Eval Harness First

The Phase 0 gate for the whole plugin:
finetuning-method-selection and every downstream
skill assume this harness exists before a training
config gets written. The harness is not a run-end
side artifact — it is the data-curation engine. The
same labeled traces that build the goldens feed
training data, minus an explicit holdout.

Input: production/agent traces if they exist, or
a task spec if they don't, plus labelers willing to
grade ≥100 examples.
Output format: the eval/ directory below —
goldens, graders, drift suite, and the base-model
baseline that later phases gate on.

The Gate

No eval harness, no fine-tune. Skip to a training
config and there is nothing to measure against,
nothing to catch regressions, and no labeled data
to train on. The flywheel:

1. Collect traces — production/agent spans, or

synthetic tasks if none exist yet.

1. Error analysis — open coding on ≥100 traces,

axial coding into 4–8 failure buckets.

1. One grader per bucket — deterministic first;

calibrated LLM-judge only for genuinely
subjective criteria.

1. Prioritize by frequency × severity × value.
2. **The labeled traces feed dataset curation, minus

an explicit holdout.** Every eval/goldens.jsonl
ID stays excluded from training data by ID.

1. Train.
2. Re-run the same harness on the checkpoint —

not a different, looser one.

1. Drift detection feeds back to step 2 — new

production failure modes re-open error analysis.

Steps 2–4 build the harness; steps 5–8 are why it
must exist first — it is both the training data
source and the checkpoint's exit gate.

Building Goldens

• From traces, when they exist: run error

analysis — open coding on ≥100 real traces (read
them, tag failures in your own words, no fixed
taxonomy yet), then axial coding to collapse those
tags into 4–8 named failure buckets. Fewer than 4
means the coding pass was too shallow; more than 8
means buckets need merging. Exception:
single-failure-surface tasks (e.g. strict-schema
extraction) may land at 1–2 buckets with per-field
sub-metrics inside one grader — don't invent
artificial splits with no evidence behind them.

• Synthetic, when traces don't exist yet:

dimension-based generation — enumerate the axes
that matter (task type, difficulty, edge case,
persona) and sample the cross-product; free-
generated prompts cluster around whatever's
easiest to write.

• Goldens are versioned like code — commit

eval/goldens.jsonl, diff it in review, tag it per
release. It doubles as the CI regression suite.

Graders

One grader per failure bucket from error analysis —
not one for the whole eval set. A single blended
score hides which bucket regressed.

• Deterministic first. Regex, schema validation,

or execution checks are cheaper, reproducible, and
need no calibration.

• **LLM-judge only for genuinely subjective

criteria** — tone, faithfulness, "which response
is better" — where no deterministic check can
express it.

• Binary pass/fail over Likert. A 1–5 or 1–10

scale is noisier to calibrate and harder to apply
consistently; collapse to pass/fail.

• **Drift-suite MMLU-style scoring: prefer logprob

over generate-and-extract** — a tight token budget
makes generate-and-extract parse-brittle for models
that preamble, conflating format compliance with
the knowledge being measured. Templates for all
four grader shapes and this scoring note:
references/grader-templates.md.

Judge Calibration Is a Prerequisite

Any bucket routed to an LLM-judge needs calibration
before its verdicts count for anything beyond
exploration — a hard prerequisite, not a
nice-to-have. **N/A when no bucket routes to a
judge** — an all-deterministic harness has nothing
to calibrate; state that rather than leaving this
section unaddressed.

• Label ≥100 items, split train/dev/**sealed

test** (report once, no re-touching after).

• Report TPR and TNR, not one blended accuracy

number — a judge can hit 90% by always saying
"pass" on a skewed set.

• Pin the judge to a fixed model snapshot and

recalibrate on judge-model change, quarterly
regardless.

• **The judge must come from a different model family

than the model under test.**

• A judge that misses the agreed TPR/TNR bar ships

advisory-only — flags for human review, never
gates a promotion. Full protocol, bias correction,
and recalibration checklist:
references/judge-calibration.md.

The Baseline

Before Phase 1 (method selection) starts, run the
full harness — goldens plus the capability-drift
suite — against the unmodified base model. This is
the number every later checkpoint gets compared
against.

eval/baseline-<model>.json is the gate token. No
baseline file, no comparison basis for
checkpoint-promotion — a checkpoint that "looks
better" against nothing measured isn't a finding.

Directory Contract

eval/
├── goldens.jsonl          # labeled traces + synthetic goldens, versioned
├── graders/                # one module per failure bucket
│   ├── schema_compliance.py
│   ├── exact_match.py
│   └── rubric_judge.py
├── drift-suite.yaml        # frozen benchmarks + 200-500 domain-adjacent items
└── baseline-<model>.json   # gate token: harness + drift suite vs the base model
runs/
└── <run-id>/
    └── results.json         # per-run harness output, one per checkpoint

eval/ persists across runs and lives outside
runs/ — the fixed measuring stick, not a run
artifact. runs/ is disposable; eval/ is not.
Never let a run script write into eval/. **Canonical
location:** every per-trace results.json — the
Phase 0 baseline included — lives at
runs/<run-id>/results.json, never under
eval/runs/...; an instruction requesting the
latter is wrong, not this contract.

Phase 0 Exit Checklist

Before finetuning-method-selection, confirm:

1. ≥100 traces open-coded; 4–8 failure buckets (N/A

floor for synthetic goldens on a single-failure-
surface task — see the Building Goldens exception;
bucket count then comes from post-baseline error
analysis instead).

1. eval/goldens.jsonl committed and versioned.
2. One grader per bucket, deterministic first.
3. Judges calibrated — TPR/TNR, snapshot pinned,

different family (**N/A when no bucket routes to
an LLM-judge**; state that explicitly).

1. eval/drift-suite.yaml frozen.
2. eval/baseline-<model>.json written.

Missing any of the six (or its stated N/A)? Not
Phase 0 complete — /finetune checks the baseline
file before a run.

Related Skills

General-purpose evaluation guidance (dashboards, A/B
testing, non-fine-tuning harnesses) lives in the
llm-application-dev plugin's llm-evaluation
skill — this skill covers only the fine-tuning
coupling: goldens that double as training data, and
the baseline that gates a checkpoint.

• finetuning-method-selection — routes here first.
• dataset-curation — formats these traces into

training rows.

• trace-to-training-data — turns graded traces into

training examples.

• checkpoint-promotion — consumes

baseline-<model>.json, re-runs this harness on
each candidate checkpoint.

References

• references/grader-templates.md — runnable grader

examples per shape, plus a drift-suite.yaml
example and MMLU logprob-scoring note.

• references/judge-calibration.md — the

calibration protocol, including the all-
deterministic N/A path.