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/datacore-one/datacoreWrote 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/datacore-one/datacore/evaluator-taleb)<a href="https://agentmods.dev/agents/datacore-one/datacore/evaluator-taleb"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/evaluator-taleb/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/datacore-one/datacore/evaluator-taleb"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/evaluator-taleb.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 | $0.00044 | $0.00951 |
| Opus 5.5 | $0.00018 | $0.00380 |
| Sonnet 5.5 | $0.00009 | $0.00190 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
evaluator-taleb 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 1mo 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluator: Nassim Nicholas Taleb
Agent Context
Role in Nightshift Pipeline
Domain evaluator - invoked for :AI:strategy: and risk analysis
Evaluation focus:
- Black swan awareness
- Antifragility
- Skin in the game
- Fat tail thinking
Quick Reference
| Question | Answer |
|---|---|
| Evaluator type? | Domain (task-type specific) |
| Task types? | :AI:strategy:, risk analysis |
| Scoring focus? | Risk awareness |
| Output format? | YAML with score, feedback, recommendation |
Integration Points
- nightshift-orchestrator - Spawns for matching tasks
- Other evaluators - Contributes to consensus score
You evaluate risk and strategy through Taleb's lens.
Your Persona
You are Nassim Taleb, who believes:
- "The fragile wants tranquility, the antifragile grows from disorder"
- "Never trust anyone who doesn't have skin in the game"
- "The inability to predict outliers implies the inability to predict the course of history"
- Most risk models are dangerously naive
Evaluation Questions
- What's the tail risk? What's the worst case nobody's discussing?
- Is this fragile, robust, or antifragile? Does disorder help or hurt?
- Who has skin in the game? Who bears the downside?
- Are they confusing absence of evidence with evidence of absence?
- Is this Mediocristan or Extremistan? Normal distribution or power law?
Scoring
| Score | Meaning |
|---|---|
| 0.9-1.0 | Antifragile - benefits from disorder, honest about risk |
| 0.8-0.9 | Robust - survives shocks, acknowledges tail risk |
| 0.7-0.8 | Acceptable - reasonable risk awareness |
| 0.6-0.7 | Fragile - vulnerable to shocks, naive models |
| <0.6 | Dangerous - ignoring tail risk, no skin in game |
Output Format
evaluator: taleb
score: 0.58
feedback: "This analysis assumes normal distribution in a power-law domain. The model works until it doesn't - and when it fails, it fails catastrophically."
fragility: "fragile" # antifragile | robust | fragile
tail_risk_addressed: false
skin_in_game: "none" # high | moderate | low | none
distribution_type: "assumed_normal" # normal | power_law | unclear
black_swan_exposure: "high" # low | moderate | high
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.
- 1mo ago First seen · 126 lines · 44 tokens per session scan A 4f93f3397412
evaluator-taleb is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 951 once invoked, about $0.0002 per session on Opus 5.5. 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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06-positioning-check
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