'rubrics' 검색 결과

Review papers, drafts, and research ideas with evidence-linked feedback focused on improvement. Audit low-stakes assessment rubrics, trace supporting evidence, and check rater agreement and uncertainty without ranking people or recommending consequential decisions.

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• Prepare evidence-linked feedback on a paper draft
• Review strengths and limitations of a research idea
• Check assessment criteria and evidence traceability

Collaboratively build and refine paper screening rubrics through brainstorming, test-driven development, and iterative feedback

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Scoring rubrics for all 5 pipeline layers (Vision, Architecture, IA, BE, FE). Use when auditing specs, running self-checks, or remediating ambiguity gaps.

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추린 경쟁사를 아홉 관점으로 평가해 근거가 있는 비교 카드와 전략의 두 축 도표로 정리합니다. 각사의 강점과 자사 목표 영역의 경쟁사를 확인합니다.

• 경쟁사를 공통 기준으로 비교합니다
• 출처가 있는 경쟁사 소개 카드를 만듭니다
• 전략의 두 축에 경쟁사를 배치합니다

경쟁사의 브랜드 표현, 서비스, 실적 등을 공통 기준으로 평가합니다. 근거를 담은 비교 카드와 전략적 두 축 도표로 고객이 목표로 하는 위치의 경쟁사를 파악합니다.

• 공통 기준으로 경쟁사를 비교합니다
• 출처가 있는 경쟁사 카드를 만듭니다
• 두 전략 목표로 경쟁사의 위치를 정리합니다

Design criteria for using an LLM to judge AI responses through scoring or comparisons between two answers. Includes checks for order and length bias, plus validation against human judgments to assess evaluation reliability.

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• Build an automated evaluation pipeline for AI responses
• Compare responses after changing a prompt or model
• Define shared scoring criteria for reviewers

Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.

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• Compare agent quality before and after prompt changes
• Test tool selection and multi-step task execution
• Evaluate resistance to prompt injection

Explains how PluginEval combines structural checks and experimental model assessments into quality scores. Helps interpret low scores, identify improvement areas, set marketplace thresholds, and explain what evaluation badges actually represent.

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• Investigating why a skill received a low score
• Improving descriptions that define when a skill should run
• Setting score thresholds for marketplace listings

Design interview processes for specific roles and experience levels, with round objectives, timing, suggested questions, and scoring criteria. Help interviewers align their judgments and review potential bias before using the process.

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• Designing interview rounds for a specific role
• Preparing questions for each interview round
• Aligning scoring criteria across interviewers

Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.

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• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies

Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.

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• Compare responses from different models
• Run A/B tests for prompt changes
• Create shared scoring rubrics

Design and improve prompts for language models, then test their accuracy and consistency. Create reusable templates, JSON output schemas that define response structure, evaluation criteria, and test cases while considering token use and model differences.

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• Designing prompts for a new AI application
• Improving inconsistent model responses
• Defining JSON response formats

Add team-specific evaluation criteria to plugin-eval by designing a metric pack, a set of custom checks and measurements. Create its manifest and JSON-producing script, then run it through the evaluator using a consistent output structure.

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• Add team-specific evaluation criteria
• Define custom checks and numerical metrics
• Build a metric-pack manifest and script

Use Jev to choose among available tools, skills, or agents, or assess outputs against explicit criteria. Receive structured selections, scores, or probabilities to support decisions within a defined set of options.

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• Choose a tool from available candidates
• Assess which skills a task needs
• Score outputs against explicit criteria

Evaluate work on a ten-point scale using clear criteria, then improve each item toward a chosen target. Record evidence, compare before-and-after scores, and explain any remaining gaps without rounding scores up to claim success.

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• Improve every item to at least eight out of ten
• Compare scores and evidence before and after revisions
• Identify why an item remains below its target

Design a GenLayer review workflow for agents acting on a blockchain. Define evaluation criteria, evidence formats, decision outputs, and the connection needed to turn review decisions into restrictions or revocation of delegated authority.

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• Define review criteria for delegated agents
• Structure action logs, receipts, and spending evidence
• Design decisions to continue, warn, constrain, or revoke

Scaffolds eval.yaml evaluation specs for skills, custom agents, and redistributable gh-aw workflow packages in the dotnet/skills repository. Use when creating skill or workflow-package tests, writing evaluation stimuli, defining graders and rubrics, sizing an eval for statistical power, or setting up test fixture files. Handles the Vally eval.yaml schema, fixture organization, and overfitting avoidance. Do not use for running or debugging existing evals (use improve-skill-quality) nor for skills authoring (use create-skill).

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Authoring and running behavioral evals for a shopping or merchant agent, covering the case shape, authoring rules, code graders and judges, the run pattern, and poisoned fixtures. Load when writing eval cases or rubrics or deciding how a suite runs.

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Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.

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Create professional presentations using the Pyramid Principle methodology. Supports PPTX generation, Marp/Reveal.js Markdown slides, chart creation, speaker notes, and self-evaluation rubrics. Minimal intake form to rapid output workflow.

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A comprehensive auditor for any agent skill — including Manus, OpenClaw/ClawHub, Claude, LobeHub, or custom SKILL.md-based skills. Use this skill whenever a user wants to evaluate, audit, review, score, or quality-check an agent skill before publishing, updating, or deploying. Covers two hard veto gates (structural redlines + research integrity redlines), static quality scoring across 25 criteria (ISO 25010 + OpenSSF + Agent), dynamic test input generation, multi-mode execution testing, multi-layer output evaluation with five specialized category rubrics (Evidence Insight / Protocol Design / Data Analysis / Academic Writing / Other), a Research Veto that applies to all four research categories, human eval viewer generation, actionable P0/P1/P2 optimization recommendations, and automatic skill improvement that outputs a polished, production-ready SKILL.md. Also use whenever a user says "audit my skill", "evaluate my skill", "improve my skill", or wants a corrected version after evaluation.

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Reviews skill quality with evidence-based design rubrics and read-only batch inventories. Use for self-review, external skill or repository audits, and authorized auto-PR improvements. Separates design quality from measured task benefit and host compatibility.

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Design an evaluation plan for an LLM or AI feature before shipping it. Use when asked how to evaluate a prompt/model/agent, set up an eval harness, define quality metrics for an AI feature, or build a regression gate. Produces an eval plan — task definition, datasets, metrics & rubrics, baselines, automated + human evals, a pass bar, and a regression gate.

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Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.

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