GS

gsd-extract_learnings

Automates the extraction of key decisions and lessons learned from past project phases to improve future workflows.

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
Intermediate

Key capabilities

  • Extract decisions from completed phase artifacts
  • Extract lessons from completed phase artifacts
  • Extract patterns from completed phase artifacts
  • Extract surprises from completed phase artifacts
  • Map `AskUserQuestion` to `request_user_input`
  • Map `Task()` to `spawn_agent`

How it works

This skill extracts structured learnings from completed phase artifacts by translating GSD workflow syntax (`AskUserQuestion`, `Task()`) to Codex tool calls (`request_user_input`, `spawn_agent`), then processes the artifacts to identify decisions, lessons, patterns, and surprises into a `LEARNINGS.md` file.

Inputs & outputs

You give it
Completed phase artifacts (PLAN.md, SUMMARY.md, VERIFICATION.md, UAT.md, STATE.md)
You get back
A LEARNINGS.md file capturing decisions, lessons learned, patterns discovered, and surprises encountered

When to use gsd-extract_learnings

  • Summarizing lessons from a completed feature sprint
  • Generating a report on technical decisions made
  • Extracting patterns from project documentation

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.

When not to use it

  • When the input is not a completed phase artifact
  • When `AskUserQuestion` is expected to have `multiSelect` functionality
  • When `Task()` is expected to use inline model selection

Limitations

  • Codex has no `multiSelect` for `request_user_input`
  • Codex uses per-role config, not inline model selection for `spawn_agent`
  • Relies on structured markers in agent output for result parsing

How it compares

This skill systematically extracts structured learnings from project artifacts by mapping GSD workflow steps to agent tool calls, providing a consistent and automated knowledge capture process compared to manual review.

Compared to similar skills

gsd-extract_learnings side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
gsd-extract_learnings (this skill)04moNo flagsIntermediate
product-manager-toolkit328moReviewBeginner
trellis-brainstorm01moReviewIntermediate
grill-destination02moNo flagsIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

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