AgentSkillsCN

video-library-pipeline-extract-review

交互式运行的第二阶段:执行提取操作,导出程序YAML,并暂停以待人工审核。

SKILL.md
--- frontmatter
name: video-library-pipeline-extract-review
description: Stage 2 of interactive operation. Run extraction, export program YAML, and stop for human review.
metadata: {"openclaw":{"emoji":"🧠","requires":{"plugins":["video-library-pipeline"]}}}

Stage 2: Extract + YAML + Human Review

Rule

  • Use plugin tools only. Do not call scripts directly.
  • Keep execution in the main agent turn. Do not use subagents.
  • This stage must save program info to YAML after extraction.
  • This stage ends with human review. Do not continue to move/apply automatically.

Tool sequence

  1. Call video_pipeline_validate with {"checkWindowsInterop": true}.
  2. Call video_pipeline_reextract with:
    • queuePath from Stage 1 summary (queue)
    • optional batchSize / maxBatches
    • keep default preserveHumanReviewed=true (reviewed rows are protected from overwrite)
  3. Call video_pipeline_export_program_yaml with:
    • sourceJsonlPath omitted (latest extraction output auto-detected), or explicit path
    • optional outputPath if user wants a fixed location
    • default is ${windowsOpsRoot}/llm/program_aliases_review_YYYYMMDD_HHMMSS.yaml
  4. If user manually edits extracted JSONL, call video_pipeline_apply_reviewed_metadata:
    • sourceJsonlPath: edited extraction JSONL path
    • default markHumanReviewed=true
    • this step writes reviewed metadata into DB (path_metadata)

Human review checklist

  • YAML file was generated successfully.
  • Program titles/aliases in YAML are acceptable.
  • Rows with needs_review are either fixed or intentionally kept for later.
  • Manual fixes are applied to DB via video_pipeline_apply_reviewed_metadata.
  • User confirms YAML should be used as the next review baseline.

Handoff

  • Return:
    • extraction result summary
    • YAML output path
    • count of programs exported
  • Ask user whether to proceed to Stage 3 (Move + Human review).