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gsd-extract_learnings

Extract decisions, lessons, patterns, and surprises from completed phase artifacts

Install

mkdir -p .claude/skills/gsd-extract-learnings && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16570" && unzip -o skill.zip -d .claude/skills/gsd-extract-learnings && rm skill.zip

Installs to .claude/skills/gsd-extract-learnings

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Extract decisions, lessons, patterns, and surprises from completed phase artifacts
82 charsno explicit “when” trigger

About this skill

<codex_skill_adapter>

A. Skill Invocation

  • This skill is invoked by mentioning $gsd-extract_learnings.
  • Treat all user text after $gsd-extract_learnings as {{GSD_ARGS}}.
  • If no arguments are present, treat {{GSD_ARGS}} as empty.

B. AskUserQuestion → request_user_input Mapping

GSD workflows use AskUserQuestion (Claude Code syntax). Translate to Codex request_user_input:

Parameter mapping:

  • headerheader
  • questionquestion
  • Options formatted as "Label" — description{label: "Label", description: "description"}
  • Generate id from header: lowercase, replace spaces with underscores

Batched calls:

  • AskUserQuestion([q1, q2]) → single request_user_input with multiple entries in questions[]

Multi-select workaround:

  • Codex has no multiSelect. Use sequential single-selects, or present a numbered freeform list asking the user to enter comma-separated numbers.

Execute mode fallback:

  • When request_user_input is rejected (Execute mode), present a plain-text numbered list and pick a reasonable default.

C. Task() → spawn_agent Mapping

GSD workflows use Task(...) (Claude Code syntax). Translate to Codex collaboration tools:

Direct mapping:

  • Task(subagent_type="X", prompt="Y")spawn_agent(agent_type="X", message="Y")
  • Task(model="...") → omit (Codex uses per-role config, not inline model selection)
  • fork_context: false by default — GSD agents load their own context via <files_to_read> blocks

Parallel fan-out:

  • Spawn multiple agents → collect agent IDs → wait(ids) for all to complete

Result parsing:

  • Look for structured markers in agent output: CHECKPOINT, PLAN COMPLETE, SUMMARY, etc.
  • close_agent(id) after collecting results from each agent </codex_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/gabrielwillen/VSCode/stateforward/emel/emel.cpp/.codex/get-shit-done/workflows/extract_learnings.md </execution_context>

Execute the extract-learnings workflow from @/Users/gabrielwillen/VSCode/stateforward/emel/emel.cpp/.codex/get-shit-done/workflows/extract_learnings.md end-to-end.

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