source-preflight — source preprocessing acceptance gate (zero semantic LLM)

Run the deterministic CLI preprocessing chain on a candidate source and decide whether its staging artifacts are good enough to enter ingest. This skill is a thin wrapper: it only orchestrates scripts/pipeline.py and does no LLM semantic splitting / unit planning / page writing. Project truth: CLAUDE.md / AGENTS.md. Engineering format: docs/skill-runtime/skill-standard.md.

1. Triggers / Non-triggers

• Triggers: "preprocess this PDF first", "run source-preflight", "build the source profile first", "see if it can be ingested", "just run up to workorder".
• Non-triggers: "add to the KB / index it" with page writing (use ingest); "summarize/split this book" without ingesting (use source-xray, published content only); "query the wiki" (use kb-query); "translate/explain" (a normal answer).

2. Inputs

• The user gives: file path <path>, domain <domain>; <fmt> inferred from the extension (pdf/md/docx/pptx); <src> derived from the filename. Confirm <src> and <domain> once.
• Read: the zero-LLM preprocessing constraint in CLAUDE.md / AGENTS.md, and docs/skill-runtime/schema.md (to understand the workorder write boundary).

3. Outputs

• pipeline-workspace/staging/<src>/{source.md, blocks.jsonl, chapters.json, parse_report.json, reconciliation.json, windows.jsonl, workorder.yaml, preflight_eval.json} + hard-page PNGs / figure assets.
• An optional deterministic report pipeline-workspace/reports/source-preflight/<src>.md: from parse_report.json + reconciliation.json show backend, dual-audit status (dual_audited / degraded / disagreements), OCR, table/equation/image counts, discarded (header/footer) count, warnings, and an ingest recommendation; plus CLI status, page count, needs_vision pages, degraded warnings, window coverage, workorder write_scope. No semantic summary.
• preflight-eval's deterministic JSON (preflight_eval.json): page coverage, window monotonicity, table/image/chart asset + source_ref traceability, the dual-audit gate (check_dual_audit), scan/OCR & low-confidence pages, orphan blocks — reads existing artifacts only, no LLM; --strict returns non-zero on a high/fail (CI-able).
• No semantic wiki content pages, no status: proposed pages, no concept-page updates.

4. Dependencies

• CLI: init-vault, add-source, profile, source-convert, source-audit, windows, workorder, preflight-eval, status.
• The actual ingest is handed to ingest; this skill does not inline ingest phases B/C/D/E/F.
• Protocols: docs/skill-runtime/skill-standard.md and docs/skill-runtime/schema.md.

5. Persisted artifacts

• pipeline-workspace/staging/<src>/source.md, reconciliation.json, windows.jsonl, workorder.yaml.
• pipeline-workspace/staging/<src>/assets/pXXXX.png (needs_vision pages).
• pipeline-workspace/reports/source-preflight/<src>.md (if written, deterministic facts only).

6. CLI commands

python scripts/pipeline.py init-vault
python scripts/pipeline.py add-source --source <src> --domain <domain> --path <path> --fmt <fmt>
python scripts/pipeline.py profile --source <src>
python scripts/pipeline.py source-convert --source <src>
python scripts/pipeline.py source-audit --source <src> [--strict]   # PDF dual-audit → reconciliation.json + evidence.json + arbitration/queue.json
python scripts/pipeline.py arbitration-status --source <src>        # if pending: agent auto-arbitrates the queue → arbitration-apply (protocol: ingest/references/arbitrate.md), before windows
python scripts/pipeline.py arbitration-resolve --source <src> --page <n> --decision render|ignore --reason "..."  # optional: close a needs_human page, then re-apply
python scripts/pipeline.py windows --source <src>
python scripts/pipeline.py workorder --source <src>
python scripts/pipeline.py preflight-eval --source <src> [--strict]
python scripts/pipeline.py status

Each step is idempotent; on any error, stop — do not skip. For a PDF, windows is fail-closed: it refuses to build unless source-audit has produced reconciliation.json + evidence.json + arbitration/queue.json AND every disagreement is closed (PyMuPDF thresholds are deliberately broad and are not a single source of truth). The only way to build a PDF's windows without a completed source-audit is the explicit dev escape hatch windows --dev-bypass, whose output is degraded / not for strict acceptance. Strict acceptance still requires the dual-audit to pass, and MinerU unavailable in strict mode is fail-closed. preflight-eval reads existing staging only, zero LLM; --strict exits non-zero on a high/fail (a hard gate before switching to ingest).

7. Workflow

8. Failure stops / recovery

Path missing; unsupported fmt; any CLI step fails; source-convert missing backend; PDF dual-audit fail-closed in strict (MinerU unavailable); windows.jsonl does not cover the full text; workorder.yaml not generated; a formula risk page has no PNG; preflight-eval --strict high/fail (non-zero exit); the user asks for LLM unit planning or semantic splitting. Recovery: every step is idempotent — fix the cause and re-run from the failed step; pipeline status shows where it stopped.

9. Acceptance criteria

• No semantic wiki pages written, no status: proposed content pages.
• source.md, reconciliation.json, windows.jsonl, workorder.yaml exist.
• workorder.yaml contains write_scope and the registry hash.
• PDF dual-audit recorded (reconciliation.json dual_audited=true, or a degraded/blocker recorded).
• PDF dual-audit disagreements arbitrated + materialized: preflight-eval's check_evidence_bundle is green (no un-arbitrated / un-materialized / pending needs_human), or a blocker recorded.
• needs_vision pages have a PNG, or a blocker is recorded.
• preflight-eval checks have no high/fail (--strict exit 0), or a blocker is recorded.
• The report contains deterministic facts only — no semantic summary / chapter interpretation.