airtable-knowledge-extract

name	airtable-knowledge-extract
description	Extract knowledge từ Airtable bases vào vault dưới dạng linked entity pages. Sonnet worker + Opus reviewer ≥95% gate, mandatory HITL re-anchor mỗi base mới. Use khi seeding historical knowledge từ company Airtable bases.

/airtable-knowledge-extract — Airtable → Vault Knowledge Extractor

Phase 1 MVP. Pure-TypeScript bun pipeline (scripts/run.mts) drives a per-base × vertical-slice extract from Airtable into a per-run cache (~/.claude/airtable-extract-cache/<run-id>/). Sonnet worker writes entity slices; Opus 4.6 reviewer gates each 10-slice batch at ≥95%; self-improver appends rejected examples; re-anchor enforces a mandatory HITL teach round on every new base. Settled design: {vault}/daily/grill-sessions/2026-04-27-auto-eval-scale-pipeline.md.

Vault Path

Read from ${CLAUDE_PLUGIN_ROOT}/brain-os.config.md (or user-local ~/.brain-os/brain-os.config.md, which takes precedence — same logic as hooks/resolve-vault.sh, ported to TS in scripts/outcome-log.mts). Key: vault_path:. The outcome log lands at <vault_path>/daily/skill-outcomes/airtable-knowledge-extract.log.

RUN MODE — Pattern B: never orchestrate from session; launch via supaterm tab

This is a Pattern B pipeline (rationale: {vault}/thinking/aha/2026-04-18-orchestration-on-script.md). The bun runtime owns the per-base loop; claude -p worker subprocesses do per-slice work. A real run takes ~30–60 min per base.

Rule 1 — never orchestrate from a Claude session. Each Bash tool result re-reads the full transcript; a 30-min in-session orchestration burns Opus context-tokens at $300+/run. Do NOT poll, Monitor, or TaskUpdate during the loop.

Rule 2 — supaterm tab + env -u CLAUDECODE is how Claude launches it. sp tab new --script creates a background supaterm tab that survives the Claude session. Stripping CLAUDECODE from the env makes child claude -p workers run outside the parent session context.

When the user invokes /airtable-knowledge-extract, print the ## Run block below verbatim and stop in-session. Do not Bash it from the session. The user pastes the block (after filling in <list-of-base-ids>) into their terminal or invokes the supaterm command directly.

Run

Resolve the bases to process up-front via scripts/active-bases.mts (which internally calls list-bases.mts, applies the inclusion + exclusion rules from references/base-selection-rules.md, and emits the filtered set). Then chain the per-base pre-steps and the orchestrator inside one supaterm tab:

RUN_ID="$(date -u +%Y-%m-%d)-airtable-extract"
SKILL_DIR="${CLAUDE_PLUGIN_ROOT}/skills/airtable-knowledge-extract"
sp tab new --script "env -u CLAUDECODE bash -c '
  set -euo pipefail
  cd \"$SKILL_DIR\"
  ACTIVE_IDS=\$(AIRTABLE_RUN_ID=\"$RUN_ID\" bun run scripts/active-bases.mts --ids)
  for BASE_ID in \$ACTIVE_IDS; do
    AIRTABLE_RUN_ID=\"$RUN_ID\" bun run scripts/classify-table-type.mts  \"\$BASE_ID\" > /dev/null
    AIRTABLE_RUN_ID=\"$RUN_ID\" bun run scripts/classify-clusters.mts    \"\$BASE_ID\" > /dev/null
    AIRTABLE_RUN_ID=\"$RUN_ID\" bun run scripts/cluster-classifier.mts   \"\$BASE_ID\" > /dev/null
    AIRTABLE_RUN_ID=\"$RUN_ID\" bun run scripts/schema-analyser.mts      \"\$BASE_ID\" > /dev/null
    AIRTABLE_RUN_ID=\"$RUN_ID\" bun run scripts/legacy-link-detector.mts \"\$BASE_ID\" > /dev/null
    for CID in \$(jq -r \".[].cluster_id\" \"\$HOME/.claude/airtable-extract-cache/$RUN_ID/clusters-\$BASE_ID.json\"); do
      AIRTABLE_RUN_ID=\"$RUN_ID\" bun run scripts/seed-selection.mts \"\$BASE_ID\" \"\$CID\" > /dev/null
    done
    AIRTABLE_RUN_ID=\"$RUN_ID\" bun run scripts/rubric-author.mts \"\$BASE_ID\" > /dev/null
    bun run scripts/run.mts --run-id \"$RUN_ID\" --base \"\$BASE_ID\" -P 1 \\
      2>&1 | tee -a /tmp/airtable-extract-\"$RUN_ID\".log
  done
  sleep 10
'"

The pre-step chain feeds run.mts's internal dispatcher (added in #237/#268). Order: classify-table-type → Stage 0 LLM table classifier (cached at ${SKILL_DIR}/cache/table-types/); classify-clusters → link-graph clusters; cluster-classifier → cluster-shapes (orphan|pair|chain|tree|hub-spoke|graph); schema-analyser → natural keys + entity-table verdicts; legacy-link-detector → cleanup-tasks; seed-selection → per-cluster seed records (the inner jq loop reads clusters-<base-id>.json to enumerate cluster IDs — seed-selection.mts requires <base-id> <cluster-id> and exits non-zero if cluster-id is omitted). Skipping any of these aborts run.mts with cache not found at ... from the dispatcher.

On the first invocation within a run-id, active-bases.mts writes both ~/.claude/airtable-extract-cache/<run-id>/bases.json (raw Meta API listing, via the wrapped list-bases.mts) and ~/.claude/airtable-extract-cache/<run-id>/active-bases.json (filter trace per base). On any subsequent invocation with the same run-id, the cached trace is read and the API probes are skipped.

For the first run on a new base, keep -P 1 so re-anchor's rung-0' shallow slice gates HITL approval before any parallel rung-1 sweep. Once the user has approved the re-anchor and the rubric, raise to -P 5 for rung-1 throughput.

rubric-author.mts defaults to an interactive readline grill — one question at a time with a concrete sample record, accepting k/d/rewrite/+/batch shortcuts. Prompts go to stderr so the > /dev/null redirect on stdout doesn't suppress the UI; the operator switches to the supaterm tab when it pauses on input. Pass --editor to fall back to the original $EDITOR-based flow for non-interactive contexts. Full shortcut table + plumbing notes: references/rubric-author-flow.md.

HITL — export-decision-and-exit (re-anchor + self-improve)

Both re-anchor.mts and self-improve.mts use the export-decision-and-exit pattern (issue #232). Workers do NOT block on stdin or poll a file mid-run. Instead:

1. Worker computes its decision context (slices, verdict, sample entities for re-anchor; raw + reasoning for self-improve).
2. Worker writes the context to a known JSON path.
3. Worker EXITS with code 42 (HITL_PENDING_EXIT_CODE).
4. run.mts propagates exit 42 → updates state.json phase=hitl-pending → exits 42 itself with a one-line operator pointer.
5. set -euo pipefail in the supaterm bash chain bails the for-loop.
6. Agent (Claude in chat) detects the *-decision-needed.json file, reads context, drives a grill conversation with concrete examples (per the rubric-author #228 pattern), and writes the decision JSON to the spec'd decision_path.
7. Operator re-runs the same supaterm chain; the for-loop walks confirmed bases (no-op fast-paths) until it reaches the paused base; the worker detects the decision file, consumes (and deletes) it, and proceeds.

Decision file paths and shapes:

Phase	Needed file (worker → agent)	Decision file (agent → worker)
re-anchor	<runDir>/hitl/re-anchor-decision-needed.json	<runDir>/hitl/re-anchor-decision.json — {verdict: "approve"|"reject", notes, rewrites?}
self-improve (per slice)	<basePath>/corrections/correction-needed-<slice_id>.json	<basePath>/corrections/correction-decided-<slice_id>.json — {corrected, reason}

Notes:

• The needed-file payload includes a wake_token (uuid). It is informational; the decision file does not need to echo it.
• For self-improve, selfImprove is atomic in two-pass mode: if any reject lacks a decision file, it writes needed-files for the missing ones only and throws HitlPendingError without consuming any decisions that are already present.
• For non-interactive contexts (CI smoke runs, scripted tests) where the legacy stdin / file-poll behavior is required, pass --strict-stdin-hitl to re-anchor.mts or self-improve.mts.

Status & analysis

Both are read-only one-shots — safe to Bash from a session when the user asks "how's it going?".

# live snapshot (human-formatted)
bun run ${CLAUDE_PLUGIN_ROOT}/skills/airtable-knowledge-extract/scripts/status.mts <run-id>

# JSON snapshot for /improve / dashboards
bun run ${CLAUDE_PLUGIN_ROOT}/skills/airtable-knowledge-extract/scripts/status.mts <run-id> --json

# user-only — do NOT call from a session, it loops:
# tail -f /tmp/airtable-extract-<run-id>.log

Outputs

Path	Purpose
~/.claude/airtable-extract-cache/<run-id>/state.json	Orchestrator resume state (single source of truth for --run-id resume)
~/.claude/airtable-extract-cache/<run-id>/cost-meter.jsonl	Per-call token usage (guard input + cumulative reporter)
~/.claude/airtable-extract-cache/<run-id>/bases.json	Cached Airtable Meta API bases listing (raw, unfiltered)
~/.claude/airtable-extract-cache/<run-id>/active-bases.json	Filter trace per base — {id, name, included, excluded_by, last_activity, table_count}
~/.claude/airtable-extract-cache/<run-id>/clusters-<base-id>.json	Cluster classification cache (link-graph components)
~/.claude/airtable-extract-cache/<run-id>/seed-records-<base-id>-<cluster-id>.json	Seed record selection cache
~/.claude/airtable-extract-cache/<run-id>/bases/<base-id>/out/<entity-type>/<slug>.md	Extracted entity page — frontmatter + body with [[wikilinks]] to other extracted slugs
~/.claude/airtable-extract-cache/<run-id>/bases/<base-id>/out/airtable-cleanup-tasks.md	Detected legacy / broken Airtable cross-cluster links
~/.claude/airtable-extract-cache/<run-id>/bases/<base-id>/examples.jsonl	Self-improver per-base examples bank (raw / corrected / reason triples)
~/.claude/airtable-extract-cache/<run-id>/metrics/base-<base-id>-rung-<n>.jsonl	Per-base run-summary (batch outcomes, pass-rate, consec-fails)
<vault>/daily/skill-outcomes/airtable-knowledge-extract.log	Outcome log line per skill-spec § 11 (one row per run.mts invocation)

Entity pages stage in the cache, not the vault, until the user reviews via sp tab focus and merges into the live vault. This is intentional Phase 1 design — the vault is the import destination, not the live extraction target.

Contract

• Model tiers pinned. Worker = claude -p --model sonnet; reviewer = claude -p --model opus. Drift caught by tests/extract-slice.test.ts and tests/review-slice.test.ts.
• Reviewer gate ≥95% per 10-slice batch. Below threshold: self-improver appends (raw, corrected, reason) to per-base examples.jsonl, retry runs at Math.max(1, floor(P/2)). Two consecutive fail-after-retry outcomes → SIGTERM workers + osascript notify + exit kind=escalated-batch.
• Mandatory re-anchor on every new base. Rung-0' shallow slice + HITL approve before rung-1 sweep. Rejection → exit kind=escalated-re-anchor, base re-tried on the next run-id (no auto-promotion).
• Idempotent resume. Re-running the same --run-id picks up at the last persisted batch index; seed-selection.mts's deterministic cache is what makes this safe.
• Pure TypeScript via bun (per feedback_scripts_ts_bun_only.md). Unit tests under tests/*.test.ts; integration smoke under tests/integration.test.ts.
• Cost-meter is the source of truth for spend. Guard polls it every 30s and SIGTERMs workers on max-tokens / max-wallclock breach (calibrated from rung-0 observation, not user-set upfront).

When NOT to use

• Two-way sync into Airtable — read-only, Phase 1.
• Cron-scheduled live re-import — one-shot per invocation in Phase 1.
• Bases mid-schema-rewrite — re-anchor will reject; settle the schema first.
• Datasets so small a manual copy is faster — heuristic: <50 records across <3 tables, just copy by hand.

Outcome log

Follow {vault}/skill-spec.md § 11. Two action types appear in this log — extract (main extraction loop) and dispatch (schema-driven dispatcher, scripts/dispatcher.mts, per #237).

extract action — appended by scripts/run.mts CLI main() via scripts/outcome-log.mts:

{date} | airtable-knowledge-extract | extract | ~/work/brain-os-plugin | <metrics-or-cache-path> | commit:none | {pass|partial|fail} | run_id=… base_id=… batches_done=… total_batches=… pass_rate=…

• pass — kind=paused (base completed) or kind=no-bases (queue drained).
• partial — kind=hitl-pending: the HITL re-anchor gate exited with code 42 and awaits an operator decision. Read <runDir>/hitl/re-anchor-decision-needed.json, drive the grill conversation using the provided context, write <runDir>/hitl/re-anchor-decision.json ({verdict: "approve"|"reject", notes}), then re-run the same supaterm chain to resume.
• fail — kind=escalated-re-anchor (operator rejected the re-anchor shallow slice — review what the slice lacked via the re-anchor-decision-needed.json context, adjust the rubric via rubric-author.mts or fix the base schema, then retry with a new run-id; no auto-promotion) or kind=escalated-batch (batch pass-rate below ≥95% threshold after retry — check examples.jsonl for the failing slices).
• commit:none — extracted pages stage in cache, not in a tracked repo, so there is no anchor commit hash for diff detection.

dispatch action — appended by scripts/dispatcher.mts after computing the schema-driven dispatch plan for a base component:

{date} | airtable-knowledge-extract | dispatch | ~/work/brain-os-plugin | ~/.claude/airtable-extract-cache/<run-id>/dispatch-<base-id>.json | commit:none | {pass|fail} | run_id=… base_id=… component_id=… shape=… records=… est_tokens=… strategy=… interaction_handler=…

• pass — dispatcher resolved the component shape and wrote the dispatch plan JSON.
• fail — dispatcher could not resolve the component (unrecognized shape, no extractable tables, or NK-scan failure).
• Optional fields: component_id (graph component id), shape (orphan|pair|chain|tree|hub-spoke|graph), records (record count in component), est_tokens (estimated token budget), strategy (edge-emit|single-pass|chunk-by-AP|record-by-record), interaction_handler (dispatch execution mode).

If result != pass, auto-invoke /brain-os:improve airtable-knowledge-extract only for unexpected failures (script errors, API failures). HITL escalations (partial=hitl-pending, fail=re-anchor) are by-design exits that need operator action, not SKILL.md changes.