Gaia Ingest Batch

This is a coordinator, not a second evidence-ingestion implementation. Every
row follows the contract in /gaia-ingest; this skill only sequences verified
rows efficiently and finalizes the resulting registry artifacts.

Input manifest

Prepare a reviewed manifest with one row per source:

rows:
  - skill: firecrawl/web-scrape-integration
    source: https://www.youtube.com/watch?v=...
    evidenceType: social-signal
    payload:
      views: 8510
    sourceStartedAt: 2025-07-20
    notes: "Third-party tutorial explicitly demonstrates page scraping."

The manifest must state a source URL, Evidence Type, source-start date,
verifiable numeric payload, factual notes, and attribution scope for every row.
Exclude any deferred candidate. Never infer an Evidence Type or a metric from a
summary. For benchmark-result, attach the Phase 2B benchmark-source report and
human approval for the lane: verified rows need CI/verifier attestation plus
runAt, datasetHash, and benchmarkInputHash; reported rows may be public
claims or mirrored benchmark evidence approved by the human gate and do not need
those reproducibility fields. rejected, pending, candidate, retired, or
unknown benchmark sources/rows must not enter ingestion as scoring rows. A
catalog status: rejected is the blacklist. appliesToGenericSkillRefs is
catalog metadata only; it does not create a named score without a named
benchmark-result row.

Procedure

1. Verify /ev-pipeline completed for the manifest’s sources and link health
   is recorded. Reject dead, unverified, duplicate, or scope-mismatched rows.
   For benchmark-result, require Phase 2B + Phase 3 + Phase 4 completion and
   explicit human gate approval before any row is ingested.

2. For each row, invoke the /gaia-ingest contract and execute its exact
   gaia dev evidence ... --no-build command. Process one row at a time and
   stop on the first CLI preflight or source-verification failure.

3. Appraise each affected skill after its final row:

   PYTHONPATH=src python3 scripts/trust_appraise.py --skill <contributor/skill-id>

4. Present proposed calibrations. Do not calibrate without explicit operator
   approval. If approval is already recorded, run each approved calibration
   with --no-build.

5. Hand off the branch-close to /gaia-review-meta-close. Do not run the
   build/validate/stage/PR steps here — that skill owns the single build, the
   calibration+Origin gate, suite wiring via gaia dev fuse (so suiteComponents
   survives the build), upstream-naming correction, the LF-renormalized artifact
   allowlist (dropping CRLF churn and blocking leaks), the UTF-8-safe validate,
   and the PR. Pass it the branch, the affected contributor handles (for badge/og
   staging), the per-skill appraised TM/grade from step 3, and the intake issues
   to Resolves. This skill stops at "evidence ingested + appraised."

Output

Report, for every row: CLI command, source verdict, Evidence Type, row grade,
TM contribution, and duplicate/scope decision. Report, for every skill: final
TM, Overall Trust Grade, current level, and any calibration proposal. Then hand
these facts to /gaia-review-meta-close for the gated close-out.

Route suite creation only after components are ingested and appraised — the
capstone/suiteComponents wiring happens inside /gaia-review-meta-close
(gaia dev fuse), or via /gaia-fuse-full-suite for a standalone suite build.