Skill UI

Automates Findymail tasks through Composio’s toolkit by orchestrating Rube MCP searches, connection checks, and multi-tool execution while insisting on up-to-date schemas and session reuse for workflows.

Guides ML engineers through dataset prep, PEFT strategy selection, LoRA/QLoRA adapter setup, training monitoring, and deployment checks for practical LLM fine-tuning, quantization, and adapter merge workflows.

Fine-tune LLMs with TRL’s RLHF toolkit, covering SFT, DPO, PPO/GRPO and reward model training so you can align HuggingFace Transformers with human preferences or feedback in post-training workflows.

Automates Finerworks operations via Rube MCP and Composio toolkit, guiding tool discovery, connection checks, and schema-compliant execution of multi-tool workflows with session reuse.

Automates Finmei tasks through the Composio Finmei toolkit via Rube MCP, guiding users to search tool schemas, verify connections, and execute workflows with schema-compliant arguments.

Automate Fireberry operations via Composio’s toolkit by discovering Rube MCP tools, managing connections, and executing schema-driven workflows while enforcing session, memory, and pagination practices.

This workflow provides the primary mechanism for converting complex, live websites into clean, LLM-ready markdown content. It supports single-page scraping using `scrapeUrl`, deep multi-page site crawling with `crawlUrl`, and scalable async crawling for large datasets. Ideal for building robust content ingestion pipelines and reliably extracting structured information from the web.

This advanced workflow executes sophisticated web data gathering tasks. It supports three primary functions: LLM-powered structured data extraction using custom JSON schemas or prompts; efficient batch scraping of multiple known URLs; and rapid site mapping to discover all reachable URLs on a domain. Use this when you require reliable, typed data structures rather than raw markdown content.

This comprehensive guide details how to set up a robust, local development environment for Firecrawl integrations. It utilizes self-hosted Docker containers and Redis to eliminate reliance on paid API credits during development. The setup supports both unit testing (with mocked SDK) and full integration testing against the local instance, ensuring a seamless and efficient workflow for scraping and web data extraction projects.

Provides comprehensive observability patterns for Firecrawl web scraping operations. It covers key metrics such as scrape success rates, credit consumption tracking, content quality assessment, and latency monitoring. Use this skill for implementing robust monitoring dashboards, setting up proactive failure alerts, and optimizing the reliability of web crawling workflows.

Automate Fireflies workflows through Composio’s Fireflies toolkit by orchestrating Rube MCP calls; it guides you to search available tools, verify Fireflies connections, and execute schema-aware multi-tool operations reliably.

This workflow connects to the Fireflies.ai API via GraphQL to fetch and process detailed meeting transcripts. It handles speaker-diarized data, extracting structured insights such as action items, key topics, sentiment analysis, and speaker breakdowns. It enables the creation of comprehensive meeting intelligence reports and robust downstream pipelines. Use this when you need to automate the retrieval, summarization, or deep analysis of recorded meeting content.