القائمة
Autonomous task completion loop with PRD tracking and persistent progress memory
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/CleanExpo/Unite-Group --skill ralph-wiggumيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| name | ralph-wiggum |
| category | workflow |
| version | 2.0.0 |
| description | Autonomous task completion loop with PRD tracking and persistent progress memory |
| author | Unite Group (ported from NodeJS-Starter-V1, adapted for Next.js-only stack) |
| priority | 2 |
| auto_load | false |
| triggers | ["ralph","autonomous","loop","prd","overnight"] |
| requires | ["verification-first","context-partitioning"] |
| context | fork |
A simple, powerful pattern for autonomous multi-iteration development:
plans/prd.json — JSON file with user stories, each with a passes booleanplans/progress.txt — Append-only LLM memory between iterationsNamed after Ralph Wiggum from The Simpsons — simple but effective.
"Me fail English? That's unpossible!" — Ralph Wiggum
| Problem | Solution |
|---|---|
| LLMs lose context between sessions | progress.txt persists learnings |
| Tendency to declare victory | Verification gate blocks false completion |
| No structured task state | prd.json machine-readable task list |
| Complex orchestration overhead | Simple loop, no LangGraph needed |
plans/prd.json){
"project": "Unite-Group Nexus",
"version": "1.0.0",
"user_stories": [
{
"id": "US-001",
"epic": "Social",
"title": "Persona avatar upload",
"description":
| Field | Purpose |
|---|---|
passes | Only true after verification gate passes |
priority | Execution order: critical > high > medium > low |
depends_on | Story IDs that must pass first |
acceptance_criteria | Specific, verifiable requirements |
attempt_count | Tracks failed attempts — escalate at 3 |
plans/progress.txt)Append-only. The LLM reads this at the start of each iteration to learn from previous failures.
# Ralph Progress Log
# Project: Unite-Group Nexus
# Created: 20/03/2026
---
## Session 1: 20/03/2026 10:30 AEST
**Task**: US-001 — Persona avatar upload
**Status**: IN_PROGRESS
### Work Done
- Added file input to SocialPersonasManager.tsx
- Wired to Supabase Storage upload
### Issues Encountered
- Type error: `File | null` not assignable — needs explicit null check
### Learnings
- Supabase Storage path must include founder_id: `avatars/${founderId}/${personaId}`
- Use `useState<File | null>(null)` not `useState(null)` for file state
### Next Steps
1. Fix null check on file input handler
2. Run Tier A verification
---
## Session 2: 20/03/2026 11:00 AEST
**Task**: US-001 — Persona avatar upload
**Status**: COMPLETED
### Work Done
- Fixed null check
- All Tier A verification passed
### Learnings
- Supabase Storage public URL: `supabase.storage.from('avatars').getPublicUrl(path).data.publicUrl`
### Next Steps
- Move to US-002
ALL must pass before marking passes: true:
pnpm run type-check # TypeScript — always required
pnpm run lint # ESLint — always required
pnpm vitest run # Unit tests — if verification.unit_tests: true
pnpm build # Build — if verification.build: true
E2E (Playwright) is run manually or via CI only — not in the loop by default.
Iteration N:
1. All stories passed? → EXIT (done)
2. Find highest-priority unpassed story (respect depends_on)
3. Read progress.txt — load learnings from previous attempts
4. Produce context manifest (context-partitioning skill)
5. Implement per acceptance criteria
6. Run verification pipeline
7. PASS → set passes: true → git commit → append to progress.txt
FAIL → increment attempt_count → record issue → append to progress.txt
8. Next iteration
| Condition | Action |
|---|---|
attempt_count >= 3 | Stop — record blocker — skip to next story — flag for human review |
| Dependency not yet passed | Skip story — work on next available |
| Verification tool fails (not code) | Record as infra blocker — do not increment attempt_count |
# Via slash command
/ralph run 50 # Max 50 iterations
# Manual: provide this prompt to Claude Code
"Read plans/prd.json and plans/progress.txt.
Find the highest-priority unpassed story.
Implement it, verify it, update both files.
Repeat until all stories pass or you reach {N} iterations."
Previous learnings prevent repeating the same mistake across sessions.
Creates safe checkpoints — a failed story doesn't undo previous work.
# Commit format
git commit -m "feat(US-001): persona avatar upload"
All implementations follow en-AU defaults automatically via the pre-response hook:
For stories requiring 3+ agents, Ralph delegates to the Harness instead of implementing directly:
Story requires frontend + database + API changes
→ Ralph creates manifest per context-partitioning skill
→ Dispatches to Agent Harness (AGENT_HARNESS.md)
→ Harness returns structured result
→ Ralph runs verification gate
→ Ralph updates prd.json and progress.txt
.claude/skills/custom/verification-first/SKILL.md.claude/skills/custom/context-partitioning/SKILL.md