cog-braindump-capture
The cog-braindump-capture skill accepts unstructured thoughts and automatically classifies them into personal, professional, or project-specific domains, then routes them to appropriate vault sections with metadata tags. Use this when you need to quickly capture raw ideas without manually organizing them, relying on automatic domain separation and cross-referencing to maintain vault structure.
git clone --depth 1 https://github.com/a5c-ai/babysitter /tmp/cog-braindump-capture && cp -r /tmp/cog-braindump-capture/library/methodologies/cog-second-brain/skills/braindump-capture ~/.claude/skills/cog-braindump-captureSKILL.md
# COG Braindump Capture Skill
Capture raw, unstructured thoughts and automatically classify them by domain (personal, professional, project-specific) for routing to appropriate vault sections.
## Capabilities
- Accept raw braindump text of any format
- Classify content into personal, professional, and project-specific domains
- Extract embedded URLs for separate processing
- Route classified content to appropriate vault directories
- Tag entries with metadata: date, domain, confidence, topics
- Maintain strict domain separation (02-personal vs 03-professional)
- Quality-gated capture with iterative refinement
## Tool Use Instructions
1. Use `file-read` to load user profile from 00-inbox for classification context
2. Classify content by domain using natural language analysis
3. Use `file-write` to create classified entries in appropriate vault directories
4. Use `file-search` to find related existing entries for cross-referencing
5. Use `file-write` to add cross-references to new and existing entries
6. Use `git-commit` to commit captured content
## Examples
```json
{
"vaultPath": "./cog-vault",
"captureType": "braindump",
"content": "Had a great idea about the auth system redesign. Also need to book vacation for July. The React 19 features look promising for our dashboard project.",
"targetQuality": 80
}
```Review TypeScript code changes for consistency, type safety, and monorepo patterns across babysitter packages
Generate and validate documentation for @a5c-ai/babysitter-sdk CLI commands and exported APIs
Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.
Architect code review with DRY, YAGNI, abstraction, and test coverage principle enforcement