Tao Run Deft Pas

PAS DEFT Workflow

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first for host preflight, credential checks, and cross-skill discovery.

Run the canonical PAS flow as one resumable, disk-backed workflow. All PAS workflow logic, templates, and host adapters ship with this skill; customers do not need a separate source checkout. The bundled scripts make stage calls deterministic without adding another orchestration layer.

This skill supports every packaged TAO execution platform: Docker, SLURM, Kubernetes, Brev, and virtualenv. The workflow produces a platform-neutral, schema-validated action bundle; the selected platform skill owns native submit/status/logs/cancel and the job-record. Virtualenv execution uses separate immutable pyt and ds runtime profiles; the workspace control .venv is not an execution runtime.

Entry Contract

tao-deft-pas and tao-run-deft-pas select this same workflow. If the user did not choose a platform, ask once among Docker, SLURM, Kubernetes, Brev, and virtualenv; never default to Docker. On resume, the immutable platform in deft_state.json is already the selection and must not be changed.

Use two intake phases:

1. Perform only bounded, lightweight path discovery. Two explicit archive file paths win and suppress all archive searching. An explicit archive directory is treated as one approved search root. Otherwise, resolve the conventional ~/workspace and use only these deduplicated search roots: ~/pas, the workspace root, and its pas/, input/, and inputs/ children. Check the workspace root itself only; beneath each other search root, inspect the root and directories at most two levels below it. This AOI-style bounded-subtree lookup supports nested export/drop directories without turning into a home or repository scan. Never follow symlinks or add the current checkout, source repositories, tutorial/notebook trees, or workspace dataset-output trees as implicit search roots.

   An archive candidate contains direct regular-file children images_raw.tar and meta.tar.gz; the workflow needs one such pair, not multiple dataset directories. Follow the classification and provenance format in references/preflight.md so the user sees every search root, its reason and depth bound, the directory in which a pair was found, and whether it is archive-only or mixed with extracted data.

   Enumerate run-state files only at <workspace>/results/run_*/deft_state.json. Read the minimal identity fields and present a resume candidate only when workflow is exactly tao-run-deft-pas; never offer AOI or unidentified DEFT state as PAS. Do not validate large archives to EOF or inspect platforms, images, GPUs, or credentials yet.

2. If max_iterations or a time budget is absent, ask one consolidated question for that required value and any genuinely ambiguous path/run choice. State the documented defaults and that a complete read-only preflight plus approval summary follows. Do not ask whether the user wants optional KPI, authentication, or parameter overrides; apply their defaults unless the prompt already supplies an override.

After required intake is resolved, discover and validate:

• workspace and either a new ${RESULTS_DIR} or one existing run to resume;
• images_raw.tar and meta.tar.gz; SHA256SUMS is optional;
• max_iterations, or a user-supplied time budget from which an iteration limit can be estimated;
• metric name, query type, operator, and optional target;
• whether the deployment requires authenticated Hugging Face model access;
• selected TAO execution platform and its platform-specific prerequisites;
• any explicit epoch, GPU, mining, continual-learning, or visualization overrides.

For a new run, RESULTS_DIR must be a child of WORKSPACE. DATASET_ROOT must be below a workspace data directory (for example $WORKSPACE/data/pas_v31_tao_ft), not directly below the workspace; neither path may contain the other. All approved paths are absolute and non-symlink.

Never replace an explicit value with a heuristic. Defaults apply only to unspecified values and must be identified as defaults in the pre-flight summary. max_iterations has no default. An absent metric target means an ungated run that stops after max_iterations. Hugging Face token forwarding defaults to disabled because the bundled model is public.

The authoritative parameter contract is the nested dataclass schema in scripts/pas_deft/config.py, adapted from the PAS reference notebook. Read that schema when a request needs the meaning, default, numeric bounds, or valid options of a DEFT parameter. Do not infer an undocumented field or bypass its metadata constraint. Config preparation, initialization, audit, and every host-side stage validate the materialized bundle through that same schema; the pas YAML section is validated through the typed PAS configuration model.

If required information remains missing after full discovery, ask one consolidated follow-up. A normal invocation should need no knowledge of stage modules, container mounts, state files, or bundled-runtime function signatures.

Safety Gate

Perform only read-only discovery before approval: resolve paths, inspect file metadata and archives, check process-environment variable presence, inspect local images, inspect GPUs, and audit an existing run. Credentials come only from the launching process environment; never open or source a credential file, and never print, grep, copy, or echo a credential value. If a required variable is absent, tell the user which name to export in the shell that launches the agent; never ask for its value in chat. Do not inspect credential-file metadata when no credential is needed. If the user explicitly asks for a file-permission check, stat only that named file and warn about group/other readability.

Show the summary defined in references/preflight.md, including every parameter and source, planned file creation/extraction, image pulls, estimated runtime, and resume status. Wait for explicit approval before registry login or pulls, platform submit, package installation, archive extraction, config/state creation, or any write under the workspace.

If an approved parameter later changes, show the changed summary rows and get approval again before continuing. No confirmation is needed between unchanged, already-approved stages.

Execution Contract

1. After approval, follow references/preflight.md to prepare the runtime, materialize the immutable run config, and initialize state once. Never reinitialize an existing run.

2. Before every stage, after context compaction, and before any completion claim, run:

   "$SKILL_ROOT/scripts/deft_python.sh" --workspace "$WORKSPACE" --runtime \
     "$SKILL_ROOT/scripts/audit_deft_run.py" --results-dir "$RESULTS_DIR"

   Continue only when the audit reports IN_PROGRESS and its next_action matches the intended stage. For INVALID, stop launching work and report the listed inconsistency. For nonterminal FAILED whose next_action is loop_stop, follow the pipeline reference and commit hard_stop; for terminal FAILED, report the failure and launch no more work. For COMPLETE, do not rerun a stage.

3. Read only the current stage reference named by read_before_action. Use run_pas_stage.py for bundled PAS host stages. For every TAO action, use run_deft_action.py prepare, reconcile any interrupted launch, bind the exact request-owned job-record before native submit, dispatch the emitted bundle through the selected platform's four verbs, synchronize remote outputs, capture native logs, then use run_deft_action.py finalize. Follow references/platform-execution.md; never assemble an untracked launch.

4. A command succeeds only when its exit status is zero and its documented output checks pass. Capture verbose output at the action-owned log path; inspect the final error block or at most the last 40 lines.

5. Commit each successful or terminally failed stage exactly once with commit_stage.py. It validates artifact structure, freshness, iteration scope, command evidence, leakage, and transition order before applying a recoverable, journaled update to deft_state.json and loop_log.jsonl. Never call log_stage.py or record_metric_result.py directly, and never hand-edit canonical state, log, metric, or history files.

6. Render the HTML report with render_deft_report.py. Reporting is a deterministic read of audited state; it does not require another agent.

7. Keep the driving turn attached through the complete bounded loop. After a stage reaches terminal status, finalize/commit it, re-audit, and immediately continue with the audit-selected next action. An in-progress update is commentary, not a final response and not a detach point.

All recorded artifact paths are absolute host paths under ${RESULTS_DIR}. Baseline artifacts live under zs/, iteration N under iter_N/, and run-wide splits, source embeddings, and history files at the run root.