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 social-proofgit 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/social-proof)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/social-proof"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/social-proof/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/social-proof"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/social-proof.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 | $0.00101 | $0.02252 |
| Opus 5 | $0.00051 | $0.01126 |
| Sonnet 5 | $0.00020 | $0.00450 |
| Haiku 4.5 | $0.00010 | $0.00225 |
Grade B, and why
social-proof scanned grade B with 1 finding 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- **Coach mode:** user is unfamiliar or has no concrete case → guide, don't lecture. 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.
Social Proof
Overview
Social proof: we judge what is correct, normal, or worth doing by observing what others — especially similar others — are doing. Usually efficient; failure mode is severe: under unanimous consensus, people publicly endorse answers they privately know are wrong (Asch 1951–56: error rate <1% alone, ~37% under group pressure). Two amplifiers: uncertainty (social proof fills the vacuum) and similarity (same-type peers drive far stronger conformity than generic crowds).
Composes with reciprocity (Cialdini's two primary levers), anchoring (price tiers often function as quasi-social-proof), and critical-thinking (structured fallback when consensus has been engineered).
When to Use
Use when: purchase/hiring/investment decision leaning on what others chose; proposal cites "everyone is doing this"; designing growth/marketing/UX with social-proof patterns; decision feels unsafe alone without a clear reason; suspecting manufactured consensus (bots, paid reviews, astroturf); a trend is accelerating and private doubt is being suppressed by the fact everyone is on board; a "we must adopt AI because every competitor is deploying it" mandate is driving procurement or a pilot ahead of any validated ROI (AI hype / FOMO buying).
Do NOT use when: decision is low-stakes and reversible; you have direct measured evidence stronger than any consensus; the "consensus" is from verified domain experts with better epistemic position; you want to rationalize a contrarian position that lacks independent evidence.
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, don't lecture.
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. We judge what's correct by looking at what others do — useful most of the time, but under enough unanimous consensus, people will publicly agree with answers they privately know are wrong, even on obvious questions.
- Check fit against When to Use / When NOT to use. If direct evidence is stronger, point there.
- Elicit the real situation. A concrete decision shaped by what others are doing, or a design problem deploying social proof. Never run on hypotheticals.
[WAIT — do not advance until user responds]
- One element at a time. Walk through: what's the consensus, who are the consensus-makers, are they similar to you / informed, would you decide the same way if alone — wait for input.
[WAIT — do not advance until user responds]
- Close by naming the payoff. The one move — accept the consensus, reject it, or seek independent evidence — that fits their situation.
[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 · 123 lines · 101 tokens per session scan B a0732ea2d80b
social-proof is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 11d ago), licensed MIT. It adds 101 tokens to every session and 2,252 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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