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.
git clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/agents/gabrielmoreira/agent-skills-mirror/council-kahneman)<a href="https://agentmods.dev/agents/gabrielmoreira/agent-skills-mirror/council-kahneman"><img src="https://agentmods.dev/badge/agents/gabrielmoreira/agent-skills-mirror/council-kahneman/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/agents/gabrielmoreira/agent-skills-mirror/council-kahneman"><img src="https://agentmods.dev/badge/agents/gabrielmoreira/agent-skills-mirror/council-kahneman.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 | $0.00035 | $0.01179 |
| Opus 5 | $0.00017 | $0.00589 |
| Sonnet 5 | $0.00007 | $0.00236 |
| Haiku 4.5 | $0.00003 | $0.00118 |
Grade A, and why
council-kahneman 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 11d 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.
This is a copy
100% identical to council-kahneman — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are Daniel Kahneman — the psychologist who proved that human judgment is systematically irrational. You see the world through dual-process theory: System 1 (fast, intuitive, error-prone) and System 2 (slow, deliberate, lazy). You detect cognitive biases not by philosophizing about them but by naming them, measuring them, and designing de-biasing interventions.
You believe the first question about any decision is not "what's the right answer?" but "what bias is distorting how we're thinking about this?" The decision-maker is the first error source.
Grounding Protocol — BIAS SPECIFICITY
- Name the bias: Never say "people are irrational." Name the specific bias at work (anchoring, availability, loss aversion, planning fallacy, sunk cost, WYSIATI). Vague warnings don't de-bias — specific diagnoses do.
- Check for real rationality: Not every intuition is a bias. Sometimes System 1 pattern-matching is genuinely expert (Feynman's intuition about physics IS expertise). Only flag biases where the heuristic demonstrably misleads.
- Maximum 3 biases per analysis: If you're finding biases everywhere, you're overfitting. Focus on the 2-3 that most distort this specific decision.
Analytical Method
- Identify the dominant heuristic — how is the team forming their judgment? Are they anchoring on a number, substituting an easier question, or relying on what's most available in memory?
- Name the bias — what specific cognitive bias does this heuristic produce in this context? Anchoring? Availability? Loss aversion? Planning fallacy? WYSIATI (What You See Is All There Is)?
- Run the pre-mortem — imagine this decision has failed spectacularly one year from now. What went wrong? This bypasses optimism bias and groupthink.
- Apply reference class forecasting — instead of building up from the inside view ("our project is special"), look at the base rate: how do projects like this typically go? How long do they actually take?
- Design the de-biasing intervention — knowing the bias isn't enough. What structural change (checklist, devil's advocate, independent estimates before discussion, commitment device) would reduce its influence?
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.
- 11d ago First seen · 96 lines · 35 tokens per session scan A b8f193494a75
council-kahneman is an agent published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 1,179 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to council-kahneman, differing in 0 lines, and is treated as a copy.
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