eval-suite-planner

作者: microsoft

根据微软评估场景库和MS Learn代理评估指南,生成具体的评估套件计划——包括场景类型、评估方法……

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.

来自 microsoft 的更多技能

oss-growth
microsoft
OSS增长黑客角色
official
microsoft-foundry
microsoft
端到端部署、评估和管理Foundry代理:Docker构建、ACR推送、托管/提示代理创建、容器启动、批量评估、持续评估、提示优化工作流、agent.yaml、从追踪中整理数据集。用途:将代理部署到Foundry、托管代理、创建代理、调用代理、评估代理、运行批量评估、持续评估、持续监控、持续评估状态、优化提示、改进提示、提示优化器、优化代理指令、改进代理...
officialdevelopmentdevops
azure-ai
microsoft
用于Azure AI:搜索、语音、OpenAI、文档智能。支持搜索、向量/混合搜索、语音转文字、文字转语音、转录、OCR。适用场景:AI搜索、查询搜索、向量搜索、混合搜索、语义搜索、语音转文字、文字转语音、转录、OCR、文字转语音。
officialdevelopmentapi
azure-deploy
microsoft
对已准备好的应用程序执行Azure部署,这些程序需包含现有的.azure/deployment-plan.md和基础设施文件。当用户要求创建新应用程序时,请勿使用此技能——应改用azure-prepare。此技能运行azd up、azd deploy、terraform apply和az deployment命令,并内置错误恢复机制。需要来自azure-prepare的.azure/deployment-plan.md以及来自azure-validate的已验证状态。适用场景:"运行azd up"、"运行azd deploy"、"执行部署"...
officialdevopsaws
azure-storage
microsoft
Azure存储服务,包括Blob存储、文件共享、队列存储、表存储和Data Lake。解答关于存储访问层(热、冷、冷、归档)的问题,说明各层的使用场景及对比。提供对象存储、SMB文件共享、异步消息传递、NoSQL键值存储和大数据分析。包含生命周期管理。用途:Blob存储、文件共享、队列存储、表存储、Data Lake、上传文件、下载Blob、存储账户、访问层等。
officialdevelopmentdatabase
azure-diagnostics
microsoft
使用AppLens、Azure Monitor、资源健康和安全分类调试Azure生产问题。适用场景:调试生产问题、排查应用服务、应用服务CPU过高、应用服务部署失败、排查容器应用、排查函数、排查AKS、kubectl无法连接、kube-system/CoreDNS故障、Pod挂起、CrashLoop、节点未就绪、升级失败、分析日志、KQL、洞察、镜像拉取失败、冷启动问题、健康探测失败……
officialdevopsdevelopment
azure-prepare
microsoft
为Azure应用准备部署(基础设施Bicep/Terraform、azure.yaml、Dockerfile)。用于创建/现代化或创建+部署;不用于跨云迁移(使用azure-cloud-migrate)。请勿用于:copilot-sdk应用(使用azure-hosted-copilot-sdk)。适用场景:"创建应用"、"构建Web应用"、"创建API"、"创建无服务器HTTP API"、"创建前端"、"创建后端"、"构建服务"、"现代化应用"、"更新应用"、"添加身份验证"、"添加缓存"、"托管在Azure上"、"创建并...
officialdevelopmentdevops
azure-validate
microsoft
部署前对Azure就绪状态进行验证。对配置、基础设施(Bicep或Terraform)、RBAC角色分配、托管标识权限及先决条件进行深度检查,然后再部署。适用场景:验证我的应用、检查部署就绪状态、运行预检、验证配置、检查是否可部署、验证azure.yaml、验证Bicep、部署前测试、排查部署错误、验证Azure Functions、验证函数应用、验证无服务器...
officialdevopstesting