Gsd Extract Learnings

---
name: gsd-extract-learnings
description: "Extract decisions, lessons, patterns, and surprises from completed phase artifacts"
---

<cursor_skill_adapter>
## A. Skill Invocation
- This skill is invoked when the user mentions `gsd-extract-learnings` or describes a task matching this skill.
- Treat all user text after the skill mention as `{{GSD_ARGS}}`.
- If no arguments are present, treat `{{GSD_ARGS}}` as empty.

## B. User Prompting
When the workflow needs user input, prompt the user conversationally:
- Present options as a numbered list in your response text
- Ask the user to reply with their choice
- For multi-select, ask for comma-separated numbers

## C. Tool Usage
Use these Cursor tools when executing GSD workflows:
- `Shell` for running commands (terminal operations)
- `StrReplace` for editing existing files
- `Read`, `Write`, `Glob`, `Grep`, `Task`, `WebSearch`, `WebFetch`, `TodoWrite` as needed

## D. Subagent Spawning
When the workflow needs to spawn a subagent:
- Use `Task(subagent_type="generalPurpose", ...)`
- The `model` parameter maps to Cursor's model options (e.g., "fast")
</cursor_skill_adapter>

<objective>
Extract structured learnings from completed phase artifacts (PLAN.md, SUMMARY.md, VERIFICATION.md, UAT.md, STATE.md) into a LEARNINGS.md file that captures decisions, lessons learned, patterns discovered, and surprises encountered.
</objective>

<execution_context>
@/Users/zaneliu/Projects/open-source/friday-ai/.cursor/gsd-core/workflows/extract-learnings.md
</execution_context>

Execute the extract-learnings workflow from @/Users/zaneliu/Projects/open-source/friday-ai/.cursor/gsd-core/workflows/extract-learnings.md end-to-end.