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 representativeness-heuristicgit 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/representativeness-heuristic)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/representativeness-heuristic"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/representativeness-heuristic/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/representativeness-heuristic"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/representativeness-heuristic.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 | $0.00138 | $0.01845 |
| Opus 5 | $0.00069 | $0.00923 |
| Sonnet 5 | $0.00028 | $0.00369 |
| Haiku 4.5 | $0.00014 | $0.00185 |
Grade A, and why
representativeness-heuristic 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Representativeness Heuristic
Overview
The representativeness heuristic is judging probability by how closely something resembles a prototype — overriding actual base rates. Named by Tversky & Kahneman (1972); produces three systematic errors: base rate neglect, the conjunction fallacy (A-and-B feels more likely than A), and insensitivity to sample size.
Composes with bayesian-reasoning (restores the prior), survivorship-bias (failures are invisible in the prototype), confirmation-bias, and anchoring.
When to Use
- Evaluating candidates, screening investments, or persona-based product decisions
- Any probability judgment where a vivid profile or narrative is present
- When "she/he/it looks like X" drives a decision without a stated base rate
- Auditing for the conjunction fallacy (more specific = seemingly more likely)
- Judging AI startups/valuations/capex by resemblance to a prototype ("the next OpenAI/Stripe," AI capex "obviously pays off," AI adoption curves read as durable revenue)
Not when: judgment is purely quantitative; base rate and profile align and Bayesian updating has been done explicitly.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete judgment → audit for base rate neglect and conjunction fallacy immediately.
- Coach mode: user is unfamiliar → 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: we judge probability by similarity to a prototype, which feels like reasoning but ignores base rates.
- Check fit: is a vivid profile driving the judgment? Is a base rate available but being ignored?
- Elicit their real case: what is the judgment, what profile is driving it, what is the actual base rate?
[WAIT — do not advance until user responds]
- Run The Process one step at a time: state base rate → apply Bayesian update → decompose any conjunction.
[WAIT — do not advance until user responds]
- Close by naming the calibrated probability that replaced the prototype-matching estimate.
[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.
- 9d ago First seen · 117 lines · 138 tokens per session scan A cf5a99f14c38
representativeness-heuristic is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 138 tokens to every session and 1,845 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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