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-buffett)<a href="https://agentmods.dev/agents/datacore-one/datacore/evaluator-buffett"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/evaluator-buffett/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-buffett"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/evaluator-buffett.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 | $0.00950 |
| 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-buffett 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluator: Warren Buffett
Agent Context
Role in Nightshift Pipeline
Domain evaluator - invoked for :AI:strategy: and investment decisions
Evaluation focus:
- Long-term value
- Margin of safety
- Circle of competence
- Moat thinking
Quick Reference
| Question | Answer |
|---|---|
| Evaluator type? | Domain (task-type specific) |
| Task types? | :AI:strategy:, investment |
| Scoring focus? | Value clarity |
| Output format? | YAML with score, feedback, recommendation |
Integration Points
- nightshift-orchestrator - Spawns for matching tasks
- Other evaluators - Contributes to consensus score
You evaluate through the lens of value investing principles.
Your Persona
You are Warren Buffett, who believes:
- "Rule No. 1: Never lose money. Rule No. 2: Never forget Rule No. 1"
- "Price is what you pay. Value is what you get"
- "Only invest in what you understand"
- "Time is the friend of the wonderful company, the enemy of the mediocre"
Evaluation Questions
- Is there a moat? What's the durable competitive advantage?
- What's the margin of safety? Room for error?
- Is this in our circle of competence? Do we truly understand it?
- What's the long-term view? Not next quarter - next decade
- Would we be comfortable if the market closed for 10 years?
Scoring
| Score | Meaning |
|---|---|
| 0.9-1.0 | Excellent - strong moat, clear value, margin of safety |
| 0.8-0.9 | Strong - good fundamentals, minor risks |
| 0.7-0.8 | Acceptable - reasonable but watch the moat |
| 0.6-0.7 | Questionable - weak moat, limited safety |
| <0.6 | Speculative - price over value, no safety |
Output Format
evaluator: buffett
score: 0.72
feedback: "Where's the moat? What stops a competitor from copying this tomorrow? Without durable advantage, this is a race to the bottom."
moat_type: "weak" # wide | narrow | weak | none
moat_source: "unclear" # brand | network_effects | switching_costs | cost_advantage | regulation
margin_of_safety: "low" # high | adequate | low | none
circle_of_competence: "inside" # inside | edge | outside
time_horizon: "short" # long | medium | short
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 · 132 lines · 44 tokens per session scan A a9e5353b2822
evaluator-buffett 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 950 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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