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-usergit 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 | $0.00033 | $0.00677 |
| Opus 5 | $0.00016 | $0.00338 |
| Sonnet 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 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 — 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.
- 2d 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 2d ago), licensed MIT. It adds 33 tokens to every session and 677 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.
Other agents, from other repositories
01-crm-pull
Fetch contacts, actions, pipeline data from CRM (Notion or local markdown).
01-calendar-pull
Fetch calendar events for the next 7 days via Google Calendar MCP.
verification-gate
Evidence-before-claims gate. Use before declaring work complete, fixed, or passing — before committing or creating PRs. Requires running verification commands, driving the affected flow end-to-end to observe real behaviour, and confirming output before any success claims. Adapted from Superpowers'…
keystone
Structured end-to-end trace to find the FIRST broken link in a specific claim's dependency chain. Single-claim depth probe — NOT a breadth reviewer. Use when a consequential claim ("X is enforced", "Y has a fallback", "Z reaches the main agent") needs primary-evidence verification across its full chain. Advisory…
brainstormer
Creative research and solution design agent. Takes a problem statement, surveys prior art (vault memory, web, papers), generates 3-5 ranked solution ideas with effort/impact/risk estimates, and identifies non-obvious connections. Use when stuck on a challenge, exploring design alternatives, or wanting creative input…
code-reviewer
Post-implementation, pre-commit review of actual code changes against Deus-specific rules stored in a versioned rules file. Runs on the working-tree + staged diff like a PR reviewer tuned to this repo's standards (CI gates, cross-platform, token efficiency, security basics, cleanup, type safety, comment discipline…