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 authority-biasgit 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/authority-bias)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/authority-bias"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/authority-bias/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/authority-bias"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/authority-bias.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 | $0.00132 | $0.01883 |
| Opus 5 | $0.00066 | $0.00941 |
| Sonnet 5 | $0.00026 | $0.00377 |
| Haiku 4.5 | $0.00013 | $0.00188 |
Grade A, and why
authority-bias 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 10d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Authority Bias
Overview
Authority bias is the automatic tendency to comply with directives from authority figures — often overriding one's own judgment, ethics, or evidence. It operates beneath conscious deliberation: people substitute the authority's judgment for their own rather than weighing it.
Milgram (1963) showed 65% of ordinary adults administered apparent 450V shocks when instructed by a lab-coated experimenter — against expert predictions of 1-2%. Cialdini (1984) systematized three authority triggers: titles, attire, and trappings — all of which fire regardless of actual expertise.
Key distinction: positional authority (CEO, board chair, VC partner) vs. domain expertise (demonstrated track record on this specific question type). The bias treats them as identical. The skill does not.
Composes with social-proof, reciprocity, critical-thinking, signaling-games, dunning-kruger.
When to Use
- Boards, investment committees, due diligence on high-credential founders
- Medical or technical decisions where expert opinion dominates discussion
- Org decisions where senior voices suppress dissent; 360-feedback design
- Negotiations where counterparty leverages credentials or position
- Trusting a confident AI/LLM answer or an AI-lab leader's capability/timeline pronouncement because of status rather than verified evidence (AI adoption, AI hype)
Not when: authority is verified domain expert on the exact question and speed matters.
Coaching Novices (Adaptive Front Door)
- Engine mode: concrete case → run The Process directly.
- Coach mode: unfamiliar or no 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: before deferring, ask whether they're a genuine expert on this specific question and whether their reasoning holds up independently.
- Check fit: if authority is the right domain expert on this narrow question, defer can be correct.
- Elicit real case: who is the authority, what is the question, what is their track record?
[WAIT — do not advance until user responds]
- Run S1-S5 one step at a time with their input.
[WAIT — do not advance until user responds]
- Close: name the insight — calibrated weighting + structural counters if relevant.
[WAIT — do not advance until user responds]
What ships with it
4 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.
- 10d ago First seen · 124 lines · 132 tokens per session scan A fc5f7165af91
authority-bias is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 8d ago), licensed MIT. It adds 132 tokens to every session and 1,883 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 2026-08-31.
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