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Run inference

Skill avnath13/evalpilot/skills/run-inference

Run the agent-under-test over every eval case and capture output + trajectory as trace artifacts. Use when you need fresh runs to grade or to A/B a fix. Do NOT use for cases that already carry a real production trace (skip them, the trace exists), do NOT use to build the eval set (use prepare-data), and do NOT use to score (use grade). Run the agent in a SANDBOX, never let eval runs hit production databases or live APIs.From its SKILL.md

Install
npx -y skills add avnath13/evalpilot --skill run-inference

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SKILL.md

1.8 KB, 361 tokens by cl100k_base, as published. Nobody here has run it

run-inference: eval set → traces

Output: runs/<runId>/<case_id>.trace.json, conforming to evalpilot/schemas/trace.schema.json.

Procedure

  1. Mint a run_id (e.g. run_2026_07_16_a). Read .evalpilot/evalset.jsonl.
  2. Skip trace-origin cases. If a case has source_trace_id and its trace is already available, copy it into the run instead of re-calling the agent. This is the diagram's "skip if traces exist" edge, production data never gets re-run.
  3. For every remaining case, call the agent per agent_under_test.kind:
    • cli: substitute the case input into command ({input} placeholder), run it, capture stdout as output and parse any structured tool log into steps.
    • http: POST input to url (as input_field), capture the JSON response.
    • python: import and call the entrypoint.
    • trace-only: do nothing; grading uses existing traces.
  4. Capture the trajectory, not just the answer, tool calls, retrievals, errors, so grading can judge how the agent got there, not only the final string.
  5. Record latency_ms and tokens when available. Run cases in parallel when safe.

Guardrails

  • On agent error, still write a trace with error set, a crash is a gradeable outcome.
  • Never fabricate an output. If the agent can't be called, stop and report the wiring bug.

What ships with it

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Just SKILL.md. No reference files, no scripts.

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