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Structured data extraction from multiple websites into normalized tables. Returns JSON with consistent schema across sources. Use when you need STRUCTURED data, not raw content.
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SOC 職業分類に基づく
Machine learning model training with HuggingFace. Fine-tuning LLMs, dataset creation, GPU selection, training monitoring. Use for training custom models. Don't use for using pre-trained models via API.
Estimates project cost at market rates. Scans codebase, counts APIs, integrations, and complexity. Shows what the project would cost without AI. Use for pitches and ROI demonstrations.
Marketing and conversion optimization. Landing page CRO, copywriting, positioning, GTM strategy, A/B testing. Don't use for product development or technical decisions.
Product management frameworks. PRD writing, user stories, OKRs, roadmap, TAM/SAM/SOM analysis, competitive research. Don't use for technical implementation.
Scraping specific platforms with ready connectors. Google Maps businesses, Twitter trends, YouTube subtitles, Instagram/TikTok content. Use for platform-specific data collection.
Data analysis from external services. Connects to Google Sheets, Notion, HubSpot via Composio. Builds charts, runs queries, generates insights. Don't use for database queries.
| name | nimble |
| description | Structured data extraction from multiple websites into normalized tables. Returns JSON with consistent schema across sources. Use when you need STRUCTURED data, not raw content. |
| disable-model-invocation | true |
| model | sonnet |
| effort | medium |
| allowed-tools | Read, Write, Bash, WebFetch |
Always return structured table:
[
{"source": "site.com", "field1": "value", "field2": "value"},
{"source": "other.com", "field1": "value", "field2": "value"}
]