cascade-mcp

Sub-skill: Analyze a single Figma design frame via the figma-frame-data MCP tool. Use this variant when local files are NOT available (e.g., GitHub cloud Copilot where curl is blocked). Calls figma-frame-data with a batchToken to retrieve image, structure, and context via MCP, then analyzes and returns the analysis as text. No filesystem reads needed — pure MCP.

Sub-skill: Fetch raw content for one or more URLs via the extract-linked-resources MCP tool. Supports Jira issues, Confluence pages, Google Docs, and Google Sheets. Saves content to .temp/cascade/context/ and appends newly discovered links to to-load.md. 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.

Write or refresh the Shell Stories section of a Jira epic by loading all linked context (Figma, Confluence, Google Docs), analyzing every Figma frame in parallel, running scope analysis, then generating a prioritized list of incremental shell story outlines grouped by user workflow. Preserves completion markers for already-written stories. Uses ☐/⏬/❌/❓ scope markers with SCREENS and DEPENDENCIES per story.

Sub-skill: Produce a Scope Analysis from frame analyses and all gathered context. This is the critical step that takes per-frame analyses + epic context + Confluence/Google Docs + Figma comments and categorizes every feature by scope (☐/✅/⏬/❌/❓/💬). Groups features by user workflow, not by screen. Supports self-healing ❓→💬 flipping on re-runs. Output drives all downstream work (questions, shell stories, story writing).

Sub-skill: Analyze a single Figma design frame from local files. Reads image.png (vision), structure.xml (component tree), and context.md (comments/annotations) from .temp/cascade/figma/{fileKey}/frames/{name}/. Writes analysis.md. Designed to run as a subagent — no MCP tools needed, pure filesystem.

Interactive Q&A flow for design behavior questions. Asks each question one at a time, collects the user's answer, and posts each Q&A pair as a Figma comment pinned to the correct frame with proper vertical spacing.

Interactive Q&A flow for design behavior questions. Asks each question one at a time, collects the user's answer, and incrementally builds a growing Q&A comment on a Jira issue using atlassian-add-comment and atlassian-update-comment.

Post generated design behavior questions as comments on Figma frames. Takes questions organized by frame (from generate-behavior-questions skill) and posts each question as a comment pinned to the correct Figma frame using the figma-post-comment MCP tool.

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.