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run-assert-eval

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Run an ASSERT evaluation from a plain-language behavior requirement. Use when the user wants to evaluate, test, or check an AI agent, LLM app, or model against requirements/policies (e.g. "evaluate my agent for budget violations", "test that the support bot never gives legal advice"). Generates or reuses an eval_config.yaml, runs the pipeline, and reports pass/violation rates with trace-cited failure examples.

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How to use this skill

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Source SKILL.md: https://github.com/responsibleai/ASSERT/blob/HEAD/.claude/skills/run-assert-eval/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/run-assert-eval/. Do not write files or run scripts until I approve.

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

Run an ASSERT evaluation

When to use

The user describes a behavior they want their agent or model to follow or avoid, and wants evidence of how it actually behaves. Not for fixing the agent — this skill finds and reports failures.

This skill has two entry modes:

  • Run mode — no usable results exist yet: generate or reuse a config, run the pipeline (Steps 1-3), then report (Step 4).
  • Results Q&A mode — judged artifacts already exist under artifacts/results/<suite>/<run>/ and the user asks a question about them ("what are the highlights?", "top 3 examples of the worst failure mode?", "why did case X fail?"). Skip to Step 4 and answer THAT question from the artifacts — do not re-run, and do not fall back to the full canned report unless asked.

Copilot vs. the local viewer

Copilot is for answering questions and synthesis — direct answers, failure-mode clustering, cited examples, next actions — with no clicking. The bundled local viewer is for visual exploration — forest plots, baseline compare, facet grouping, and stepping through a transcript with the judge's citations highlighted. Answer in chat when the user asks "what / why / which"; hand off to the viewer (Step 5) when they want to see, read a full transcript, compare runs, or watch a live run.

Preconditions (check, don't assume)

  1. ASSERT installed: assert-ai --help succeeds. If not, guide install:

    python -m pip install -e ".[otel,langgraph]"

  2. Provider creds exist in .env. NEVER read or print .env. If a run fails with an auth error, tell the user which variable NAMES are required (AZURE_API_KEY, AZURE_API_BASE, OPENAI_API_KEY, etc.) — never their values.

Steps

1. Get or make a config

  • If the user has an existing config, use --from <path> to extend it:

    assert-ai init --from <path> --model <litellm-model> --non-interactive -o eval_config.yaml

  • If the user provides a plain-language requirement, generate from scratch:

    assert-ai init --model <litellm-model> --describe "<user requirement + target context>" --non-interactive -o eval_config.yaml

  • If the spec is vague, ask ONE clarifying question first — vague specs produce vague test sets.

  • After generation, show the user the generated behavior.description, context, and pipeline.judge dimensions. Confirm before running.

2. Identify the target shape

Help the user set the right target in the config:

  • Framework agent (LangGraph, CrewAI, etc.) with a Python entry function: use target.callable WITH target.trace so the judge can cite tool calls and routing.
  • Hosted model with a system prompt and optional tools: use target.model and target.tools.
  • Pre-collected traces (no live inference needed): use assert-ai judge-traces --traces <path> --config <path>.

3. Run the pipeline

assert-ai run --config eval_config.yaml --output json

This is long-running (systematize -> test_set -> inference -> judge). Stream status to the user as each stage completes.

  • To re-run from a specific stage: --force-stage <stage>
  • Note the suite and run names from the config for Step 4.

4. Report results — never collapse to one number

Read only structured artifacts. Aggregate from the pre-computed, schema'd files — never trawl raw Phoenix/OpenTelemetry traces to reconstruct an answer (that bulk, unguided trace-reading is exactly what the viewer's evidence drawer is for). Reading the inference_set.jsonl row for a specific case the judge already cited is fine; bulk trace trawling is not.

  1. Headline rates: run assert-ai results status <suite> <run> for per-dimension flagged rates (split into prompt and scenario). Report policy_violation and overrefusal SEPARATELY — they are two different problems.

  2. Top failing cases: read scores.jsonl from artifacts/results/<suite>/<run>/. For each dimension with failures, pull 3-5 representative cases with:

    • The test case description (what was tested)
    • verdict.dimensions — which dimensions failed
    • verdict.dimension_justifications — the judge's rationale with cited evidence
    • verdict.node_judgments — which behavior categories were violated, with reasoning

  3. Cost and timing: read metrics.json for token usage and elapsed time per stage. This file contains cost metadata only, not score roll-ups.

For Results Q&A mode, answer the user's specific question from these same artifacts (e.g. rank dimensions by flagged rate for "top failure mode", then quote dimension_justifications for the cited examples). Don't emit the full template unless asked.

5. Hand off to the local viewer

After reporting, point the user to the bundled viewer for anything visual or self-directed — it went through extensive design iteration and owns the exploration surface Copilot should not replicate:

cd viewer && npm install && npm run dev   # then open http://localhost:5174

Select the suite and run for forest plots, per-dimension breakdowns, facet grouping, the permissible vs. not-permissible policy-violation split (a viewer-only breakdown), and a transcript drawer with the judge's [N] citations highlighted on the cited turns. Suggest it specifically when the user wants to:

  • read a full transcript or see the trace for a case → viewer evidence drawer
  • compare against a baseline → viewer compare view (or assert-ai results compare <suite> <runA> <runB>)
  • watch a run in progress → viewer live run monitor (manifest.json-driven)

See docs/guides/use-local-viewer.md for the full layout.

Output format

Present a short summary with this structure:

Headline metrics (per dimension):

  • Policy violation rate: X% (N/M cases)
  • Overrefusal rate: X% (N/M cases)

Top failing cases (3-5 per dimension): For each failure:

  • Requirement cited: [behavior category from taxonomy]
  • Action cited: [specific turn or tool call from judge rationale]
  • Judge rationale: [verbatim from dimension_justifications]

Suggested next step: one concrete action (e.g. "tighten the system prompt around X behavior", "add a dimension for Y", "apply an ACS guardrail at the failing checkpoint").

Guardrails

  • Don't invent metrics — only report what's in the artifacts.
  • Don't trawl raw traces to answer questions — answer from results status, scores.jsonl, and metrics.json; hand off to the viewer for visual trace/transcript exploration.
  • Hand off, don't reimplement the viewer — for visual drill-down, baseline compare, or live monitoring, point to the local viewer rather than reproducing it in chat.
  • Don't read, print, or commit .env, credential values, artifacts/, traces, .venv, or logs.
  • If the spec is vague, ask one clarifying question FIRST.
  • Reference env variable NAMES only (AZURE_API_KEY, AZURE_API_BASE, azure_ad_token) — never values.
  • Don't commit artifacts to the repository.