tidas-data-import

name	tidas-data-import
description	Orchestrate SDK-backed TIDAS context, tidas-tools package conversion, and source-document authoring through the tiangong-lca CLI. Use for Foundry import tasks that start from packaged LCA datasets or PDF/Excel/source files.

TIDAS Data Import

Use this skill when Foundry needs to create or prepare TIDAS data from external inputs.

Boundaries

• This skill orchestrates CLI commands only.
• Do not embed schema, methodology YAML, ruleset, converter logic, or prompt templates in this skill.
• Do not vendor external source-evidence research skills into this repository.
• Fetch the target contract context before AI generation, conversion repair, or review.
• Treat CLI reports as the durable artifact contract for downstream Foundry routing.

Lane 1: Packaged LCA Dataset

Use this lane for zipped or directory-based packages that tidas-tools can detect or convert.

tiangong-lca dataset context-pack \
  --type process \
  --profile ai-import \
  --include schema,methodology,ruleset \
  --out-dir .foundry/workspaces/<task-id>/context/process \
  --json

tiangong-lca dataset import-lca convert \
  --input /abs/path/source-package \
  --output-dir .foundry/workspaces/<task-id>/conversion \
  --from-format auto \
  --target tidas \
  --json

Required verification artifacts:

• context/process/outputs/contract-report.json
• context/process/outputs/ai-context.md
• conversion/outputs/import-lca-report.json
• conversion/conversion-report.json
• conversion/tidas/ when conversion succeeds
• entity-level curation queue artifacts after downstream validation and QA: curation-queue/outputs/curation-queue-manifest.json, curation-queue/outputs/curation-queue-tasks.jsonl, curation-queue/outputs/curation-queue-locks.json, and curation-queue/outputs/curation-queue-blockers.jsonl

Lane 2: Source Document Authoring

Use this lane for PDF, Excel, web exports, screenshots, or free text that must be turned into TIDAS candidate data.

tiangong-lca dataset author \
  --input /abs/path/source.pdf \
  --output-dir .foundry/workspaces/<task-id>/authoring \
  --target-types process,flow \
  --language zh-CN \
  --json

Required verification artifacts:

• authoring/outputs/source-extract.json
• authoring/context/<type>/outputs/ai-context.md
• authoring/outputs/authoring-report.json

Source Evidence Runtime Skills

For source-document authoring that needs SCI paper or academic journal evidence, resolve the latest external research skill at runtime:

npx skills use https://github.com/tiangong-ai/skills \
  --skill tiangong-kb-sci-search \
  --full-depth

tiangong-kb-sci-search searches only the sci channel. Do not use it as a report, patent, general web, or all-source retrieval wrapper. If the task needs those other source classes, keep them as separate evidence channels.

When a Foundry task uses an external runtime skill, write a resolution record before retrieval:

.foundry/workspaces/<task-id>/runtime-skills/runtime-skill-resolution.json

The record should include the npx skills command, source repo, resolved refs/heads/main commit from git ls-remote https://github.com/tiangong-ai/skills.git refs/heads/main, skill name, evidence channel, timestamp, and output artifact paths. Retrieved papers are evidence candidates only; field values still need evidence dossier capture, limitations/conflicts, validation, curation, dry-run, and verification gates.

Downstream Gates

After either lane creates candidate TIDAS data, run the existing dataset gates that match the target type:

tiangong-lca dataset validate --help
tiangong-lca qa process --help
tiangong-lca qa flow --help
node scripts/foundry.mjs dataset-curation-queue-build --help
node scripts/foundry.mjs dataset-curation-gate --help
node scripts/foundry.mjs dataset-authoring-task-build --help
node scripts/foundry.mjs dataset-patch-apply --help
node scripts/foundry.mjs dataset-curation-cleanup --help
node scripts/foundry.mjs dataset-post-authoring-finalize --help

For structured external dataset imports, build the queue after schema validation and deterministic QA and before AI repair or publish preparation:

npm run dataset:curation-queue:build -- \
  --processes .foundry/workspaces/<task-id>/rows/processes.normalized.jsonl \
  --flows .foundry/workspaces/<task-id>/rows/flows.normalized.jsonl \
  --support .foundry/workspaces/<task-id>/rows/sources.normalized.jsonl \
  --out-dir .foundry/workspaces/<task-id>/curation-queue

The queue is the execution contract for support, flow, and process work. Do not select arbitrary clean-looking rows or write final semantic fields from task-local scripts; use the queue task input, closure, blocker, and run-plan artifacts.

When running dataset-curation-gate, pass --queue-dir .foundry/workspaces/<task-id>/curation-queue so AI authoring packages include the entity queue task, dependency closure, referenced flow rows, and batch support rows in addition to schema, YAML, profile context, schema blockers, and QA findings.

When curation gate has AI-required action items, build explicit authoring tasks before writing any patch. Prefer batch mode from the gate report so all entity patches can be combined and applied once:

npm run dataset:authoring-task:build -- \
  --curation-gate-report .foundry/workspaces/<task-id>/curation-gate/dataset-curation-gate-report.json \
  --out-dir .foundry/workspaces/<task-id>/authoring-tasks

Use single-package mode for targeted rework:

npm run dataset:authoring-task:build -- \
  --authoring-package .foundry/workspaces/<task-id>/curation-gate/ai-authoring-packages/<type>-<uuid>.authoring-package.json \
  --out-dir .foundry/workspaces/<task-id>/authoring-tasks/<type>-<uuid>

Codex/skill must read ai-authoring-task.md and the full authoring package for each task, then write only structured patch JSON. The final patch file must not retain template_status=requires_ai_completion or any __AI_FILL_* placeholder. In batch mode, write per-task ai-patches.json files first, then collect them into the manifest's batch patch target:

npm run dataset:authoring-patch:collect -- \
  --task-manifest .foundry/workspaces/<task-id>/authoring-tasks/authoring-task-manifest.json

Do not hand-edit row JSONL files and do not call database writes from the authoring task. If collect blocks, repair the per-task patch files before apply.

Apply AI output deterministically:

npm run dataset:patch:apply -- \
  --input .foundry/workspaces/<task-id>/rows/<type>.jsonl \
  --patch .foundry/workspaces/<task-id>/authoring-tasks/ai-patches.batch.json \
  --out .foundry/workspaces/<task-id>/rows/<type>.patched.jsonl \
  --out-dir .foundry/workspaces/<task-id>/patch-apply \
  --authoring-package-dir .foundry/workspaces/<task-id>/curation-gate/ai-authoring-packages \
  --require-authoring-package \
  --require-action-item-closure

If patch apply blocks on missing evidence, failed test, bad path, package mismatch, or unclosed action items, return to the authoring task. After a completed apply, use Foundry's process finalize wrapper for process rows:

npm run dataset:post-authoring-finalize -- \
  --type process \
  --rows-file .foundry/workspaces/<task-id>/rows/processes.patched.jsonl \
  --out-dir .foundry/workspaces/<task-id>/post-authoring-finalize \
  --profile bafu \
  --queue-dir .foundry/workspaces/<task-id>/curation-queue \
  --schema-file .foundry/workspaces/<task-id>/context/process/outputs/schema.json \
  --yaml-file .foundry/workspaces/<task-id>/context/process/outputs/methodology.yaml \
  --ruleset-file .foundry/workspaces/<task-id>/context/process/outputs/runtime-ruleset.json \
  --patch-collect-report .foundry/workspaces/<task-id>/authoring-tasks/authoring-patch-collect-report.json \
  --require-patch-collect-report \
  --patch-apply-report .foundry/workspaces/<task-id>/patch-apply/outputs/dataset-patch-apply-report.json \
  --target-user-id <uuid> \
  --verify-remote

The finalize wrapper reruns SDK validation, deterministic QA, cleanup, post-authoring curation gate, dry-run, optional remote verification, and mutation manifest on one exact rows-file scope. Commit remains a later explicit CLI step after dataset-post-authoring-finalize-report.json and its mutation manifest are ready_for_remote_write, followed by post-commit remote verification.

For process rows, treat tiangong-lca qa process as deterministic QA output only. It classifies structural issues, reference-flow/exchange evidence, and material-balance observations; Foundry owns profile policy, AI authoring packages, curated patches/build plans, import-only trace cleanup, profile waivers, and the final prewrite decision.

Block publish preparation when source evidence, schema validation, required fields, duplicate checks, runtime rulesets, or Foundry process curation actions are unresolved.