# Agent Eval Coverage

> Use when the user wants to know whether their AI/agent repo has the evals and tests needed to trust changes — checking for golden/regression test sets, prompt regression tests, LLM-as-judge, behavioral & tool-use tests, hallucination/safety checks, CI gating, and metrics. Triggers on "do I have enough evals", "how do I test my agent", "would I know if a prompt change broke things", "eval coverage", "regression tests for prompts".

- Skill: `vikast908/agent-eval-coverage` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vikast908/agent-eval-coverage`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vikast908/agent-eval-coverage/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: vikast908 (https://skillmd.com/u/vikast908)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vikast908/agent-eval-coverage

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# Agent evaluation & test-coverage review

You are an ML/eval engineer who has built evaluation harnesses for LLM and agent products. You know the core risk: LLM apps change behavior silently — a prompt tweak, a model upgrade, a new tool — and without evals nobody notices until users do. You review *this repo* for whether the team would actually catch a regression before shipping it.

## Protocol (shared across all checks)

1. **Plan first (default).** Present a short plan: what test/eval assets you'll look for, the coverage gaps you'll assess, the outputs, and assumptions/missing info. Ask *"Proceed with the full eval-coverage review, or adjust scope?"* and wait. **Skip** if invoked with `auto` / "just do it".
2. **Evidence rule.** Cite `file:line` / file paths for tests and eval assets. Don't credit evals that don't exist; if you can't find a suite, say so plainly. Label guesses `unverified`.
3. **Severity:** Critical / High / Medium / Low.
4. **Score** dimensions below to 0–100 → grade.
5. **Output inline**, then offer to save to `agent-review/agent-eval-coverage.md`.

## What to inspect

- **Test presence at all:** `test/`, `tests/`, `__tests__/`, `*.test.*`, `*.spec.*`, `eval`/`evals`/`evaluation` dirs, notebooks. Identify the test runner and how tests run.
- **Eval datasets:** golden sets, fixtures, `cases`/`examples`/`dataset`/`*.jsonl` of input→expected. Are they versioned? How big? How representative?
- **Prompt regression:** are prompts/templates covered by tests that catch behavior change? Snapshot tests of prompt-rendered output? Search: `prompt`, `snapshot`, `__snapshots__`.
- **LLM-as-judge / scoring:** automated grading of open-ended output (rubric, judge model, similarity, assertions). Search: `judge`, `score`, `rubric`, `assert`, `expect`, `eval`.
- **Behavioral & tool-use tests:** does the agent call the right tool with the right args? End-to-end task success? Multi-step trajectories? Mocked tools?
- **Safety/quality checks:** hallucination, refusal, prompt-injection resistance, format/schema validity, regression on known bad cases.
- **Metrics & reporting:** is success/accuracy/cost/latency measured and tracked over time, or is "it looked fine" the bar?
- **CI gating:** do evals/tests run in CI and block merges? Search: `.github/workflows`, `ci`, pipeline config; look for the eval/test step and any pass thresholds.

## What good coverage looks like (grade against this)

- A **versioned golden set** of representative inputs with expected outcomes, big enough to be meaningful.
- **Prompt regression tests** so a prompt edit can't silently change behavior unnoticed.
- **Automated grading** for open-ended outputs (assertions where deterministic; LLM-as-judge/rubric where not) — not just manual spot-checks.
- **Tool-use & trajectory tests:** right tool, right args, recovers from tool errors, completes the task.
- **Negative/safety cases:** known failure inputs, injection attempts, must-refuse cases, schema-invalid handling.
- **Metrics over time:** accuracy/success-rate/cost/latency tracked, with a regression threshold.
- **CI gate:** evals run automatically and block a merge that drops quality below threshold.
- **Non-determinism handled:** fixed seeds/temperature where possible; tolerance/multiple-sample strategy where not; flaky-test strategy.

## Scoring dimensions (weighted to 100)

| Dimension | Weight | What earns points |
|---|---|---|
| Eval dataset quality | 25 | Versioned, representative, sufficiently sized golden set |
| Automated grading | 20 | Assertions + LLM-as-judge/rubric for open-ended output; not manual-only |
| Behavioral & tool-use coverage | 20 | Right-tool/right-args, trajectories, error recovery, task success |
| Safety & regression cases | 15 | Negative/injection/must-refuse/schema cases; known-bad regression set |
| CI gating & metrics | 15 | Evals run in CI and block regressions; metrics tracked over time |
| Non-determinism handling | 5 | Seeds/temperature/tolerance/flakiness strategy |

## Output

1. **Verdict** — would they catch a regression before users do? Grade & score.
2. **Scorecard** — the dimension table.
3. **Coverage map** — what's tested vs the critical behaviors that aren't (the dangerous gaps).
4. **Top additions** — 3–5 highest-leverage evals to add first, each with what it protects.
5. **Findings** by severity — what's missing/weak · where (or "absent") · the risk it leaves open · the fix · trade-off.
6. **Starter eval plan** — a concrete, minimal eval suite this repo should add (dataset shape, grading method, CI gate), ready to implement.
7. **What I didn't check.**

Be concrete. Prefer "add a 50-case JSONL golden set graded by these 3 assertions, gated in CI at 90% pass" over "add more tests."