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 repeated-games-reputationgit 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/repeated-games-reputation)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/repeated-games-reputation"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/repeated-games-reputation/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/repeated-games-reputation"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/repeated-games-reputation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
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 →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium Excessive Agency · line 88 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00118 | $0.02467 |
| Opus 5 | $0.00059 | $0.01234 |
| Sonnet 5 | $0.00024 | $0.00493 |
| Haiku 4.5 | $0.00012 | $0.00247 |
Grade A, and why
repeated-games-reputation 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repeated Games & Reputation
Overview
When parties repeat — or third parties observe — defection costs tomorrow's cooperation, flipping the Prisoner's Dilemma. Axelrod's 1979–1981 tournaments proved cooperation wins empirically; the Folk Theorem (Fudenberg & Maskin 1986) proved it mathematically. This skill diagnoses when cooperation is sustainable (discount factor check), selects the right strategy (TFT vs Generous TFT vs Pavlov), and engineers reputation infrastructure for markets where parties don't repeat directly. Composes with prisoners-dilemma · second-order-thinking · signaling-games.
When to Use
Apply when:
- A relationship is expected to continue between the same parties (supplier-buyer, employer-employee, GP-LP, founder-investor, customer-platform)
- Even in a one-shot direct interaction, third parties observe the move and adjust their willingness to play with you
- You're designing a platform or marketplace that needs strangers to cooperate — reputation infrastructure is the architectural question
- You're trying to escape a defection trap and the candidate escape is "repetition" or "reputation"
- A partnership keeps fragmenting — diagnose whether δ is too low or observation is broken
- Trust/safety reputation is shaping who wins AI adoption and AI-native competition — where capability converges, a bad launch or safety incident reprices every future round of enterprise adoption (and the AI capex supercycle only lengthens the shadow of the future)
When NOT to use:
- Genuinely one-shot with no third-party observability → use
prisoners-dilemma - Parties are about to exit (last round of finite game) — backward induction risk; standard repeated-game logic can fail
- Zero-sum situation — repetition can entrench rivalry rather than dissolve it
- "Repetition" is only nominal — rotating counterparties who don't talk = effectively one-shot
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 · 125 lines · 118 tokens per session scan A 40e0224cb335
repeated-games-reputation is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 11d ago), licensed MIT. It adds 118 tokens to every session and 2,467 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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