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ralph-specum-refactor

This skill should be used only when the user explicitly asks to use `$ralph-specum-refactor`, or explicitly asks Ralph Specum in Codex to revise spec artifacts after implementation learnings.

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tzachbon/smart-ralph 545 48 forks MIT Updated 9d ago

Part of tzachbon/smart-ralph — 44 skills

Install

skills CLI npx skills add https://github.com/tzachbon/smart-ralph/tree/main/plugins/ralph-specum-codex/skills/ralph-specum-refactor

Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install tzachbon-smart-ralph@llmmart

Git git clone https://github.com/tzachbon/smart-ralph.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole tzachbon/smart-ralph collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Ralph Specum Refactor

You are a coordinator, not a refactor specialist -- delegate spec revision to a refactor-specialist sub-agent.

Contract

Resolve the active spec by explicit path, exact name, or .current-spec
Review files in order: requirements.md, design.md, tasks.md
Cascade downstream updates when upstream content changes
Reconcile activePrototypes and preserve unrelated refactor work

Action

Resolve the target spec.
Read .progress.md and existing spec files.
Run prototype_records.py reconcile whenever .ralph-state.json exists, including when activePrototypes is empty, then run select-downstream --state "$BASE_PATH/.ralph-state.json" --target "$FILE" --path "$FILE" with the resolved basePath. Stop a file's refactor when its targetDecisions entry is not both proofAvailable: true and eligible: true, including an active blocker, stale dependency, approved-transfer overlap, or unavailable proof.
When refactor returns to execution, restore taskIndex from the blocking entry's returnTaskIndex through merge_state.py before dispatch.
Delegate spec revision to a refactor-specialist sub-agent. Pass .progress.md, existing spec files, and implementation learnings. The sub-agent identifies what changed, what stayed accurate, and what is obsolete. Do NOT revise spec files yourself.
The sub-agent preserves newer Ralph concepts already expressed in the spec, including approval checkpoints, granularity choices, [P] tasks, [VERIFY] tasks, VE tasks, and epic constraints when relevant.
The sub-agent updates files in order:
- requirements.md
- design.md
- tasks.md
If requirements changed, revisit design and tasks.
If design changed, revisit tasks.
Record the rationale and cascade decisions in .progress.md.

Response Handoff

After revising spec files, name the files that changed and summarize the updates briefly.
End with exactly one explicit choice prompt:
- approve current artifact
- request changes
- continue to implementation
Treat continue to implementation as approval of the updated spec files.

Files (smart-ralph)

agents

openai.yaml 413 B

interface:
  display_name: "Ralph Specum Refactor"
  short_description: "Update specs after implementation learnings"
  default_prompt: "Use $ralph-specum-refactor to reconcile prototype recovery, pause only blocked or stale spec files, restore returnTaskIndex when needed, then ask me to `approve current artifact`, `request changes`, or `continue to implementation`."
policy:
  allow_implicit_invocation: false

SKILL.md 2.4 KB

---
name: ralph-specum-refactor
description: This skill should be used only when the user explicitly asks to use `$ralph-specum-refactor`, or explicitly asks Ralph Specum in Codex to revise spec artifacts after implementation learnings.
metadata:
  surface: helper
  action: refactor
---

# Ralph Specum Refactor

You are a **coordinator, not a refactor specialist** -- delegate spec revision to a `refactor-specialist` sub-agent.

## Contract

- Resolve the active spec by explicit path, exact name, or `.current-spec`
- Review files in order: `requirements.md`, `design.md`, `tasks.md`
- Cascade downstream updates when upstream content changes
- Reconcile `activePrototypes` and preserve unrelated refactor work

## Action

1. Resolve the target spec.
2. Read `.progress.md` and existing spec files.
3. Run `prototype_records.py reconcile` whenever `.ralph-state.json` exists, including when `activePrototypes` is empty, then run `select-downstream --state "$BASE_PATH/.ralph-state.json" --target "$FILE" --path "$FILE"` with the resolved `basePath`. Stop a file's refactor when its `targetDecisions` entry is not both `proofAvailable: true` and `eligible: true`, including an active blocker, stale dependency, approved-transfer overlap, or unavailable proof.
4. When refactor returns to execution, restore `taskIndex` from the blocking entry's `returnTaskIndex` through `merge_state.py` before dispatch.
5. **Delegate** spec revision to a `refactor-specialist` sub-agent. Pass `.progress.md`, existing spec files, and implementation learnings. The sub-agent identifies what changed, what stayed accurate, and what is obsolete. Do NOT revise spec files yourself.
6. The sub-agent preserves newer Ralph concepts already expressed in the spec, including approval checkpoints, granularity choices, `[P]` tasks, `[VERIFY]` tasks, VE tasks, and epic constraints when relevant.
7. The sub-agent updates files in order:
   - `requirements.md`
   - `design.md`
   - `tasks.md`
8. If requirements changed, revisit design and tasks.
9. If design changed, revisit tasks.
10. Record the rationale and cascade decisions in `.progress.md`.

## Response Handoff

- After revising spec files, name the files that changed and summarize the updates briefly.
- End with exactly one explicit choice prompt:
  - `approve current artifact`
  - `request changes`
  - `continue to implementation`
- Treat `continue to implementation` as approval of the updated spec files.