dvcrn Content Repurpose Studio
Purpose
Transform one source asset into a coordinated pack for multiple channels such as WeChat, Xiaohongshu, TikTok, email, and slides.
Trigger phrases
- 一稿多发
- repurpose this content
- 内容分发改写
- multi-channel content pack
- 同一内容改成多平台
Ask for these inputs
- source article/video/transcript
- target channels
- brand voice
- CTA
- length constraints
Workflow
- Identify the core message, proof, and CTA from the source.
- Map channel-specific constraints using the bundled channel specs.
- Produce platform-native variants instead of shallow copies.
- Keep a shared facts block so claims remain consistent across channels.
- Return a launch checklist with filenames and publishing order.
Output contract
- channel pack
- shared facts block
- launch checklist
- reuse matrix
Files in this skill
- Script:
{baseDir}/scripts/channel_packager.py - Resource:
{baseDir}/resources/channel_specs.yaml
Operating rules
- Be concrete and action-oriented.
- Prefer preview / draft / simulation mode before destructive changes.
- If information is missing, ask only for the minimum needed to proceed.
- Never fabricate metrics, legal certainty, receipts, credentials, or evidence.
- Keep assumptions explicit.
Suggested prompts
- 一稿多发
- repurpose this content
- 内容分发改写
Use of script and resources
Use the bundled script when it helps the user produce a structured file, manifest, CSV, or first-pass draft. Use the resource file as the default schema, checklist, or preset when the user does not provide one.
Boundaries
- This skill supports planning, structuring, and first-pass artifacts.
- It should not claim that files were modified, messages were sent, or legal/financial decisions were finalized unless the user actually performed those actions.
Compatibility notes
- Directory-based AgentSkills/OpenClaw skill.
- Runtime dependency declared through
metadata.openclaw.requires. - Helper script is local and auditable:
scripts/channel_packager.py. - Bundled resource is local and referenced by the instructions:
resources/channel_specs.yaml.