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Pl3ntz/llmfoundry

OpenCode-native, model-agnostic AI engineering harness with role routing, specialist agents, local memory, evals, and runtime safety gates.

llmfoundry 是什麼?

llmfoundry is a OpenCode agent skill that openCode-native, model-agnostic AI engineering harness with role routing, specialist agents, local memory, evals, and runtime safety gates.

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說明文件

AI Evals

Treat evals as the unit tests of AI development. Define expected behavior before implementing, run continuously, track regressions with every change.

Principles

  1. Golden set first, fixed inputs + expected outputs, frozen (versioned).
  2. Assert, don't judge, deterministic assertions over LLM-as-judge wherever possible.
  3. Regression gate, a prompt/agent change that fails the golden set does not ship.
  4. Pass@k, reliability under sampling, not one lucky run.

Golden set structure

{
  "id": "GS-1",
  "type": "factual | current-events | false-premise | osint | comparative",
  "input": "...",
  "expected": { "answer": "...", "mustContain": ["..."], "mustNotContain": ["..."] },
  "knownTraps": ["hallucinate version", "HIGH on single source"]
}

Eval types

TypePurpose
CapabilityCan it do something new?
RegressionDoes it still do what it used to?
RobustnessAdversarial inputs, edge cases, false premises
StabilitySame input K times, where does output fluctuate?

Assertions

  • Exact match, normalized answer equals key (factual items)
  • Contains / not-contains, presence of required elements, absence of banned ones
  • Schema valid, output parses, required fields present
  • URL liveness, cited URLs resolve (research)
  • Fabrication kill-check, banned specifics absent (false-premise items)
  • Contract lint, required headers, length budget, zero preamble

Regression flow

  1. Change a prompt/agent
  2. Run the full golden set (N≥3 runs for stability)
  3. Compare vs baseline: no regressions on automatable dims
  4. Report per-item deltas, not just the mean
  5. Promote only if it passes the gate

CI integration

  • Run evals on every prompt change (in CI or pre-commit).
  • Keep baselines versioned. A baseline IS the contract.
  • Do not publish the answer key with the eval harness if it would contaminate the benchmark.

Anti-patterns

Anti-patternFix
LLM-judge-onlyDeterministic assertions first
Edit golden set freelyFreeze + version; edit = new version
One run, passN≥3, report variance
Mean-only reportingPer-item deltas
Change prompt without evalRegression gate blocks the change

Verification

  • Golden set is frozen and versioned
  • Every change ran the full suite
  • No regressions on automatable dims
  • Fabrication kill-checks pass on adversarial items

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