「grader」の検索結果

Designs PROTACs, molecular glues, and bivalent degraders with explicit handling of E3 ligase choice (VHL, CRBN, IAP, MDM2, KEAP1), linker design (length, composition, rigidity), ternary complex prediction (PRosettaC, DeepTernary, AlphaFold3), cooperativity (alpha), DC50 / Dmax characterization, hook effect, and prediction-experiment reconciliation. Use when designing targeted protein degraders, planning linker SAR, predicting ternary complex stability, or building generative degrader workflows.

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Generate MATLAB Grader assessment item sets. Use when the user asks to create MATLAB Grader assessment items, generate MATLAB assessment materials, build MATLAB homework assessment items, QTI 3 portable assessment items, or mentions "grader assessment items". Produces complete assessment item folders with description, solution, template, tests, Function call blocks, and optional QTI 3 interchange files.

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Designs PROTACs, molecular glues, and bivalent degraders with explicit handling of E3 ligase choice (VHL, CRBN, IAP, MDM2, KEAP1), linker design (length, composition, rigidity), ternary complex prediction (PRosettaC, DeepTernary, AlphaFold3 with constraints), cooperativity (alpha), DC50 / Dmax characterization, hook effect, and prediction-experiment reconciliation. Use when designing targeted protein degraders, planning linker SAR, predicting ternary complex stability, or building generative degrader workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Designs PROTACs, molecular glues, and bivalent degraders with explicit handling of E3 ligase choice (VHL, CRBN, IAP, MDM2, KEAP1), linker design (length, composition, rigidity), ternary complex prediction (PRosettaC, DeepTernary, AlphaFold3 with constraints), cooperativity (alpha), DC50 / Dmax characterization, hook effect, and prediction-experiment reconciliation. Use when designing targeted protein degraders, planning linker SAR, predicting ternary complex stability, or building generative degrader workflows.

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Phase 3 of building a Claude Managed Agent — the bounded grade→iterate loop. Define a CMA outcome (a required markdown rubric graded by an isolated grader), read each verdict, decide the next move (sharpen / re-run / promote to schedule), and once a version passes, run held-back eval cases in parallel. Use when the user says "grade my agent", "make it pass the rubric", "iterate until it's good", "is it good enough", or when the orchestrator routes phase=grade-iterate. outcome_builder.py builds the user.define_outcome payload (rubric required, max_iterations clamped 1..20 — never unbounded); verdict_reader.py reads the grader result and recommends the next move; eval_scaffold.py generates held-back cases + a parallel run plan (capped at the 25-thread CMA ceiling). Distinct from stage-launch (first launch) and run-without-you (scheduling).

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Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

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Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

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Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.

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Use when an instructor asks for a MATLAB Grader assessment setup guide, adoption guide, pilot plan, course-specific rollout, QTI 3 sharing workflow, or recommended MATLAB Grader assessment-item-generation configuration based on a learning objective, course title, course description, module description, lab description, or assessment goal.

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Generate MATLAB Grader assessment items that are suitability-gated, profile-driven, feedback-aware, and validated through MATLAB MCP. Produces Script, Function, Class Definition, Class Inheritance, Object Usage, and Class Methods items with MATLAB Grader assessment setup instructions.

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Use when a task needs the judgment of an Agricultural Grader/Sorter — calling a defect grade on a moving packing line, deciding whether a lot with a marginal QC sample gets accepted, hand re-sorted, downgraded, or held, choosing where to station a human grader relative to an optical sorter, or diagnosing why a lot's cull rate or customer chargebacks spiked.

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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.

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Suede-owned launch-appeal grader for public pages. Grades findability, first-screen clarity, CTA pull, proof quality, design signal, and AI citation readiness. Use when a public page needs a fast A-F visibility grade before or after launch. NOT FOR: the deep nine-lane SEO audit with rewrite fixes (use suede-seo-audit); implementing conversion changes (use suede-site-alchemy); getting cited by AI answer engines (use suede-ai-seo).

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Work on bifunctional degraders and molecular glues, where potency comes from a ternary complex rather than occupancy. Use this skill to apply the property rules that govern this beyond-rule-of-five space, reason about linker length, attachment vector and E3 ligase choice, prepare inputs for ternary complex structure prediction, and interpret degradation readouts — DC50, Dmax, cooperativity, and the hook effect that makes a dose-response curve turn over. Also trigger on PROTAC, molecular glue, targeted protein degradation, E3 ligase, cereblon, VHL, ternary complex, DC50, Dmax, hook effect, cooperativity, or PROTAC-DB.

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OpenAI API (developers.openai.com) の Evals / Fine-tuning リファレンス。 Evals API, Datasets, graders（string check, text similarity, model, python, multi）, agent evals, trace grading, external models、 SFT, DPO, RFT（reinforcement fine-tuning）, vision fine-tuning, distillation, prompt optimizer, fine-tuning best practices。 セルフサービス fine-tuning は段階的廃止進行中。

AIによる開発の成功条件を実装前に定義し、コード・ルール・モデル・人の評価で新機能と既存機能を確認しながら、複数回の試行から信頼性を記録するスキル。

• AI実装前に成功条件を決めたいとき
• プロンプトやエージェント変更後の回帰確認
• 複数回の試行で信頼性を測りたいとき

Hands-on project tutor for the AI Engineering from Scratch Projects section. Guides a learner through one stage of a real project per session: read the stage lesson, predict, write the code, run the stage grader, reflect, and record progress in PROJECTS-LEARNING.md. Gives hints, never full solutions. Trigger phrases: "build a project", "next project stage", "continue my project", "start the research report agent".

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When the user wants to plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," "free resource," "ROI calculator," "grader tool," "audit tool," "should I build a free tool," or "tools for lead gen." Use this whenever someone wants to build something useful and give it away to attract leads or earn links. For downloadable content lead magnets (ebooks, checklists, templates), see lead-magnets.

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Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.

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When the user wants to build a free tool for marketing — lead generation, SEO value, or brand awareness. Use when they mention 'engineering as marketing,' 'free tool,' 'calculator,' 'generator,' 'checker,' 'grader,' 'marketing tool,' 'lead gen tool,' 'build something for traffic,' 'interactive tool,' or 'free resource.' Covers idea evaluation, tool design, and launch strategy. For pure SEO content strategy (no tool), use seo-audit or content-strategy instead.

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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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Design and verify a deterministic operational-value grader for any GitHub Agentic Workflow. Use when reasoning from workflow goals to measurable downstream outcomes, defining repository evidence, choosing outcome metrics, or creating an operational-value evaluator. Usage: /operational-value-designer OWNER/REPO WORKFLOW-NAME.

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Upgrade any skill to v5 Hybrid format using decision theory + modal logic

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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.

日本語の概要は準備中です。原文の説明を表示しています。