Eval anova

Skill opendatahub-io/agent-eval-harness/skills/eval-anova

Run Design-of-Experiments (DoE) evaluations with ANOVA over a matrix of agent configurations — comparing models, thinking-effort levels, prompts, or other factors across shared test cases, with repeated-measures / mixed-effects statistics that account for case difficulty plus a cost/quality Pareto view. Use whenever the user wants to compare models or configurations on an eval, decide which model or config is best, sweep or grid factors, run replications, or check whether a difference in eval scores is statistically significant (F, p, effect size) — even if they don't say "ANOVA" or "DoE". Also use when an eval.yaml has a matrix block or the user asks to fan an eval out across configurations.From its SKILL.md

Install

npx -y skills add opendatahub-io/agent-eval-harness --skill eval-anova

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SKILL.md

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eval-anova

Run a full-factorial experiment comparing agent configurations (models, effort levels, prompts) across shared test cases, then analyze results with repeated-measures ANOVA.

Usage

python3 ${CLAUDE_SKILL_DIR}/scripts/orchestrate.py --config eval.yaml               # run → analyze → report
python3 ${CLAUDE_SKILL_DIR}/scripts/orchestrate.py --config eval.yaml --dry-run     # design + cost estimate, no execution
python3 ${CLAUDE_SKILL_DIR}/scripts/orchestrate.py --config eval.yaml --analyze-only  # re-analyze existing runs + re-render

New to this skill? See QUICKSTART.md for from-scratch setup and run steps, and eval/anova-example/ for a self-contained worked example (with committed sample runs you can analyze offline).

How it works

eval-anova is not its own executor — it wraps /eval-run in a matrix loop:

eval-run stays the single-condition primitive (one model/config → one run with a summary.yaml). eval-anova runs it once per matrix cell (condition × replication), so every cell is a standard run under $AGENT_EVAL_RUNS_DIR/<eval-name>/, tagged with a condition.json recording its factor levels.
Statistics are computed over those runs' summary.yaml files (analyze.py → anova.json): each case's composite uses the harness's canonical reward composition (the eval.yaml reward: section, else boolean-gate + normalised-numeric average), then repeated-measures / mixed-effects ANOVA + a cost/quality Pareto frontier.
The report is /eval-compare, which eval-anova invokes over the runs. eval-compare surfaces the ANOVA/Pareto stats automatically when it finds anova.json, and stays a standalone descriptive comparison when it does not.

Because the stats read standard summary.yaml runs, you can also analyze runs produced elsewhere (e.g. a CI fan-out that runs /eval-run per model) — just point --analyze-only at their directory.

Prerequisites

Install ANOVA dependencies:

pip install -e ".[anova]"

Results archival is optional — set RHAI_RESULTS_REPO=/path/to/results to archive experiments to a git repo (a per-user temp dir is used as a fallback when unset).

Workflow

Design: Define factors and levels in your eval YAML's matrix: section (--dry-run prints the grid + a cost estimate).
Execute: For each condition × replication, drive /eval-run (workspace → execute → collect → score) → one standard run + summary.yaml, tagged with condition.json.
Analyze: Repeated-measures / mixed-effects ANOVA + cost/quality Pareto over the runs → anova.json (--analyze-only runs just this over existing runs).
Report: /eval-compare renders the cross-condition comparison, including the statistics section, from the runs + anova.json.

Reports

The orchestrator invokes /eval-compare automatically. To (re-)render from existing artifacts:

# Comparison report (leaderboard, heatmap, + the ANOVA/Pareto stats section if anova.json exists):
python3 ${CLAUDE_PLUGIN_ROOT}/skills/eval-compare/scripts/compare.py generate $AGENT_EVAL_RUNS_DIR/<eval-name>

# Statistics-forward deep view for one experiment (condition means, F / p / η², per-case matrix):
python3 ${CLAUDE_SKILL_DIR}/scripts/report.py $AGENT_EVAL_RUNS_DIR/<eval-name>   # reads anova.json

Both read only on-disk artifacts (summary.yaml / anova.json) and never re-run the experiment.

Matrix Configuration

Add a matrix: key to your eval YAML:

matrix:
  factors:
    model:
      - claude-opus-4-8
      - claude-sonnet-4-6
    effort:
      - low
      - high
  replications: 3

See references/matrix-schema.md for the full schema.

Statistical Methods

Repeated-measures ANOVA (default): Accounts for case difficulty as a blocking factor. Correct when the same cases are evaluated under all conditions.
Mixed-effects model: For multi-factor designs with crossed random effects.
One-way ANOVA: available in the stats library for independent samples (cases NOT reused), but rarely appropriate — the orchestrator does not auto-select it.

See prompts/interpret-anova.md for guidance on interpreting results.