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.
npx agentmods add skills/microsoft/eval-guide/eval-generatornpx skills add microsoft/eval-guide --skill eval-generatorgit clone --depth 1 https://github.com/microsoft/eval-guideWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00115 | $0.06723 |
| Opus 5 | $0.00057 | $0.03361 |
| Sonnet 5 | $0.00023 | $0.01345 |
| Haiku 4.5 | $0.00012 | $0.00672 |
Grade A, and why
eval-generator 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.
How it starts
The opening of the file, as written. The whole thing — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
This skill produces the Generate artifact of the /eval-guide lifecycle: importable test cases for Copilot Studio's Evaluation tab plus a .docx test-case report carrying the full manifest for human review and downstream Run/Interpret stages. It is the standalone form of /eval-guide Generate.
In the canonical Practical Guidance on Agent Evaluation: 10-step playbook, this skill delivers Step 2 — Build the Capability Eval Sets and Step 3 — Build the Trust & Safety Eval Sets, and it designs the Step 8 — Regression Suite partition for those sets. Keep the operational stage name Generate as UX scaffolding; use the playbook terms for methodology.
Primary mode — the conversation or attachments contain the populated /eval-suite-planner workbook (eval-suite-<agent-name>-<date>.xlsx). Use 2 . Eval Suite Registry as the source of truth for eval sets, and 1 . Planning for risk tier, owners, gates, lifecycle stage, and source dependencies. Generate one set of cases per capability row and one set per trust & safety row. If only a narrative plan is available, use it as a fallback source.
Fallback mode — no plan in conversation. Accept a plain-English agent description and generate test cases from scratch (6–8 cases minimum), using the same data model and including at least one adversarial / trust & safety scenario.
Maturity callout — Pillar 2 (Build your eval sets): Generate advances Pillar 2 from L100 Initial ("no established eval set") to L300 Systematic ("versioned eval set with coverage purposefully targeted"). The CSV files plus companion manifest are the Pillar 2 artifact. The Step 8 partition also seeds Pillars 3 and 5 for later operation.
Instructions
When invoked as /eval-generator (with or without input):
Step 0 — Detect input mode
Scan the conversation and attachments for a populated planner workbook first. If present, read:
1 . Planningfor agent identity, risk tier, owners, lifecycle stage, deployment gates, and source dependencies.2 . Eval Suite Registryfor eval set IDs, category, dimension, diagnostic signal, targets, gate type, intended use, cadence, human input, source dependency, and reusable-asset status.3 . Run Logonly for existing baseline/iteration context, if any.
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.
- 2d ago First seen · 412 lines · 115 tokens per session scan A 04e433f19005
eval-generator is a skill published in the GitHub repository microsoft/eval-guide (127 stars, last pushed 2mo ago), licensed MIT. It adds 115 tokens to every session and 6,723 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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