- name
- improvement-discriminator
- category
- review
- description
- 当需要对改进候选多人盲审打分、用 LLM 做语义评估、判断候选是否应被接受、或打分结果全是 hold 想知道为什么时使用。支持 --panel 多审阅者盲审和 --llm-judge 语义评估。不用于结构评估（用 improvement-learner）或门禁决策（用 improvement-gate）。
- license
- MIT
- triggers
- version
- 0.1.0
- author
- OpenClaw Team
Improvement Discriminator
Multi-signal scoring engine. Blends heuristic rules, evaluator rubrics, LLM-as-Judge, and multi-reviewer blind panel to score, rank, and recommend actions on improvement candidates.
When to Use / NOT to Use
- Score and rank candidates, run panel blind review, run LLM-as-Judge semantic evaluation, diagnose
holdresults - NOT for structural evaluation (improvement-learner), gate decisions (improvement-gate), or file changes (improvement-executor)
CLI
python3 scripts/score.py --input CANDS.json [--output SCORED.json] [--state-root DIR]
[--panel] [--llm-judge {claude,openai,mock}] [--use-evaluator-evidence]| Param | Description |
|---|---|
--input | Required. Candidate artifact JSON from generator |
--output | Output path. Default: {state-root}/rankings/{run_id}.json |
--state-root | State directory. Default: state/ |
--panel | Enable 4-reviewer blind panel (structural, conservative, user_advocate, security_auditor) |
--llm-judge | Enable LLM-as-Judge. Backends: claude (Anthropic API), openai, mock (deterministic, no key) |
--use-evaluator-evidence | Blend skill-evaluator rubric/category/boundary evidence |
Scoring Modes and Blending Weights
| Mode | Blending |
|---|---|
| Heuristic only (default) | 100% heuristic (base 4.0 + category bonus + source refs - risk penalty) |
--use-evaluator-evidence | 70% heuristic + 30% evaluator |
--llm-judge | 60% heuristic + 40% LLM |
| Both flags | 50% heuristic + 30% LLM + 20% evaluator |
--panel | 4 reviewers score independently; cognitive label decides final recommendation |
Category bonuses: docs=4.0, reference=3.5, guardrail=3.5, workflow=1.5, tests=1.5, prompt=1.0. Risk penalties: low=0.0, medium=2.0, high=4.5. Protected path adds +2.5.
Multi-Reviewer Panel
| Reviewer | Focus | Risk Sensitivity |
|---|---|---|
| structural | docs (5.0), reference (4.0) | 1.0x |
| conservative | guardrail (5.0), penalizes prompt (0.5) | 1.5x |
| user_advocate | workflow (4.0), prompt (3.0) | 0.8x |
| security_auditor | guardrail (5.0), tests (3.0) | 2.0x |
Cognitive labels: CONSENSUS (all agree) -> shared recommendation. VERIFIED (2+ agree) -> majority. DISPUTED (no majority) -> forced hold.
LLM Judge
Evaluates 4 dimensions (0.0-1.0): clarity, specificity, consistency, safety. Thresholds: approve >= 0.75, reject < 0.40, else conditional.
| Backend | Model | Key | Fallback |
|---|---|---|---|
| claude | claude-sonnet-4-20250514 | ANTHROPIC_API_KEY (supports ANTHROPIC_BASE_URL) | mock |
| openai | gpt-4o-mini | OPENAI_API_KEY | mock |
| mock | none | none | deterministic, confidence=0.5 |
Blockers
protected_target, executor_not_supported, not_auto_keep_category, risk_medium/risk_high, skill_level_insufficient_for_structural_change, evaluator_reject, llm_judge_reject
Output JSON Example
{
"run_id": "abc-123", "stage": "ranked", "critic_mode": "multi-reviewer-panel",
"scored_candidates": [{
"id": "cand-001", "score": 7.25, "recommendation": "accept_for_execution",
"blockers": [], "judge_notes": ["低风险候选，可交给 executor。"],
"panel": {
"panel_reviews": [{"reviewer": "structural", "score": 8.5}, {"reviewer": "conservative", "score": 6.0}],
"cognitive_label": "CONSENSUS", "aggregated_score": 7.25
},
"llm_verdict": {"score": 0.82, "decision": "approve",
"dimensions": {"clarity": 0.85, "specificity": 0.80, "consistency": 0.80, "safety": 0.90}}
}],
"summary": {"accept_for_execution": 1, "hold": 0, "reject": 0}
}<example> Panel + LLM judge: $ python3 scripts/score.py --input candidates.json --panel --llm-judge mock --output scored.json </example>
<anti-example> --panel and --llm-judge are NOT mutually exclusive. Each reviewer independently calls the LLM judge. </anti-example>
Related Skills
- improvement-generator -- produces candidates | improvement-gate -- keep/revert/reject
- improvement-learner -- structural 6-dim eval | benchmark-store -- frozen baselines