eval-suite-planner

Produit un plan concret de suite d'évaluation basé sur la bibliothèque de scénarios d'évaluation de Microsoft et les directives d'évaluation des agents MS Learn — types de scénarios, méthodes d'évaluation,…

npx skills add https://github.com/microsoft/eval-guide --skill eval-suite-planner

Purpose

This skill produces the Plan artifact of the /eval-guide lifecycle: a populated copy of the customer's Eval Suite Planning & Logging Template plus an interactive HTML review page. The workbook is the source-of-truth artifact; do not replace it with a scenario table, quality-signal table, generic spreadsheet, default .docx report, or HTML-only plan.

The skill aligns to skills/eval-guide/playbook.md and skills/eval-guide/eval-suite-template.md. Use the 10-step playbook as the methodology spine and the XLSX template as the output shape.

Core rule

Copy the blank XLSX template and populate existing cells/rows only. Do not modify the template.

Do not rename sheets, add sheets, delete sheets, add columns, change headers, rewrite README text, edit Dropdown Lists, change styles, change data validation, or convert the template into a different spreadsheet.

If a blank template workbook is available in the session, use it. If not, ask the user to provide the template; do not silently invent a new workbook.

Question policy

Ask targeted questions only when a workbook field materially affects the plan and cannot be inferred safely:

  1. Eval owner / named approver.
  2. Lifecycle stage and target deployment decision.
  3. Whether the agent is prompt-only, RAG/knowledge-grounded, or agentic with tools/connectors.
  4. Regulated/compliance obligations.
  5. Authoritative sources and source owners.

If the user wants speed or cannot answer, populate TBD - confirm before baseline.

Planning method

When invoked as /eval-suite-planner <agent description>:

  1. Extract or infer the agent's purpose, users, knowledge sources, capabilities, boundaries, architecture, lifecycle stage, and known risks.
  2. Populate Step 1 — Plan the Eval Effort:
    • one-sentence eval objective;
    • five-factor risk tier: reach, criticality of error, autonomy/blast radius, regulatory/compliance exposure, data sensitivity;
    • one accountable owner.
  3. Define eval sets, not scenarios:
    • Capability eval sets: one row per capability dimension that must be diagnostic, e.g. accuracy/correctness, faithfulness/groundedness, relevancy, style/tone, reasoning/tool use.
    • Trust & Safety eval sets: one row per refusal, boundary, or safety category, e.g. guardrails, out-of-scope handling, sensitive-data handling, prompt injection/jailbreak, compliance-specific behavior.
  4. Apply Step 4 v5 gates/improvement-target logic:
    • T&S sets use absolute pass-rate hard gates, usually near 100%.
    • Capability sets usually use a launch floor for first deployment plus regression/direction after baseline, not a standing absolute pass-rate target.
    • High-risk capabilities that function like guardrails keep explicit hard floors.
    • Use the template's existing Target pass rate, Target rationale, Gate type, Intended use, Run cadence, and Notes columns to express this; do not add a new column.
  5. Specify Step 5 human inputs:
    • grading rubric, ground truth, golden answer, or rubric + ground truth;
    • author/owner;
    • grounding source dependency;
    • whether source changes require review.
  6. Plan Step 6 grader validation without changing the template:
    • record grader type and validation expectation in the registry row's Notes;
    • for LLM-as-judge / Custom rubrics, note that human-labeled hard and borderline cases must validate the judge before baseline scores are trusted;
    • for programmatic checks, note the deterministic check to confirm;
    • for human grading, note reviewer agreement expectations where relevant.
  7. Seed Step 7 baseline placeholders in 3 . Run Log only when useful:
    • one placeholder row per eval set;
    • Run type = Baseline;
    • result fields blank;
    • Actionable next step = Validate grader, then run baseline;
    • Status = Open.
  8. Apply Step 8 regression partitioning in existing registry fields:
    • capability sets usually Intended use = Both or Regression;
    • most T&S sets are Gate; the slim subset likely affected by model/tool/policy changes can be Both or Regression;
    • set Run cadence using existing dropdown values such as Per-change, Nightly, Weekly, or Milestone-only.
  9. Flag Step 10 reusable assets in 4 . Reusable Library:
    • reusable T&S sets;
    • grading rubrics;
    • failure-pattern templates;
    • production-derived edge-case categories when applicable.

Workbook population rules

Use skills/eval-guide/eval-suite-template.md as the exact tab/column map.

README

Do not edit.

1 . Planning

Populate only existing input cells:

  • Agent identity.
  • Risk classification (5 factors).
  • Owners & roles.
  • Deployment gates / sign-off criteria.

For the template's Min pass rate - Capability row, reflect v5 Step 4 accurately: use launch floor / high-risk capability floor / regression-governance language, not a generic scenario pass-rate target.

2 . Eval Suite Registry

Populate one row per eval set. Do not populate one row per test case or legacy planning artifact.

Required row semantics:

  • Category: Capability or Trust & Safety.
  • Dimension tested: capability dimension or T&S category from the template dropdowns.
  • Purpose / diagnostic signal: what failure in this set diagnoses.
  • Target pass rate: absolute gate for T&S; launch floor or Regression / direction after baseline for most capability sets.
  • Target rationale: v5 Step 4 rationale.
  • Gate type: closest existing dropdown value.
  • Intended use: Gate, Regression, or Both.
  • Run cadence: cadence for Step 8.
  • Human input type, Human input author, Grounding source dependency, Source change -> review?: Step 5.
  • Reusable asset?, Reuse tier, Set status: Step 10 and lifecycle status.
  • Notes: assumptions, open questions, Step 4 nuance, and Step 6 grader-validation plan.

3 . Run Log

Use this for Step 7 baseline/iteration logging. During planning, add placeholder baseline rows only if useful; keep result fields blank.

4 . Reusable Library

Populate candidate reusable assets only. Do not duplicate every eval set; promote assets that could help other agents.

Dropdown Lists

Do not edit.

Output

Create eval-suite-<agent-name>-<YYYY-MM-DD>.xlsx as a populated copy of the template.

Then create eval-suite-<agent-name>-<YYYY-MM-DD>-review.html next to the workbook using skills/eval-guide/plan-review-page.md.

Do not paste the summary, eval-set table, or checklist into chat. The HTML page carries that content. The final chat response should be only the workbook path, the HTML review page path, and any blocker/manual action.

Human review checkpoints

Include these in the HTML review page checklist instead of displaying them in chat:

#CheckpointWhat to verify
1Objective, risk tier, ownerThe objective is decision-oriented, the five-factor risk tier is right, and a named owner can sign off.
2Eval-set decompositionCapability sets isolate one diagnostic capability each; T&S sets remain separate from capability.
3Step 4 barsT&S has absolute hard gates; capability uses launch floors / regression-direction unless high-risk.
4Human inputsRubrics, ground truths, golden answers, and source dependencies have owners.
5Grader validationEach set has a plausible grader type and validation plan before baseline.
6Regression partitionCapability and slim T&S regression sets have cadence; gate-only T&S sets run at milestones.
7Template integrityNo sheets, columns, headers, dropdowns, README text, or formatting were changed.

Behavior rules

  • Do not generate scenario-plan tables as the Plan artifact.
  • Do not generate quality-signal sheets or quality-signal grouping as the Plan artifact.
  • Do not add columns to support missing concepts; use existing fields, especially Notes.
  • Do not create a .docx unless the user explicitly asks for a narrative report.
  • Do not produce long narrative chat output after artifact generation; use the HTML review page for the interactive summary and checkpoints.
  • Be specific to the described agent, but at eval-set granularity.

Companion skills

  • /eval-generator — Generate test cases from the populated workbook registry.
  • /eval-result-interpreter — Interpret baseline / iteration results using Step 6-7 and gate status.
  • /eval-triage-and-improvement — Diagnose failures and feed the Step 9 optimization loop.
  • /eval-library-promoter — Promote Step 10 reusable assets.
  • /eval-guide — Orchestrated workflow with dashboard review checkpoints.

Plus de skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
official
microsoft-foundry
microsoft
Déployer, évaluer et gérer les agents Foundry de bout en bout : build Docker, push ACR, création d’agent hébergé/par prompt, démarrage de conteneur, évaluation par lots, évaluation continue, workflows d’optimisation de prompt, agent.yaml, curation de jeux de données à partir de traces. UTILISER POUR : déployer un agent vers Foundry, agent hébergé, créer un agent, invoquer un agent, évaluer un agent, exécuter une évaluation par lots, évaluation continue, surveillance continue, statut d’évaluation continue, optimiser un prompt, améliorer un prompt, optimiseur de prompt, optimiser les instructions d’un agent, améliorer un agent...
officialdevelopmentdevops
azure-ai
microsoft
Utiliser pour Azure AI : Recherche, Parole, OpenAI, Intelligence documentaire. Aide pour la recherche, la recherche vectorielle/hybride, la reconnaissance vocale, la synthèse vocale, la transcription, l'OCR. QUAND : Recherche AI, recherche par requête, recherche vectorielle, recherche hybride, recherche sémantique, reconnaissance vocale, synthèse vocale, transcrire, OCR, convertir du texte en parole.
officialdevelopmentapi
azure-deploy
microsoft
Exécutez les déploiements Azure pour les applications DÉJÀ PRÉPARÉES disposant de fichiers .azure/deployment-plan.md et d'infrastructure existants. N'utilisez PAS cette compétence lorsque l'utilisateur demande de CRÉER une nouvelle application — utilisez plutôt azure-prepare. Cette compétence exécute les commandes azd up, azd deploy, terraform apply et az deployment avec une récupération d'erreur intégrée. Nécessite .azure/deployment-plan.md de azure-prepare et un état validé de azure-validate. QUAND : "exécuter azd up", "exécuter azd deploy", "exécuter le déploiement",...
officialdevopsaws
azure-storage
microsoft
Services Azure Storage incluant Blob Storage, File Shares, Queue Storage, Table Storage et Data Lake. Répond aux questions sur les niveaux d'accès au stockage (chaud, froid, froid, archive), quand utiliser chaque niveau et comparaison des niveaux. Fournit du stockage d'objets, des partages de fichiers SMB, de la messagerie asynchrone, du NoSQL clé-valeur et de l'analyse de big data. Inclut la gestion du cycle de vie. À UTILISER POUR : stockage blob, partages de fichiers, stockage de files d'attente, stockage de tables, data lake, téléchargement de fichiers, téléchargement de blobs, comptes de stockage, niveaux d'accès,...
officialdevelopmentdatabase
azure-diagnostics
microsoft
Déboguer les problèmes de production Azure à l'aide d'AppLens, Azure Monitor, l'état des ressources et un triage sécurisé. QUAND : déboguer des problèmes de production, résoudre les problèmes d'App Service, CPU élevé d'App Service, échec de déploiement d'App Service, résoudre les problèmes de Container Apps, résoudre les problèmes de Functions, résoudre les problèmes d'AKS, kubectl ne peut pas se connecter, échecs kube-system/CoreDNS, pod en attente, crashloop, nœud non prêt, échecs de mise à niveau, analyser les logs, KQL, insights, échecs de pull d'image, problèmes de démarrage à froid, échecs de sonde de santé,...
officialdevopsdevelopment
azure-prepare
microsoft
Préparer les applications Azure pour le déploiement (infra Bicep/Terraform, azure.yaml, Dockerfiles). Utiliser pour créer/moderniser ou créer+déployer ; pas pour la migration cross-cloud (utiliser azure-cloud-migrate). NE PAS UTILISER POUR : les applications copilot-sdk (utiliser azure-hosted-copilot-sdk). QUAND : "créer une application", "construire une application web", "créer une API", "créer une API HTTP serverless", "créer un frontend", "créer un backend", "construire un service", "moderniser une application", "mettre à jour une application", "ajouter une authentification", "ajouter un cache", "héberger sur Azure", "créer et...
officialdevelopmentdevops
azure-validate
microsoft
Validation pré-déploiement pour la préparation Azure. Effectuez des vérifications approfondies sur la configuration, l'infrastructure (Bicep ou Terraform), les attributions de rôles RBAC, les autorisations d'identité managée et les prérequis avant le déploiement. QUAND : valider mon application, vérifier l'état de préparation au déploiement, exécuter des contrôles préalables, vérifier la configuration, vérifier si prêt à déployer, valider azure.yaml, valider Bicep, tester avant le déploiement, résoudre les erreurs de déploiement, valider Azure Functions, valider l'application de fonction, valider serverless...
officialdevopstesting