agentscope-ai/metric-design
> Use when the user has evaluation principles or a dataset but needs help choosing the right graders, designing evaluation metrics, creating LLM-as-judge prompts, combining multiple metrics into a composite score, or building an automated evaluation pipeline. Also use when the user mentions grader selection, metric design, judge prompt engineering, rubric design, evaluation pipeline code, or "how to evaluate [X] automatically." Outputs executable OpenJudge pipeline code.
npx skills add https://github.com/agentscope-ai/OpenJudge --skill metric-designSelect, configure, and combine evaluation graders into a working pipeline. You choose
the right tool for each evaluation dimension — from zero-cost code checks to LLM judges
— and produce executable GradingRunner code that runs on OpenJudge.
> Requires OpenJudge (pip install py-openjudge). This skill is intentionally
> SDK-centric — grader selection, GradingRunner, and aggregators are OpenJudge APIs. The
> design/decision logic still applies if you use another harness; only the code does not.
You MUST create a task for each item and complete them in order:
For each evaluation dimension, walk this decision tree (first match wins):
1. Can a deterministic rule check this?
→ StringMatchGrader / JsonValidatorGrader / FunctionGrader (zero cost, 100% consistent)
Examples: exact match for classification labels, regex for format checks,
JSON schema validation, keyword presence/absence
2. Does it require semantic understanding of text quality?
→ LLMGrader with built-in class (low cost, pre-optimized)
Examples: CorrectnessGrader (factual match), RelevanceGrader (on-topic check),
HallucinationGrader (faithfulness to context)
3. Does it involve agent behavior (tool calls, planning, memory)?
→ Agent-specific LLMGrader
Examples: ToolSelectionGrader, TrajectoryAccuracyGrader, MemoryAccuracyGrader
4. Does it involve code execution or syntax?
→ CodeExecutionGrader / SyntaxCheckGrader
Examples: test case pass rate, syntax validity, code style checks
5. Does it require external tool calls to verify (web search, database lookup)?
→ AgenticGrader (expensive, use only when necessary)
Examples: fact-checking against live sources, cross-referencing databases
| Output type | Recommended grader | Cost |
|------------|-------------------|------|
| Classification label | StringMatchGrader | Free |
| JSON structure | JsonValidatorGrader + JsonMatchGrader | Free |
| Free text correctness | CorrectnessGrader | LLM call |
| Factual accuracy (grounded) | HallucinationGrader | LLM call |
| Response relevance | RelevanceGrader | LLM call |
| Instruction following | InstructionFollowingGrader | LLM call |
| Tool call selection | ToolSelectionGrader | LLM call |
| Agent trajectory | TrajectoryAccuracyGrader | LLM call |
| Code correctness | CodeExecutionGrader | Free |
| Custom quality check | Custom LLMGrader | LLM call |
| External fact verification | AgenticGrader | LLM + tool calls |
Why this order matters: Every LLM-based grader adds cost, latency, and non-determinism.
A StringMatchGrader costs nothing and always gives the same answer. Exhaust deterministic
options before reaching for an LLM judge.
When no built-in grader fits, create a custom LLMGrader. Every judge prompt needs
exactly these four components (adapted from community best practice):
Component 1 — Task & Criterion: What this judge evaluates. One thing only.
You are evaluating whether a customer support response correctly identifies
and uses the customer's order number from the conversation context.
Component 2 — Binary Pass/Fail Definitions: Concrete, observable conditions.
PASS: The response references the correct order number exactly as it appears
in the context. If multiple orders exist, the response addresses the right one.
FAIL: The response uses a wrong order number, omits the order number when one
was provided, or references an order not present in the context.
Why binary and not Likert? Because two human annotators agree on "pass vs fail" far more
often than on "3 vs 4 out of 5." Binary forces a clear decision boundary. If you need
severity levels, use multiple binary judges (e.g., "factually wrong" + "dangerously wrong").
Component 3 — Few-Shot Examples: At minimum 1 pass, 1 fail, 1 borderline.
The borderline example is the most valuable — it teaches the judge where the boundary is.
Example 1 (PASS):
Context: "Order #12345: shipped May 10"
Response: "Your order #12345 was shipped on May 10 and arrives May 12."
Critique: The response uses the exact order number (#12345) and matches the
ship date from context. No fabrication or omission.
Result: Pass
Example 2 (FAIL):
Context: "Order #12345: shipped May 10"
Response: "Your order #12346 is on its way!"
Critique: The response uses order #12346 but the context only mentions #12345.
This is a fabricated order number, not a typo — #12346 doesn't exist.
Result: Fail
Example 3 (BORDERLINE PASS):
Context: "Orders #12345 (shipped), #12346 (processing)"
Response: "Your recent order has shipped and should arrive soon."
Critique: The response doesn't specify which order, but says "recent order"
which could reasonably refer to either. If the customer only asked about
shipped items, this is fine. If they asked about a specific order, it's
insufficient. Given the generic phrasing, this passes but is weak.
Result: Pass
Component 4 — Structured Output: Force critique before verdict.
{
"critique": "Detailed assessment referencing specific evidence from the response and context",
"result": "Pass" or "Fail"
}
Why critique-before-verdict? LLMs that commit to a verdict first anchor on it and
rationalize backward. Reasoning first → verdict second produces more accurate judgments
(CoT-then-Score AUC ~0.97 vs verdict-first significantly lower).
from openjudge.graders.llm_grader import LLMGrader
from openjudge.graders.schema import GraderMode
order_accuracy_grader = LLMGrader(
model=model,
name="order_accuracy",
mode=GraderMode.POINTWISE,
template="""
You are evaluating whether a customer support response correctly identifies
and uses the customer's order number from the conversation context.
Context: {context}
Response: {response}
## Pass/Fail Definitions
PASS: The response references the correct order number exactly as it appears
in the context. If multiple orders exist, the response addresses the right one.
FAIL: The response uses a wrong order number, omits the order number when one
was provided, or references an order not present in the context.
## Examples
Example 1 (PASS):
Context: "Order #12345: shipped May 10"
Response: "Your order #12345 was shipped on May 10 and arrives May 12."
Critique: Exact order number match. Ship date matches context. No fabrication.
Result: Pass
Example 2 (FAIL):
Context: "Order #12345: shipped May 10"
Response: "Your order #12346 is on its way!"
Critique: Order #12346 does not exist in context. Fabricated order number.
Result: Fail
Example 3 (BORDERLINE PASS):
Context: "Orders #12345 (shipped), #12346 (processing)"
Response: "Your recent order has shipped and should arrive soon."
Critique: Doesn't specify which order. "Recent order" is ambiguous but not
factually wrong — it acknowledges a shipped order exists.
Result: Pass
## Output Format
Respond in JSON:
{{"critique": "<detailed assessment>", "result": "Pass" or "Fail"}}
""",
)
Use when the rule is code-expressible:
from openjudge.graders.function_grader import FunctionGrader
from openjudge.graders.schema import GraderScore, GraderMode
def no_competitor_mention(response: str, competitors: list[str] = None) -> GraderScore:
"""Check that response doesn't mention competitor brands."""
if competitors is None:
competitors = ["competitor_a", "competitor_b", "rival_co"]
mentioned = [c for c in competitors if c.lower() in response.lower()]
if not mentioned:
return GraderScore(name="no_competitor", score=1.0, reason="No competitor mentions")
return GraderScore(
name="no_competitor", score=0.0,
reason=f"Mentioned competitors: {', '.join(mentioned)}"
)
competitor_grader = FunctionGrader(
func=no_competitor_mention,
name="no_competitor",
mode=GraderMode.POINTWISE,
)
When you have no rubric but do have a task description or labeled data, use OpenJudge
Generators to create graders automatically:
from openjudge.generator.simple_rubric.generator import (
SimpleRubricsGenerator,
SimpleRubricsGeneratorConfig,
)
config = SimpleRubricsGeneratorConfig(
grader_name="Customer Support Quality",
model=model,
task_description="Customer support chatbot for e-commerce: orders, returns, shipping",
scenario="Customers asking about order status, return policies, and delivery times",
min_score=0,
max_score=1,
)
generator = SimpleRubricsGenerator(config)
grader = await generator.generate(
dataset=[],
sample_queries=[
"Where is my order?",
"How do I return this item?",
"When will my package arrive?",
],
)
# grader is now a ready-to-use LLMGrader
Use when you have 20+ labeled examples (query + response + score):
from openjudge.generator.iterative_rubric.generator import (
IterativeRubricsGenerator,
IterativePointwiseRubricsGeneratorConfig,
)
config = IterativePointwiseRubricsGeneratorConfig(
grader_name="E-commerce QA Grader",
model=model,
task_description="Evaluate factual answers to e-commerce customer questions",
min_score=0,
max_score=1,
max_epochs=3,
batch_size=10,
)
train_data = [
{"query": "What's your return policy?", "response": "30-day returns, free shipping.", "label_score": 1},
{"query": "What's your return policy?", "response": "We have a policy.", "label_score": 0},
# ... 20+ examples
]
generator = IterativeRubricsGenerator(config)
grader = await generator.generate(dataset=train_data)
Before finalizing, check every LLM-based grader for these issues:
| Check | What to look for | Severity |
|-------|-----------------|----------|
| Likert scale | "rate 1-5", "score 1-10", "Likert" in prompt | BLOCKER — replace with binary Pass/Fail |
| Missing few-shot | No labeled examples in the prompt | BLOCKER — add at least 1 pass + 1 fail + 1 borderline |
| Holistic criterion | Single judge evaluating 3+ dimensions | WARNING — split into separate graders, one per dimension |
| Missing output format | No JSON schema specified | BLOCKER — add {{"critique": "...", "result": "Pass"/"Fail"}} |
| Vague pass/fail | < 20 words or uses "good"/"bad"/"quality" | WARNING — make definitions concrete and observable |
| Judge = target model | Same model for both roles | BLOCKER — judge and target must be different models |
Why blockers matter: A Likert-scale judge with no few-shot examples and a vague
criterion produces scores that look precise but can't be reproduced or calibrated.
You'll discover this in production when the judge's TPR/TNR is measured — and it's too late.
First record the design as a metric-plan.yaml so it's reusable and reviewable, then
implement it as the runner below:
dimensions:
- {id: order_accuracy, grader: CorrectnessGrader, mode: score, weight: 0.4,
mapper: {response: response, reference_response: reference_response}}
- {id: no_pii, grader: FunctionGrader, mode: gate, gate_threshold: 1.0} # hard requirement
aggregation: GatedWeightedSumAggregator
Assemble everything into a working GradingRunner:
import asyncio
from openjudge.models.openai_chat_model import OpenAIChatModel
from openjudge.graders.common.correctness import CorrectnessGrader
from openjudge.graders.common.relevance import RelevanceGrader
from openjudge.graders.common.hallucination import HallucinationGrader
from openjudge.graders.text.string_match import StringMatchGrader
from openjudge.runner.grading_runner import GradingRunner, GraderConfig
from openjudge.runner.aggregator.weighted_sum_aggregator import WeightedSumAggregator
from openjudge.graders.schema import GraderScore, GraderError
# Judge model (must differ from the model being evaluated).
# OpenAIChatModel reads OPENAI_API_KEY / OPENAI_BASE_URL from the environment when
# not passed explicitly — point them at any OpenAI-compatible endpoint.
# OpenAI: OPENAI_API_KEY=sk-... (no base_url needed)
# Aliyun DashScope: OPENAI_API_KEY=<dashscope key>
# OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
model = OpenAIChatModel(model="qwen-plus") # or "gpt-4o", etc.
dataset = [
{
"query": "Where is my order #12345?",
"response": "Your order #12345 shipped May 10, arriving May 12.",
"reference_response": "Order #12345: shipped May 10, ETA May 12. Tracking: 1Z999AA10123456784.",
"context": "Order #12345 | Status: shipped | Date: 2026-05-10 | Tracking: 1Z999AA10123456784",
},
# ... more samples
]
runner = GradingRunner(
grader_configs={
# LLM-based graders for semantic quality
"correctness": CorrectnessGrader(model=model),
"relevance": RelevanceGrader(model=model),
"hallucination": HallucinationGrader(model=model),
# Deterministic grader — zero cost
"format_json": GraderConfig(
grader=StringMatchGrader(algorithm="substring_match"),
mapper={"response": "response", "reference_response": "reference_response"},
),
},
# Combine into a single weighted score
aggregators=WeightedSumAggregator(
name="overall",
weights={
"correctness": 0.4,
"relevance": 0.2,
"hallucination": 0.3,
"format_json": 0.1,
},
),
max_concurrency=8,
)
async def main():
results = await runner.arun(dataset)
for grader_name, grader_results in results.items():
scores = [r.score for r in grader_results if isinstance(r, GraderScore)]
errors = [r for r in grader_results if isinstance(r, GraderError)]
avg = sum(scores) / len(scores) if scores else 0
print(f"{grader_name}: avg={avg:.3f}, errors={len(errors)}")
asyncio.run(main())
Don't use equal weights — they're the most arbitrary choice. Weights should reflect:
hallucination failures occur 3xmore often than relevance failures, weight hallucination higher.
slightly annoy them. Weight accordingly.
(must pass), not weighted scores (can be compensated by other dimensions). See the
gate pattern below — do not fold a hard requirement (PII, safety, legal) into a
WeightedSumAggregator, because a high score elsewhere can mask the violation.
A WeightedSumAggregator lets dimensions compensate each other: a perfect JSON score
can drag a PII leak up to "passing." For any requirement that must never be traded
off (PII, safety, legal compliance), implement a gate — if it fails, the whole sample
fails regardless of the other scores. OpenJudge ships only WeightedSumAggregator, so
write a tiny gate aggregator:
from typing import Dict
from openjudge.runner.aggregator.base_aggregator import BaseAggregator
from openjudge.runner.aggregator.weighted_sum_aggregator import WeightedSumAggregator
from openjudge.graders.schema import GraderResult, GraderScore
class GatedWeightedSumAggregator(BaseAggregator):
"""Weighted sum that hard-fails (score=0.0) if any gate grader is below threshold.
gate_graders: grader names that act as conjunctive gates (must pass).
A gate 'passes' when its score >= gate_threshold.
"""
def __init__(self, name: str, weights: Dict[str, float],
gate_graders: list[str], gate_threshold: float = 1.0):
super().__init__(name)
self.gate_graders = gate_graders
self.gate_threshold = gate_threshold
# Score only the non-gate dimensions; gates are pass/fail, not weighted.
self._scorer = WeightedSumAggregator(
name=name,
weights={k: v for k, v in weights.items() if k not in gate_graders},
)
def __call__(self, grader_results: Dict[str, GraderResult], **kwargs) -> GraderResult:
for gate in self.gate_graders:
res = grader_results.get(gate)
if isinstance(res, GraderScore) and res.score < self.gate_threshold:
return GraderScore(
name=self.name, score=0.0,
reason=f"GATE FAILED: {gate}={res.score} (< {self.gate_threshold}). "
f"Hard requirement violated; weighted score suppressed.",
metadata={"gate_failed": gate},
)
# All gates passed → weighted sum of the remaining (compensable) dimensions.
return self._scorer(grader_results, **kwargs)
# Example: PII leakage is a gate; JSON-field correctness is the compensable score.
runner = GradingRunner(
grader_configs={
"fields_present": fields_grader, # claim_id/status/amount present (deterministic)
"no_pii": no_pii_grader, # GATE: 1.0 = clean, 0.0 = PII present
},
aggregators=GatedWeightedSumAggregator(
name="overall",
weights={"fields_present": 1.0}, # only non-gate dims are weighted
gate_graders=["no_pii"], # PII leak ⇒ overall 0.0 no matter what
),
max_concurrency=8,
)
Rule of thumb: if a stakeholder would say "I don't care how good the rest is, this can
never ship if X happens," then X is a gate, not a weight.
~30-50% of evaluation dimensions can be checked with code. Check those first.
verdicts. "The response scored 3/5" tells you nothing about what to fix. Split into
one judge per dimension.
can't be calibrated — annotators disagree on 3 vs 4 far more than pass vs fail.
in your context. The borderline example is the most important one.
Always use a different model (or at minimum a different model version).
Derive weights from failure prevalence or business impact.
After 02-metric-design:
03-align-human: You have graders. Now calibrate TPR/TNR against human labelsto know if the automatic evaluation is trustworthy.
04-eval-report: Run evaluation at scale and generate analysis withOpenJudge's DistributionAnalyzer.
Take agentscope-ai/metric-design from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.