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 signaling-gamesgit 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/signaling-games)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/signaling-games"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/signaling-games/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/signaling-games"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/signaling-games.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 | $0.00145 | $0.02115 |
| Opus 5 | $0.00072 | $0.01058 |
| Sonnet 5 | $0.00029 | $0.00423 |
| Haiku 4.5 | $0.00015 | $0.00212 |
Grade A, and why
signaling-games 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.
Signaling Games
Overview
One party knows something the other cannot verify. Cheap talk fails — anyone can claim high quality. A signal works when its cost differs by type: only a capable worker can bear four years of demanding study; only a quality manufacturer can afford a lifetime warranty. The signal conveys truth exactly when it would be unbearable for the wrong type to fake it. Spence (1973) formalized this; Akerlof (1970) showed what happens when no signal exists (lemons).
Composes with prisoners-dilemma, repeated-games-reputation, and pricing-strategy.
When to Use
Apply when:
- One party cannot verify what the other knows — hiring, fundraising, M&A, B2B, branding, diplomacy
- A market is failing to price quality correctly (Akerlof lemons problem) and you need a fix
- You're designing a credential, certification, warranty, or brand investment
- You're trying to break into a market with a signal competitors can't cheaply imitate
- You need to detect real quality among multiple claimants
- You're reading geopolitical or policy moves as costly signals — chip export controls, sanctions, AI-capex commitments, or a rival's public capability demo read as signals of hidden capability or resolve
When NOT to use: information is symmetric; signal cost is the same for both types (expense, not signal); one-shot low-stakes interaction where direct verification is cheap.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete case → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → 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 what-it-is (≤2 sentences): when one side knows something the other can't verify, anyone can claim it — so only actions too expensive for the wrong type to fake actually communicate truth.
- Check fit against When to Use / When NOT to use. If symmetric info, point elsewhere.
- Elicit their real case (hiring, branding, fundraising, partnership — never run on hypotheticals).
[WAIT — do not advance until user responds]
- Walk The Process one step per turn — what's asymmetric, candidate signals, single-crossing test, equilibrium.
[WAIT — do not advance until user responds]
- Close by naming the one signal that fits their situation and what makes it credible.
[WAIT — do not advance until user responds]
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 · 145 tokens per session scan A cc478a0ac3b5
signaling-games is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 11d ago), licensed MIT. It adds 145 tokens to every session and 2,115 once invoked, about $0.0007 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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