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bastosbastos/skills/design-resumable-model-evaluation/SKILL.md

design-resumable-model-evaluation

Skill7 starsChanged 10 months agoMITView on GitHub

Designs strict model benchmarks that persist per-case evidence, resume without repeating completed work, separate first-attempt behavior from remediation, and stop early only when failure is mathematically certain. Use for slow, costly, or interruptible evaluations.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Create a unique revision for every combination of corpus membership, model, prompt/instructions, schema, decoder settings, validator, run …

    line 12
  2. Distinctive:

    Write the case manifest before inference.

    line 14
  3. Distinctive:

    Use an append-only result stream or atomically replace a small state file after every case/run.

    line 18
  4. Distinctive:

    start/end timestamps and latency.

    line 25
  5. Distinctive:

    Keep required run counts and thresholds unchanged.

    line 34

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Design Resumable Model Evaluation

Make interruption cheap and evidence comparable.

Freeze an evaluation revision

Create a unique revision for every combination of corpus membership, model, prompt/instructions, schema, decoder settings, validator, run count, and thresholds. Hash this configuration into the state file. Never seed a new revision from incompatible results.

Write the case manifest before inference. Select cases from stable metadata, not previous outcomes.

Persist atomically

Use an append-only result stream or atomically replace a small state file after every case/run. Store:

  • case and run IDs;
  • frozen configuration hash;
  • raw and normalized output;
  • parse, identifier, semantic, execution, and safety gate results;
  • abstention and generation-failure status;
  • start/end timestamps and latency;
  • interruption and retry metadata.

On resume, validate the configuration hash, scan completed keys, and execute only missing (case, run) pairs. Deduplicate safely if a crash occurred after writing output but before updating summary state.

Preserve benchmark semantics

Evaluate first-attempt output with exactly one generation when that is the product question. Do not call production remediation, repair malformed JSON, or deterministically choose a fallback. Exercise remediation in a separate test and metric series.

Keep required run counts and thresholds unchanged. A resumable evaluator improves execution efficiency, not acceptance criteria.

Stop only on safe math

For a minimum rate t over N required trials, fail early only when even winning every remaining trial cannot reach the required successes:

successes + remaining < ceil(t * N)

For maximum-failure gates, stop only once the allowed failure count is irreversibly exceeded. Apply the same reasoning separately to each gate and subgroup. Do not early-pass unless all required trials are complete.

Report

Report completed and required runs, completed fixtures/cases, failures, abstentions, gate numerators and denominators, distribution/diversity gates, p50/p95 latency, interruptions, resumptions, and exact automated gate results. State which human, device, energy, usefulness, or product gates remain untested.

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