data-exhaust

name	data-exhaust
description	Package a terminal experiment's data exhaust (aggregate stats and, where license-permitted, row-level generation text) into reproducible Hugging Face datasets, with a license gate, containment lint, and a thin uploader. Use when an experiment under experiments/<slug>/ reaches a terminal verdict (resolved, null-result, or falsified) and its analysis-committed/ artifacts should be published.
allowed-tools	Read, Bash, Write, Grep, Glob

Data Exhaust

An experiment's exhaust is everything worth publishing once it reaches a terminal verdict: the aggregate artifacts already committed under analysis-committed/<cell_id>/ (dose-response tables, direction fits, gate AUROCs, manifests) and, separately and only where the license permits it, the per-row generation text. This skill turns that exhaust into HF-ready dataset directories, gates them, and uploads them. It never decides WHAT an experiment found; it only packages what the experiment already produced.

When to use

An experiment has reached a terminal status (resolved, null-result, or falsified in experiment.yaml) and its analysis-committed/ artifacts, or locally staged row-level JSONL, are ready to become a public HF dataset. Do not run this on a draft, signed, or running experiment: there is nothing terminal to package yet, and publishing mid-run invites goalpost confusion about what the released data actually represents.

Build-time requirement (binding on every harness, not just packaging)

Exhaust can only be packaged if the run persisted it. Every generation harness MUST write, per row or per sample, into its gitignored row-level run log: the raw generation text, the FULL sub-grade dict its grader computes (not just the final booleans), and the termination/readback inputs the gate math consumes. Booleans-only run logs are a build defect: they make failure anatomy unrecoverable without a paid re-run, and they leave response-quality problems (degenerate output, format-checker artifacts, batched-decode misgrading) undetectable after the fact. The cautionary case is experiments/snap-seed-sampled-decode-replication (H3, 2026-07-13): its grader computed the complete sub-grade dict and the pipeline discarded it, so the registered falsifier fired with the failure mechanism undiagnosable from committed artifacts and the resolve PR had to be held for a text-persisting re-run. Containment is unchanged by this rule: text stays under gitignored analysis/; only ID-manifests and aggregates go to analysis-committed/; publication still runs through the license gate below. Harness builders and pre-sign reviewers both check this: a CPU smoke that asserts the persistence schema (text present, sub-grade dict intact) is part of the standard suite.

This rule is now structurally enforced, not just procedural. Generation harnesses open their row-level run log through experiments/common/runlog_contract.py's open_generation_runlog, which refuses to accept any record missing non-empty text unless the harness declares a textless_reason (durably recorded in the log's own meta fingerprint). bin/exp validate separately requires every experiment's experiment.yaml to carry a top-level text_capture field (enabled, not-applicable, or textless: <reason>); a missing or invalid value is a hard error. textless: is the only sanctioned opt-out from either layer -- there is no way to omit text capture silently.

Start here

Task	Do
Understand the two dataset shapes and their fields	read reference/dataset-schema.md
Check or update a source's redistribution verdict	read reference/license-gates.md
Build a dataset dir	run scripts/build_exhaust_dataset.py (see below)
Gate a built dataset dir before upload	run scripts/verify_exhaust.py
Actually upload	run scripts/upload_exhaust.py

The five-move workflow

1. License-gate. Before building anything with row text, confirm every source that will appear has a verdict in reference/license-gates.md. Each source resolves to one of three row dispositions: full text (permitted / permitted-with-conditions), text-free (text-free-only, row kept but its text-bearing fields stripped), or excluded entirely (forbidden / pending-audit). A source with no table entry is pending-audit, the most conservative disposition, not the text-free one. Aggregate-only builds do not need this step to have resolved verdicts, since they carry no source text, but they still run the same structural hard-exclusion scan.
2. Build. Run build_exhaust_dataset.py against the experiment directory. Aggregate shape (default) recursively copies EVERY file under analysis-committed/ -- any depth, flat or celled layout -- byte-for-byte into <out-dir>/, preserving relative paths. There is no filename allowlist: analysis-committed/ is already the repo's containment boundary, so the only filter applied is the hard-exclusion deny-list (a file whose relative PATH matches OpenMOSS/Cheng IDK or bridge_llama2_7b_chat is skipped and recorded with a reason; a file whose CONTENT matches one aborts the whole build). Row-level shape (--rows-dir <dir>) reads locally staged <cell_id>.jsonl files and gives every row one of the three dispositions above, per row, based on its own source field -- a single rows.jsonl can mix full-text and text-free rows from different sources. Excluded rows are counted, not written in any form. The build never writes inside the experiment's own directory; only --out-dir is touched.
3. Verify. Run verify_exhaust.py --dataset-dir <out-dir> --experiment-dir experiments/<slug> against the build output. It checks schema/sha256 consistency, row counts against PROVENANCE.json, a containment lint over every file (including an independent PER-ROW re-check of each row's license-gate disposition, so a file mixing sources only passes if each row matches its own source's rule), that any permitted-with-conditions source's disclosure text actually landed in the README, that license-gates.md's machine-readable table is well-formed and still carries both hard exclusions, and (aggregate shape) a completeness check that independently re-walks the source tree right now and requires the staged files plus the recorded exclusions to equal it exactly -- omitting FAILS this check rather than skipping it silently, so always pass it. A single failure fails the whole gate loudly; it does not report a partial pass.

Publication gate

Do not run upload_exhaust.py --live until:

1. The experiment's experiment.yaml status is terminal (resolved, null-result, or falsified), not draft/signed/running.
2. verify_exhaust.py passed with zero errors on the exact dir about to be uploaded.
3. For a row-level dataset, every source appearing has an explicit permitted, permitted-with-conditions, or text-free-only verdict in reference/license-gates.md -- not pending-audit. For any permitted-with-conditions source, its disclosure text must actually be in the built README (verified, not just built).
4. The user has explicitly approved this specific upload. A prior approval for a different experiment or a different dataset shape does not carry over.

This mirrors the model-artifact publication gate in .skills/experiment-runner/reference/hf-publication.md; that reference stays the source of truth for model checkpoints, this skill is the analogous gate for exhaust data.

Invariants

• The public git repo never receives question text, answer aliases, row-level generation text, or token IDs. HF is the sanctioned text channel, and only for license-permitted sources. Do not commit a built dataset dir; the --out-dir you point the builder at should be a scratch location outside version control.
• Two hard exclusions apply regardless of anything else: OpenMOSS/Cheng IDK data and bridge_llama2_7b_chat. These are enforced structurally in code (scripts/build_exhaust_dataset.py and scripts/verify_exhaust.py), not only by the license-gates table, so an accidental table edit cannot reopen them.
• A source with no entry in reference/license-gates.md is pending-audit (excluded entirely), never text-free-only and never permitted. Never infer a permissive verdict from a different dataset's card or from memory of a prior release.
• HF_TOKEN comes from the environment only. Never print, log, or echo it.
• Quick commands:

python3 .skills/data-exhaust/scripts/build_exhaust_dataset.py \
  --experiment-dir experiments/<slug> \
  --out-dir /tmp/<slug>-exhaust-aggregate

python3 .skills/data-exhaust/scripts/verify_exhaust.py \
  --dataset-dir /tmp/<slug>-exhaust-aggregate \
  --experiment-dir experiments/<slug>   # required for the aggregate completeness check

python3 .skills/data-exhaust/scripts/upload_exhaust.py \
  --dataset-dir /tmp/<slug>-exhaust-aggregate   # dry run by default; add --live once approved

Skill maintenance

Edit the canonical tree under .skills/data-exhaust/ only. .agents/ and .claude/ are generated mirrors; never hand-edit them. After canonical edits:

python3 bin/sync_skills.py --write --skill data-exhaust
python3 bin/sync_skills.py --check --skill data-exhaust