By bitovi
Automate Figma-to-Jira design handoff: analyze frames against epics/Confluence/Google Docs for scope categorization and behavior questions, post Q&A as pinned comments, generate/refresh shell stories, and write full Gherkin user stories with AC/NFRs.
Post generated design behavior questions as a Jira comment on the source epic/issue. Takes questions organized by frame (from generate-behavior-questions skill) and formats them as a single structured Jira comment with Figma frame links using the atlassian-add-comment MCP tool.
Sub-skill: Summarize and categorize fetched content from .temp/cascade/context/. Extracts key information, identifies newly discovered links, and writes analysis summaries. Used as a building block by parent skills like generate-questions and write-jira-story.
Generate frame-specific clarifying questions about ambiguous UI behaviors from a Jira epic and its linked Figma designs, Confluence pages, and Google Docs. Uses iterative content loading, parallel Figma frame analysis, and cross-content synthesis to produce targeted behavior questions organized by Figma frame.
Write or refine a Jira story description with full context from Figma designs, Confluence docs, Google Docs, and parent epic. Gathers all linked resources, analyzes Figma frames, runs scope analysis, and writes a comprehensive story with User Story Statement, Scope Analysis, Acceptance Criteria (Gherkin), NFRs, and Developer Notes. Uses ☐/✅/❌/❓/💬 scope markers and flips ❓→💬 when answers are found.
Write a full Jira story (User Story Statement, Scope Analysis, Acceptance Criteria in Gherkin, NFRs, Developer Notes) from the next unwritten shell story in a Jira epic. Loads only the Figma screens listed in that shell story, runs scope analysis anchored to its scope bullets, generates the full story description, creates a Jira story under the epic, adds blocker links for dependencies, and marks the shell story complete in the epic.
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Cascade MCP helps you analyze Figma, Google Docs and Confluence, and write Jira tickets with AI! This open source project provides a collection of MCP (Model Context Protocol) capabilities for Bitovi's Cascading AI Enablement Initiative.
Checkout our introduction video to understand what it can do:
👉 Btw, Bitovi can help you integrate this into your own SDLC workflow: AI for Software Teams
Supported tools at the https://cascade.bitovi.com/mcp endpoint:
Combined Tools (Multi-provider workflows):
analyze-feature-scope - Generate scope analysis from Figma designs linked in a Jira epic (identifies features, establishes scope boundaries, surfaces questions before implementation)write-shell-stories - Generate shell stories from Figma designs linked in a Jira epic (analyzes screens, downloads assets, creates prioritized user stories using AI)write-epics-next-story - Write the next Jira story from shell stories in an epic (validates dependencies, generates full story content, creates Jira issue with acceptance criteria)write-story - Generate or refine a Jira story by gathering comprehensive context (parent hierarchy, comments, linked Figma/Confluence/Google Docs) and writing the best possible storyextract-linked-resources - Fetch a URL (Jira issue, Confluence page, Google Doc) and return its content as markdown with YAML frontmatter listing all discovered linked resourcesreview-work-item - Review a Jira work item and generate questions identifying gaps, ambiguities, and missing information (posts review as Jira comment)Atlassian/Jira Tools:
atlassian-get-sites - Get list of accessible Atlassian sites for the authenticated useratlassian-get-issue - Retrieve complete details of a Jira issue by ID or key, including description, attachments, comments, and full field dataatlassian-get-attachments - Fetch Jira attachments by attachment ID (returns base64-encoded content)atlassian-update-issue-description - Update a Jira issue description with markdown content (automatically converted to ADF)atlassian-add-comment - Post a comment to a Jira issue (markdown automatically converted to ADF)atlassian-update-comment - Update an existing Jira comment (markdown automatically converted to ADF)confluence-analyze-page - Fetch a Confluence page, convert to markdown, score relevance with LLM, and cache the resultsearch - Search Jira issues using JQL (Jira Query Language) with standardized document format outputfetch - Fetch Jira issue details by issue key/ID in standardized document format (ChatGPT-compatible)npx claudepluginhub bitovi/cascade-mcp --plugin cascade-mcpSkills for creating new agent skills for Claude Code and VS Code Copilot
Product management: ticket-info, user-stories-create, prd, spec
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