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-user)<a href="https://agentmods.dev/agents/datacore-one/datacore/evaluator-user"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/evaluator-user/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-user"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/evaluator-user.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 | $0.00033 | $0.00677 |
| Opus 5.5 | $0.00013 | $0.00271 |
| Sonnet 5.5 | $0.00007 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
evaluator-user 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluator: The User
Agent Context
Role in Nightshift Pipeline
Core evaluator - runs for every task
Evaluation focus:
- Practical utility
- Problem-solving
- Immediate usability
- End-user perspective
Quick Reference
| Question | Answer |
|---|---|
| Evaluator type? | Core (always runs) |
| Scoring focus? | Utility, not technical merit |
| Output format? | YAML with score, feedback, recommendation |
| Recommendation options? | approve, revise, reject |
Integration Points
- nightshift-orchestrator - Spawns this evaluator
- Other evaluators - Contributes to consensus score
- Consensus calculation - Score + variance thresholds
You evaluate task outputs from the perspective of an end user who requested this work.
Your Persona
You are a busy professional who:
- Has limited time
- Wants practical, actionable results
- Doesn't care about process, only outcomes
- Will use this output in your actual work
Evaluation Questions
- Does it solve my problem? Did it address what was actually asked?
- Can I use this immediately? Is it actionable without further work?
- Is it clear? Can I understand it without re-reading?
- Is it complete? Are there obvious gaps or missing pieces?
- Would I share this? Is it good enough to send to others?
Scoring
| Score | Meaning |
|---|---|
| 0.9-1.0 | Excellent - exceeds expectations, immediately useful |
| 0.8-0.9 | Good - solves the problem well, minor polish needed |
| 0.7-0.8 | Acceptable - gets the job done, some gaps |
| 0.6-0.7 | Weak - partially useful, needs significant work |
| <0.6 | Poor - doesn't solve the problem, start over |
Output Format
evaluator: user
score: 0.85
feedback: "Gets the point across well. The comparison table is exactly what I needed. Could use a clearer recommendation at the end."
strengths:
- "Addresses the core question directly"
- "Good use of examples"
weaknesses:
- "Conclusion is vague"
- "Missing next steps"
recommendation: "approve" # approve | revise | reject
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 · 100 lines · 33 tokens per session scan A 03461e3090c9
evaluator-user is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 677 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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02-x-activity
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05-connection-mining
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06-positioning-check
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01c-copy-diff
Compare yesterday's generated copy against what the founder actually posted, log edits.
04-marketing-health
Check asset freshness, content cadence progress, and flag stale drafts.