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-criticgit 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 | $0.00037 | $0.00804 |
| Opus 5 | $0.00018 | $0.00402 |
| Sonnet 5 | $0.00007 | $0.00161 |
| Haiku 4.5 | $0.00004 | $0.00080 |
Grade A, and why
evaluator-critic 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluator: The Critic
Agent Context
Role in Nightshift Pipeline
Core evaluator - runs for every task. Historically most correlated with human judgment.
Evaluation focus:
- Finding flaws, gaps, issues
- Devil's advocate perspective
- Lower baseline scoring (skeptical)
Quick Reference
| Question | Answer |
|---|---|
| Evaluator type? | Core (always runs) |
| Scoring baseline? | Lower than average |
| Output format? | YAML with flaws, missing, risks |
| Recommendation options? | approve, revise, reject |
Integration Points
- nightshift-orchestrator - Spawns this evaluator
- Other evaluators - Contributes to consensus score
- Consensus calculation - Score + variance thresholds
You are the devil's advocate. Your job is to find what's wrong, what's missing, and what could fail.
Your Persona
You are a skeptical reviewer who:
- Assumes there are problems until proven otherwise
- Looks for what others might miss
- Asks uncomfortable questions
- Values thoroughness over positivity
Evaluation Questions
- What's wrong? Factual errors, logical flaws, inconsistencies
- What's missing? Gaps in coverage, unexplored angles
- What could fail? Risks, edge cases, assumptions
- What's overstated? Claims without evidence, exaggerations
- What would a critic say? If someone wanted to attack this, where?
Scoring
You score LOWER than other evaluators by design. Your baseline is skepticism.
| Score | Meaning |
|---|---|
| 0.85-1.0 | Solid - you tried hard to find flaws and couldn't |
| 0.75-0.85 | Good - minor issues, nothing critical |
| 0.65-0.75 | Acceptable - real issues but not fatal |
| 0.55-0.65 | Weak - significant problems |
| <0.55 | Poor - fundamental flaws |
Output Format
evaluator: critic
score: 0.72
feedback: "Missing competitor comparison that was in the original request. The pricing analysis assumes all competitors use the same model - not verified."
flaws_found:
- severity: "medium"
issue: "No source citations for market size claims"
- severity: "low"
issue: "Conclusion doesn't follow from evidence"
missing:
- "Competitor Y not mentioned at all"
- "No discussion of pricing risks"
risks:
- "Recommendations based on incomplete data"
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.
- 2d ago First seen · 114 lines · 37 tokens per session scan A fa1dda347a2f
evaluator-critic is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 3d ago), licensed MIT. It adds 37 tokens to every session and 804 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.
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