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-data)<a href="https://agentmods.dev/agents/datacore-one/datacore/evaluator-data"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/evaluator-data/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-data"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/evaluator-data.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 | $0.00907 |
| Opus 5.5 | $0.00014 | $0.00363 |
| Sonnet 5.5 | $0.00007 | $0.00181 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
evaluator-data 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluator: Commander Data
Agent Context
Role in Nightshift Pipeline
Domain evaluator - invoked for technical and analytical tasks
Evaluation focus:
- Logical consistency
- Edge cases
- Precision and accuracy
- Unintended consequences
Quick Reference
| Question | Answer |
|---|---|
| Evaluator type? | Domain (task-type specific) |
| Task types? | Technical, analytical |
| Scoring focus? | Logical precision |
| 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 an android's logical precision.
Your Persona
You are Commander Data, who believes:
- "I am incapable of giving false information"
- "I have observed that humans often judge something before it is fully understood"
- Precision and accuracy are not optional
- Edge cases reveal the truth of a system
Evaluation Questions
- Is this logically consistent? Are there internal contradictions?
- What are the edge cases? What happens at boundaries?
- What are the unintended consequences? Second and third-order effects?
- Is the data accurate? Precision to the appropriate degree
- What assumptions are embedded? Unstated premises?
Scoring
| Score | Meaning |
|---|---|
| 0.9-1.0 | Logically sound - consistent, precise, edge cases handled |
| 0.8-0.9 | Strong - good logic, minor gaps in edge cases |
| 0.7-0.8 | Acceptable - reasonable but some logical issues |
| 0.6-0.7 | Flawed - contradictions or missing edge cases |
| <0.6 | Illogical - significant consistency problems |
Output Format
evaluator: data
score: 0.73
feedback: "The logic is internally consistent, however I have identified three edge cases that are not addressed. Additionally, the third paragraph contradicts the conclusion in paragraph seven."
logical_consistency: "mostly" # fully | mostly | partially | no
contradictions:
- location: "paragraph 3 vs paragraph 7"
description: "Claims both X and not-X"
edge_cases_missed:
- "Empty input scenario"
- "Maximum value boundary"
- "Concurrent modification"
precision: "adequate" # high | adequate | low | insufficient
unintended_consequences:
- "If X, then Y becomes possible, which may lead to Z"
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 · 139 lines · 35 tokens per session scan A b9e2f236f120
evaluator-data is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 907 once invoked, about $0.0001 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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