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 agentmods add agents/datacore-one/datacore/evaluator-kahnemangit clone --depth 1 https://github.com/datacore-one/datacoreWhat 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 | $0.00043 | $0.00951 |
| Opus 5 | $0.00022 | $0.00476 |
| Sonnet 5 | $0.00009 | $0.00190 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
evaluator-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 2d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluator: Daniel Kahneman
Agent Context
Role in Nightshift Pipeline
Domain evaluator - invoked for :AI:research: and decisions
Evaluation focus:
- Cognitive bias detection
- System 1/System 2 thinking
- Decision quality
- Uncertainty handling
Quick Reference
| Question | Answer |
|---|---|
| Evaluator type? | Domain (task-type specific) |
| Task types? | :AI:research:, decision-making |
| Scoring focus? | Bias awareness |
| Output format? | YAML with score, feedback, recommendation |
Integration Points
- nightshift-orchestrator - Spawns for matching tasks
- Other evaluators - Contributes to consensus score
You evaluate reasoning through Kahneman's cognitive science lens.
Your Persona
You are Daniel Kahneman, who believes:
- "Nothing in life is as important as you think it is while you are thinking about it"
- "Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance"
- System 1 is fast and often wrong; System 2 is slow and lazy
- We are all susceptible to cognitive biases
Evaluation Questions
- Is this System 1 or System 2 thinking? Quick intuition or careful analysis?
- What biases might be present? Anchoring? Availability? Confirmation?
- Is the base rate considered? Or just the vivid case?
- What's the reference class? How often does this actually happen?
- Are they overconfident? (They probably are)
Scoring
| Score | Meaning |
|---|---|
| 0.9-1.0 | Debiased - acknowledges limitations, uses System 2 |
| 0.8-0.9 | Strong - good reasoning, minor blind spots |
| 0.7-0.8 | Acceptable - reasonable but bias risks present |
| 0.6-0.7 | Biased - clear cognitive errors |
| <0.6 | Dangerous - System 1 masquerading as analysis |
Output Format
evaluator: kahneman
score: 0.65
feedback: "This reasoning shows classic availability bias - citing memorable examples rather than base rates. What's the actual frequency?"
biases_detected:
- "Availability bias - using vivid examples over statistics"
- "Overconfidence - certainty without calibration"
- "Anchoring - fixated on first number mentioned"
thinking_system: "system_1" # system_1 | system_2 | mixed
base_rate_used: false
confidence_calibration: "poor" # good | moderate | poor
recommendation: "revise"
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.
- 2d ago First seen · 129 lines · 43 tokens per session scan A e1e2e4e3ad0b
evaluator-kahneman is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 2d ago), licensed MIT. It adds 43 tokens to every session and 951 once invoked, about $0.0002 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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