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Steven EnamakelandGitHub
8057f2283c
* refactor(composio): restructure toolkit metadata handling and onboarding steps - Removed the old `toolkitMeta.ts` file and replaced it with a new `toolkitMeta.tsx` file that includes updated metadata handling for Composio toolkits, enhancing the integration with React components. - Updated the `ComposioConnectModal` to directly render icons without additional markup, streamlining the component structure. - Modified the `Skills` page to utilize the new icon rendering method, improving consistency across the application. - Enhanced the onboarding process by introducing a new `ContextGatheringStep` component, which gathers user context from connected integrations, improving the onboarding experience. - Updated the `SkillsStep` to reflect changes in toolkit connection handling and display, ensuring a smoother user interaction during onboarding. * feat(apify): introduce Apify integration tools for actor execution and status retrieval - Added new tools for running Apify actors and fetching their run statuses, enhancing automation capabilities. - Updated the integration schema to include an `apify` toggle for user configuration, allowing for flexible integration management. - Enhanced the onboarding experience by modifying the SkillsStep to focus on Gmail integration, streamlining user interactions. - Improved documentation and comments for clarity on the new Apify functionalities and their usage. * feat(learning): add LinkedIn enrichment module and schemas - Introduced a new `linkedin_enrichment` module for enriching user profiles by scraping LinkedIn data from Gmail. - Implemented the `run_linkedin_enrichment` function to handle the enrichment pipeline, including Gmail search, scraping via Apify, and data persistence. - Added controller schemas for the learning domain, enabling integration with the existing controller framework. - Updated the `all.rs` file to register the new learning controllers and schemas, enhancing the overall functionality of the learning system. * feat(linkedin): implement PROFILE.md generation for LinkedIn enrichment - Added functionality to generate a PROFILE.md file from scraped LinkedIn data, summarizing user profiles for agent context. - Updated the `run_linkedin_enrichment` function to write PROFILE.md to the workspace, enhancing data persistence. - Introduced helper functions for rendering and summarizing LinkedIn profiles, improving the overall enrichment process. - Ensured minimal PROFILE.md creation even when scraping fails, maintaining essential user context. * feat(onboarding): enhance ContextGatheringStep for LinkedIn enrichment pipeline - Updated the ContextGatheringStep to integrate a new pipeline for LinkedIn enrichment, replacing the previous Gmail profile fetching stages. - Implemented a progress animation and logging for the enrichment process, improving user feedback during data retrieval. - Refactored stage definitions to align with the new pipeline structure, enhancing clarity and maintainability. - Introduced error handling and status updates for each stage of the enrichment process, ensuring robust user experience. * feat(instructions): refactor tool instruction generation for clarity and flexibility - Introduced helper functions `tool_instructions_preamble` and `append_tool_entry` to streamline the construction of tool instructions. - Updated `build_tool_instructions` to utilize the new helper functions, improving code readability and maintainability. - Added `build_tool_instructions_filtered` to allow for generating instructions from a filtered list of tools, enhancing flexibility in tool usage. - Adjusted the startup process to use the filtered instructions, ensuring only relevant tools are included in the system prompt. * refactor(toolkitMeta): simplify component structure and improve readability - Refactored the `BrandIcon` component to streamline its props definition, enhancing code clarity. - Consolidated SVG path definitions in various icons for better readability and maintainability. - Updated the `ContextGatheringStep` to simplify the RPC call syntax, improving code conciseness. - Enhanced logging in the LinkedIn enrichment process for clearer tracking of Gmail searches and scraping stages. * fix: address PR review — USER.md→PROFILE.md consistency, Composio tool filtering, and quality fixes - Replace USER.md with PROFILE.md across all prompt paths: channels_prompt.rs, subconscious/prompt.rs, workspace/ops.rs bootstrap, and channel tests - Remove Composio tool description from main agent system prompt (tool_descs) so skills_agent is the only agent that sees Skill-category tools - Add "learning" namespace description for CLI help discovery - Fix react-hooks/set-state-in-effect: wrap synchronous setState in queueMicrotask in ContextGatheringStep - Derive SkillsStep displayToolkits from backend allowlist with error/retry UI - Add KNOWN_COMPOSIO_TOOLKITS alternate slug variants (google_calendar, etc.) - Add namespaced debug logging to onboarding handlers and pipeline - Deduplicate MemoryClient creation in linkedin_enrichment persist functions * fix: use setTimeout instead of queueMicrotask for ESLint compatibility * refactor(onboarding): streamline debug logging in handleContextNext function - Consolidated debug logging in the handleContextNext function to improve clarity and reduce verbosity. - Removed unnecessary line breaks for a more concise code structure. * refactor(linkedin): enhance enrichment pipeline with structured stage results - Introduced a new `EnrichmentStage` struct to capture detailed results for each stage of the LinkedIn enrichment process. - Updated the `LinkedInEnrichmentResult` to include a vector of stages, allowing for structured reporting of success, failure, and skipped stages. - Improved error handling and logging throughout the enrichment pipeline, ensuring better traceability of issues during execution. - Adjusted the API response to include stage results, enhancing the frontend's ability to display detailed enrichment outcomes. * chore(workflows): update macOS E2E test configuration and comment out Linux E2E job - Modified the description and default values in the macOS E2E test input options for clarity. - Commented out the entire Linux E2E job configuration to prevent execution while maintaining the setup for future use.