Augustus Train

Deliver a runnable task specialist and its evidence, or retain a better incumbent. The artifact may be a rule, classifier, ranker, regressor, adapted encoder, prompted decision, or compiled program. Fit only what the application needs. Load the companion augustus skill for placement and composition; this skill carries the chosen component from messy records into inference.

Start from the application, not a training recipe

Write a short executable contract in the application's own files:

• Decision: input available at decision time, unit of work, target and label vocabulary, output meaning, and downstream action. Separate exact computation, bounded judgment, and freeform generation. A multi-step application can contain several bounded components; test the complete path as well as each component.
• Policy: action costs or useful outcome metric, hard constraints, ambiguity, missing evidence, invalid output, timeout, abstention/fallback, and authority. Define tie rules and unknown/no-match separately from a real class.
• Acceptance: incumbent, minimum useful change or non-inferiority margin, critical slices, deployment latency/memory, available labels and compute, maximum trials/spend, and an outcome that would reject this candidate.

An exact rule may finish the task immediately. Small data may justify a bounded prototype without a generalization guarantee. Missing labels do not justify fabricating gold. Do not require every task to train, call a zero-shot model, calibrate, or visit every artifact family before making progress. For exact rules, verify the authoritative mapping and failure paths directly. Incidental labeled notes are not automatically a representative holdout; do not attach population-error bounds without the requisite sampling assumptions.

Choose a primitive and a small candidate set

Required behavior	Candidate primitive and base stock	Fit or adapt when useful
Exact eligibility, arithmetic, fixed vocabulary mapping	Ordinary code, parser, lookup	No model; test boundary and failure cases
Stable categorical field in text or records	Sparse features, frozen domain encoder, tabular classifier	Regularized head; SetFit/encoder adaptation if representation is the bottleneck
Choice from a changing candidate set	Instruction-conditioned decision model or pair/cross-encoder	Few-shot criteria, readout/head adaptation; test candidate coverage and option order
Ordered rating or quantity	Ordinal classifier or regressor with defined target/units	Ordinal loss or regression; do not reinterpret a normalized score as a physical quantity
Select or order items for a query	Retriever plus pointwise/pairwise/listwise ranker	Query-grouped relevance labels; evaluate retrieval recall and ranking separately
Visual/audio evidence	Modality-appropriate encoder and head	Freeze then adapt only if task evidence supports it; text proxies may omit the signal
Stable semantic rule with local high-volume serving	Small instruction model, adapter or compiled program	SFT/distillation with permitted targets; validate emitted vocabulary and runtime

Use compatible stock already available: pin identity/revision, tokenizer or preprocessor, feature layer/pooling, modality, language/domain, context limits, license and training lineage. Check that deployment can run the artifact before fitting. A larger base may help, but adds memory, latency and provenance costs. Choose only candidates justified by the task and budget, not by a label-count ladder.

A task specialist is not a general instruction-conditioned Jev. A fixed-label classifier can expose a Choice-shaped response without understanding arbitrary new criteria/options. Generalization across tasks requires a task-diverse training mixture and held-out tasks, not merely new rows of one taxonomy. Choice, Score and Noul describe interfaces; they do not supply a training objective or guarantee calibration. Record whether each value is an event probability estimate, logit, relative score, ordinal level or quantity. Shape alone supplies none of those meanings.

For a binary predicate, define the positive event and missing-evidence behavior. For categorical Choice, bind scores to option identities, including open-set cases. For an ordered Score, define level meanings and preserve the distribution when needed; an expected level index is not automatically cardinal utility or a unit.

Build and exercise the whole path

1. Assemble data. Follow data and splits: audit raw records, define/adjudicate labels, resolve groups and timestamps, preserve unknowns and lineage, then freeze representative data roles.
2. Make the evaluator executable first. Test labels, polarity, aliases, abstention and loss on hand-calculated cases; use spec defects when model and spec disagree. Keep the incumbent runnable. A cost-optimal constant, existing rules or current workflow is often the useful comparator; a compatible zero/few-shot model is optional. Majority class is not always the cheapest constant under unequal costs.
3. Fit a bounded candidate. Use fit and serve for a CPU example and family-specific adaptation steps. Fit transformations only on training data, tune on development data, record every tried version, and smoke-test the inference seam before a long fit.
4. Select policy where needed. Inspect discrimination and task loss, critical class recall, errors, fallback and total serving cost. Calibrate only if the consumer needs probability meaning or a calibration defect warrants it. Choose thresholds on development/policy data, then freeze them. Predicted class proportions alone establish neither miscalibration nor threshold causality.
5. Export, reload and replay. Save the entire preprocessor/model/label/policy bundle and environment. Reload in a fresh process without fit data. Test normal, ambiguous, empty, long, invalid and shifted inputs plus action-boundary cases. Compare outputs and end-to-end actions under prespecified tolerances, not a universal demand for bitwise identity. Measure the actual deployment path.
6. Improve and confirm. Follow bounded improvement. Use development errors to choose the next data, model or program change. Search data may be reused descriptively; independent confirmation stays outside candidate selection. Stop on acceptance, budget, no useful progress or missing authority. An unsupported improvement leaves the incumbent active.

Match measurements to the claim

Report task loss and the applicable failure modes rather than filling every row of a universal checklist. Probability consumers need reliability evidence; option readers need permutation tests mapped back to option identities; rankers need query-level evaluation; classifiers need per-class and rare-case errors. All serving paths need resource/failure measurements relevant to their contract.

Use compute envelope before any expensive run. No new rental, paid model call, upload or production activation follows merely from loading this skill. Reuse authorized local resources and existing tooling. Measure bottlenecks before buying capacity; uncertain projections are not promises.

The companion scripts perform narrow checks, not training or autonomous search. Use only those needed: binary probability evaluation, paired outcome comparison, declared provenance, overlap audit, or ledger replay. Their exact inputs and limits are wired in the references above; do not copy research benchmark drivers into the app. For the commands, set AUGUSTUS to the absolute companion directory reported by the skill loader (in this checkout, .agents/skills/augustus), not this skill's path.

Finish with a usable result

Deliver the fit and inference commands, versioned artifact and policy, data/split and source identities, evaluator and representative receipts, rejected trials, resource cost, and acceptance decision. State whether evidence is fixture, proxy, adjudicated or observed, and distinguish exploratory from confirmed results. Keep known-overlap benchmark rows as labeled diagnostics when useful, not clean generalization evidence. Package-only is not deployed. For authorized promotion, exercise the application journey, observe independent outcomes and preserve rollback; otherwise return the runnable candidate with the incumbent retained.