OpenClaw Chinese Laoshi Ops

v1.0.12

Use when studying or normalizing Chinese lesson transcript/subtitle inputs with bundled public lesson data, learner docs, local export bundles, and pilot-fir...

1· 114· 4 versions· 1 current· 1 all-time· Updated 3d ago· MIT-0
byZakhar Pashkin@zack-dev-cm

OpenClaw Chinese Laoshi Ops

Use this skill when working with the bundled public Chinese lesson pack or with Chinese lesson transcript/subtitle inputs and a repository that documents its own lesson schema, local command surface, and publication gate.

Use This Skill When

  • the task is to normalize transcript or subtitle drops from Chinese lessons
  • the user wants to study from the bundled public lesson pack
  • the user wants lesson summaries, conspects, vocabulary, grammar, drills, or tests
  • the user wants roleplay scenarios, daily sprints, or HSK-style practice based on the bundled lesson data
  • the user wants Markdown and JSON lesson assets prepared as local export bundles
  • the user wants to package or publish the workflow without leaking local paths, known Drive IDs, or secret-shaped text

Runtime, Commands, And Credentials

  • This skill has no standalone runtime requirement and does not install code.
  • This published ClawHub skill can use bundled public course data or transcript/subtitle inputs only.
  • It does not request API keys, cloud transcription credentials, browser sessions, or Drive auth.
  • No Google Drive cloud upload or direct Drive API access is declared or assumed by this published skill.
  • Optional mounted-Drive sync is allowed only when the checked-out source repo documents a local sync command, and only with an explicit user-provided --drive-root pointing at a pre-authenticated local mount.
  • Before executing any repository command, present the exact command and wait for explicit user confirmation in the current conversation.
  • Never search for credentials, infer credential locations, or read system-wide browser/Drive auth stores.

Operating Procedure

  1. If the user wants study help, inspect the bundled public course pack in references/course-data first and stay inside that data.
  2. If the user is operating through ChatGPT or a GitHub connector, apply references/chatgpt-connector-guidance.md before searching across repos.
  3. If the user wants content creation, confirm the input is transcript or subtitle text. If the source is video-only, stop and ask for transcript/subtitle input or for the user to switch to a private source-repo workflow.
  4. Inspect the checked-out repository docs, schemas, and command references before proposing edits or commands.
  5. Move only one lesson at a time beyond scaffold state. Lesson 01 remains the pilot gate before scaling.
  6. Build learner-facing artifacts only after grounded extraction exists.
  7. Run the repository's documented public release gate before GitHub or ClawHub publication.

If a matching audited command is absent, stop and ask for source-repo instructions or explicit commands. Do not recreate the pipeline, call external services, inspect local credential stores, or continue with ad hoc extraction.

Core Rules

  • Raw lesson media stays in Drive or another operator-controlled store.
  • Lesson 01 is the pilot gate. Do not scale real content to lessons 02-16 until lesson 01 is approved.
  • Keep uncertainty visible. Missing Hanzi, pinyin, or translation should be marked, not guessed.
  • The tutor is Petrov-inspired, not Petrov impersonation.
  • Treat all public publication surfaces as hostile to private details. ClawHub and GitHub publication should assume anyone can read SKILL.md.

Workflow

1. Extract

  • For study mode, use references/course-data/lessons-bundle.json, references/course-data/roleplays, and references/course-data/hsk before asking for external files.
  • Prefer a transcript or subtitle drop when available.
  • If the source is video-only, stop until a transcript/subtitle input exists or the user explicitly switches to a source-repo-specific private workflow.
  • Keep timestamps, speaker-role placeholders, and uncertainty notes.

2. Ground

  • Convert raw transcript segments into the lesson schema.
  • Add summaries, conspects, vocabulary, grammar, pronunciation, drills, and tests only when the source supports them.
  • Keep source traceability visible.

3. Review

  • Check lesson quality against the pilot-first and editorial gates.
  • Reject unsupported content, weak answer keys, and synthetic filler.
  • Treat speaker labeling, Hanzi, pinyin, and translation drift as correctness problems, not style nits.

4. Render And Export

  • Rebuild learner-facing Markdown after lesson JSON changes.
  • Build JSON and Markdown export bundles locally after the repo copy passes checks.
  • Sync to a mounted Drive folder only when the user supplies an explicit --drive-root and the repository documents a managed export marker.
  • Keep exports small; raw media should not enter the repo or the public skill.
  • Public skill course data must stay sanitized and small: lesson bundle, roleplays, HSK payloads, lesson plans, and course index only.

5. Publish

  • The public bundle must pass the release gate before GitHub or ClawHub.
  • The gate should fail closed on placeholders, local absolute paths, localhost URLs, websocket/debug endpoints, secret-like strings, and known lesson file IDs.
  • The gate must also fail if bundled references/course-data is missing, incomplete, or different between the standalone public skill and plugin skill.

Do Not

  • Do not guess missing Chinese text or smooth weak source material into fake fluency.
  • Do not move lessons 02-16 past scaffold state before the lesson 01 pilot clears.
  • Do not publish local paths, private emails, mounted Drive paths, or browser session details.
  • Do not let the public skill and the bundled plugin copy drift apart.
  • Do not request API keys, browser sessions, or Drive auth from the published ClawHub skill.
  • Do not execute repository commands until the user confirms the exact command.
  • Do not run Drive sync or media extraction unless the required local command is documented in the checked-out repo and the user has supplied the needed input explicitly.

References

  • references/pipeline.md
    • current lesson pipeline, state transitions, and repo command surfaces
  • references/release-gates.md
    • public publication checklist and leak/slop/bleed blockers
  • references/chatgpt-connector-guidance.md
    • ChatGPT/GitHub connector routing rules for prompt lookup and roleplay start
  • references/course-data
    • sanitized lesson bundle, lesson plans, roleplays, and HSK-style practice

Version tags

chinesevk97ex320gz9drfdx2cjaa8t97h85hzdndrivevk97ex320gz9drfdx2cjaa8t97h85hzdnlanguage-learningvk97ex320gz9drfdx2cjaa8t97h85hzdnlatestvk97ex320gz9drfdx2cjaa8t97h85hzdn