context | Ask; proceed with labeled gaps |
Provisional mode
Without comp detail: reward fit labeled [review]; do not assert "supports" without mechanism evidence.
Trust spine
- Confidence bands (
structured-aggregation):- High: Reward, process, people checks complete with specific mechanisms named.
- Medium: Some gaps inferred; verdict qualified.
- Low: Structure intent unstated — halt.
- Failure modes:
- Strategic advice vs. support: Verdict is diagnostic; leadership owns implementation.
- Client confidentiality: Comp details highly sensitive — CONFIDENTIAL header; internal-only gate.
- Accountability gap: "Structure alone sufficient" only when all three fits support intent.
- Analytical Rigor: MECE across reward/process/people dimensions.
- Incentive Gaming: Predicts behavior from actual incentives, not values statements.
- Escalation triggers: Reward system actively undermines structure — state plainly reorg will fail without reward change.
Workflow
- Read practice profile for how people are measured and paid.
- State structure's intent plainly.
- Check reward fit — apply
map-incentiveslogic internally; predict behavior if unchanged. - Check process fit — approval chains, planning cycles assuming old structure.
- Check people/capability fit — skills the new structure assumes.
- Recommend specific changes beyond org chart.
- Completeness check before output.
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.
- 11d ago First seen · 110 lines · 76 tokens per session scan A 75c84426484d
align-rewards-and-incentives is a skill published in the GitHub repository daddia/claude-for-strategy (2 stars, last pushed 2mo ago), licensed MIT. It adds 76 tokens to every session and 1,057 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 2026-08-31.
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