| name | gsd-extract-learnings |
| description | Extract decisions, lessons, patterns, and surprises from completed phase artifacts |
<augment_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 Augment tools when executing GSD workflows:
launch-process for running commands (terminal operations)
str-replace-editor for editing existing files
view for reading files and listing directories
save-file for creating new files
grep for searching code (or use MCP servers for advanced search)
web-search, web-fetch for web queries
add_tasks, view_tasklist, update_tasks for task management
D. Subagent Spawning
When the workflow needs to spawn a subagent:
- Use the built-in subagent spawning capability
- Define agent prompts in
.augment/agents/ directory
</augment_skill_adapter>
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.
<execution_context>
@/home/delorenj/code/bhappy/.augment/get-shit-done/workflows/extract_learnings.md
</execution_context>
Execute the extract-learnings workflow from @/home/delorenj/code/bhappy/.augment/get-shit-done/workflows/extract_learnings.md end-to-end.