ralph

supercent-io/skills-template · updated Apr 8, 2026

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$npx skills add https://github.com/supercent-io/skills-template --skill ralph
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skill.md

ralph (Ouroboros) — Specification-First AI Development

Stop prompting. Start specifying.

"The beginning is the end, and the end is the beginning." The serpent doesn't repeat — it evolves.


When to use this skill

  • Before writing any code — expose hidden assumptions with Socratic interviewing
  • Long-running tasks that need autonomous iteration until verified
  • Vague requirements — crystallize them into an immutable spec (Ambiguity ≤ 0.2)
  • Tasks requiring guaranteed completion — loop until verification passes
  • When stuck — 5 lateral thinking personas break through stagnation
  • Drift detection — measure how far execution has deviated from original spec

Core Architecture: The Loop

    Interview → Seed → Execute → Evaluate
        ↑                           ↓
        └──── Evolutionary Loop ────┘

Each cycle evolves, not repeats. Evaluation output feeds back as input for the next generation until the system converges.

Double Diamond

    ◇ Wonder          ◇ Design
   ╱  (diverge)      ╱  (diverge)
  ╱    explore      ╱    create
 ╱                 ╱
◆ ──────────── ◆ ──────────── ◆
 ╲                 ╲
  ╲    define       ╲    deliver
   ╲  (converge)     ╲  (converge)
    ◇ Ontology        ◇ Evaluation

The first diamond is Socratic: diverge into questions, converge into ontological clarity. The second diamond is pragmatic: diverge into design options, converge into verified delivery.


1. Commands (Full Reference)

Command Trigger Keywords What It Does
ooo interview ooo interview, interview me, clarify requirements, socratic questioning Socratic questioning → expose hidden assumptions
ooo seed ooo seed, crystallize, generate seed, freeze requirements Crystallize interview into immutable spec (Ambiguity ≤ 0.2)
ooo run ooo run, execute seed, ouroboros run Execute via Double Diamond decomposition
ooo evaluate ooo evaluate, 3-stage check, evaluate this, verify execution 3-stage gate: Mechanical → Semantic → Multi-Model Consensus
ooo evolve ooo evolve, evolutionary loop, iterate until converged Evolutionary loop until ontology converges (similarity ≥ 0.95)
ooo unstuck ooo unstuck, I'm stuck, think sideways, lateral thinking 5 lateral thinking personas when stuck
ooo status ooo status, am I drifting?, drift check, session status Drift detection + session tracking
ooo ralph ooo ralph, ralph, don't stop, must complete, keep going Persistent loop until verified — The boulder never stops
ooo setup ooo setup Register MCP server (one-time)
ooo help ooo help Full reference

2. Interview → Specification Flow

Philosophy: From Wonder to Ontology

Wonder → "How should I live?" → "What IS 'live'?" → Ontology — Socrates

   Wonder                          Ontology
     💡                               🔬
"What do I want?"    →    "What IS the thing I want?"
"Build a task CLI"   →    "What IS a task? What IS priority?"
"Fix the auth bug"   →    "Is this the root cause, or a symptom?"

Step 1: Interview (expose hidden assumptions)

ooo interview "I want to build a task management CLI"

The Socratic Interviewer asks questions until Ambiguity ≤ 0.2.

Ambiguity formula:

Ambiguity = 1 − Σ(clarityᵢ × weightᵢ)

Greenfield: Goal(40%) + Constraint(30%) + Success(30%)
Brownfield: Goal(35%) + Constraint(25%) + Success(25%) + Context(15%)

Threshold: Ambiguity ≤ 0.2 → ready for Seed

Example scoring:

Goal:       0.9 × 0.4 = 0.36
Constraint: 0.8 × 0.3 = 0.24
Success:    0.7 × 0.3 = 0.21
                        ──────
Clarity             = 0.81
Ambiguity = 1 − 0.81 = 0.19 ≤ 0.2 → ✓ Ready for Seed

Step 2: Seed (crystallize into immutable spec)

ooo seed

Generates YAML specification:

goal: Build a CLI task management tool
constraints:
  - Python 3.14+
  - No external database
  - SQLite for persistence
acceptance_criteria:
  - Tasks can be created
  - Tasks can be listed
  - Tasks can be marked complete
ontology_schema:
  name: TaskManager
  fields:
    - name: tasks
      type: array
    - name: title
      type: string

Step 3: Run (execute via Double Diamond)

ooo run seed.yaml
ooo run  # uses seed from conversation context

Step 4: Evaluate (3-stage verification)

ooo evaluate <session_id>
Stage Cost What It Checks
Mechanical $0 Lint, build, tests, coverage
Semantic Standard AC compliance, goal alignment, drift score
Consensus Frontier (optional) Multi-model vote, majority ratio

Drift thresholds:

  • 0.0 – 0.15 — Excellent: on track
  • 0.15 – 0.30 — Acceptable: monitor closely
  • 0.30+ — Exceeded: course correction needed

3. Ralph — Persistent Loop Until Verified

ooo ralph "fix all failing tests"
/ouroboros:ralph "fix all failing tests"

"The boulder never stops." Each failure is data for the next attempt. Only complete success or max iterations stops it.

How Ralph Works

┌─────────────────────────────────┐
│  1. EXECUTE (parallel)          │
│     Independent tasks           │
│     concurrent scheduling       │
├─────────────────────────────────┤
│  2. VERIFY                      │
│     Check completion            │
│     Validate tests pass         │
│     Measure drift vs seed       │
├─────────────────────────────────┤
│  3. LOOP (if failed)            │
│     Analyze failure             │
│     Fix identified issues       │
│     Repeat from step 1          │
├─────────────────────────────────┤
│  4. PERSIST (checkpoint)        │
│     .omc/state/ralph-state.json │
│     Resume after interruption   │
└─────────────────────────────────┘

State File

Create .omc/state/ralph-state.json on start:

{
  "mode": "ralph",
  "session_id": "<uuid>",
  "request": "<user request>",
  "status": "running",
  "iteration": 0,
  "max_iterations": 10,
  "last_checkpoint": null,
  "verification_history": []
}

Loop Logic

while iteration < max_iterations:
    result = execute_parallel(request, context)
    verification = verify_result(result, acceptance_criteria)
    state.verification_history.append({
        "iteration": iteration,
        "passed": verification.passed,
        "score": verification.score,
        "timestamp": <now>
    })
    if verification.passed:
        save_checkpoint("complete")
        break
    iteration += 1
    save_checkpoint("iteration_{iteration}")

Progress Report Format

[Ralph Iteration 1/10]
Executing in parallel...

Verification: FAILED
Score: 0.65
Issues:
- 3 tests still failing
- Type errors in src/api.py

The boulder never stops. Continuing...

[Ralph Iteration 3/10]
Executing in parallel...

Verification: PASSED
Score: 1.0

Ralph COMPLETE
==============
Request: Fix all failing tests
Duration: 8m 32s
Iterations: 3

Verification History:
- Iteration 1: FAILED (0.65)
- Iteration 2: FAILED (0.85)
- Iteration 3: PASSED (1.0)

Cancellation

Action Command
Save checkpoint & exit /ouroboros:cancel
Force clear all state /ouroboros:cancel --force
Resume after interruption ooo ralph continue or ralph continue

4. Evolutionary Loop (Evolve)

ooo evolve "build a task management CLI"
ooo evolve "build a task management CLI" --no-execute  # ontology-only, fast mode

Flow

Gen 1: Interview → Seed(O₁) → Execute → Evaluate
Gen 2: Wonder → Reflect → Seed(O₂) → Execute → Evaluate
Gen 3: Wonder → Reflect → Seed(O₃) → Execute → Evaluate
...until ontology converges (similarity ≥ 0.95) or max 30 generations

Convergence Formula

Similarity = 0.5 × name_overlap + 0.3 × type_match + 0.2 × exact_match
Threshold: Similarity ≥ 0.95 → CONVERGED

Gen 1: {Task, Priority, Status}
Gen 2: {Task, Priority, Status, DueDate}  → similarity 0.78 → CONTINUE
Gen 3: {Task, Priority, Status, DueDate}  → similarity 1.00 → CONVERGED ✓

Stagnation Detection

Signal Condition Meaning
Stagnation Similarity ≥ 0.95 for 3 consecutive gens Ontology has stabilized
Oscillation Gen N ≈ Gen N-2 (period-2 cycle) Stuck bouncing between two designs
Repetitive feedback ≥ 70% question overlap across 3 gens Wonder asking the same things
Hard cap 30 generations reached Safety valve

Ralph in Evolve Mode

Ralph Cycle 1: evolve_step(lineage, seed) → Gen 1 → action=CONTINUE
Ralph Cycle 2: evolve_step(lineage)       → Gen 2 → action=CONTINUE
Ralph Cycle 3: evolve_step(lineage)       → Gen 3 → action=CONVERGED ✓
                                                └── Ralph stops.
                                                    The ontology has stabilized.

Rewind

ooo evolve --status <lineage_id>          # check lineage status
ooo evolve --rewind <lineage_id> <gen_N>  # roll back to generation N

5. The Nine Minds (Agents)

Loaded on-demand — never preloaded:

Agent Role Core Question
Socratic Interviewer Questions-only. Never builds. "What are you assuming?"
Ontologist Finds essence, not symptoms "What IS this, really?"
Seed Architect Crystallizes specs from dialogue "Is this complete and unambiguous?"
Evaluator 3-stage verification "Did we build the right thing?"
Contrarian Challenges every assumption "What if the opposite were true?"
Hacker Finds unconventional paths "What constraints are actually real?"
Simplifier Removes complexity "What's the simplest thing that could work?"
Researcher Stops coding, starts investigating "What evidence do we actually have?"
Architect Identifies structural causes "If we started over, would we build it this way?"

6. Unstuck — Lateral Thinking

When blocked after repeated failures, choose a persona:

ooo unstuck                 # auto-select based on situation
ooo unstuck simplifier      # cut scope to MVP — "Start with exactly 2 tables"
ooo unstuck hacker          # make it work first, elegance later
ooo unstuck contrarian      # challenge all assumptions
ooo unstuck researcher      # stop coding, find missing information
ooo unstuck architect       # restructure the approach entirely

When to use each:

  • Repeated similar failures → contrarian (challenge assumptions)
  • Too many options → simplifier (reduce scope)
  • Missing information → researcher (seek data)
  • Analysis paralysis → hacker (just make it work)
  • Structural issues → architect (redesign)

7. Platform Installation & Usage

Claude Code (Native Plugin — Full Mode)

# Install
claude plugin marketplace add Q00/ouroboros
claude plugin install ouroboros@ouroboros

# One-time setup
ooo setup

# Use
ooo interview "I want to build a task CLI"
ooo seed
ooo run
ooo evaluate <session_id>
ooo ralph "fix all failing tests"

All ooo commands work natively. Hooks auto-activate:

  • UserPromptSubmit → keyword-detector.mjs detects triggers
  • PostToolUse(Write|Edit) → drift-monitor.mjs tracks deviation
  • SessionStart → session initialization

Claude Code hooks.json (installed at ${CLAUDE_PLUGIN_ROOT}/hooks/hooks.json):

{
  "hooks": {
    "SessionStart": [
how to use ralph

How to use ralph on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add ralph
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/supercent-io/skills-template --skill ralph

The skills CLI fetches ralph from GitHub repository supercent-io/skills-template and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/ralph

Reload or restart Cursor to activate ralph. Access the skill through slash commands (e.g., /ralph) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.873 reviews
  • Min Zhang· Dec 28, 2024

    We added ralph from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Henry Torres· Dec 24, 2024

    Solid pick for teams standardizing on skills: ralph is focused, and the summary matches what you get after install.

  • Henry Flores· Dec 24, 2024

    Useful defaults in ralph — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Maya Wang· Dec 24, 2024

    ralph is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Henry Sanchez· Dec 20, 2024

    I recommend ralph for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ganesh Mohane· Dec 12, 2024

    Registry listing for ralph matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Shikha Mishra· Dec 8, 2024

    ralph is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Amelia Gupta· Dec 4, 2024

    ralph fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sakura Abebe· Dec 4, 2024

    ralph reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Nikhil Liu· Dec 4, 2024

    Keeps context tight: ralph is the kind of skill you can hand to a new teammate without a long onboarding doc.

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