eval-suite-planner

A planning tool that fills an evaluation workbook from an agent description or vision. Agent evaluation means testing an AI agent against defined examples, quality checks, and human review.

In plain words
What is it for?
Use it to plan evaluation scenarios, quality signals, pass/fail gates, improvement targets, human inputs, and grader validation.
Why use it?
It turns a plain-language idea into a structured evaluation plan while preserving the required workbook template.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/microsoft/eval-guide/eval-suite-planner
Any agent
npx skills add microsoft/eval-guide --skill eval-suite-planner
Clone the repo
git clone --depth 1 https://github.com/microsoft/eval-guide

Made for: Claude Code, Codex.

Per session 122 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,076 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00122 $0.02076
Opus 5 $0.00061 $0.01038
Sonnet 5 $0.00024 $0.00415
Haiku 4.5 $0.00012 $0.00208

Measured 2d ago against content hash 205e6d8352f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

eval-suite-planner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/eval-suite-planner/SKILL.md · 162 lines

How it starts

The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 162 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 162 lines · 0 tokens per session scan A 205e6d8352f1

Subscribe to this mod's changes

eval-suite-planner is a skill published in the GitHub repository microsoft/eval-guide (127 stars, last pushed 2mo ago), licensed MIT. It adds 122 tokens to every session and 2,076 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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