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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionralphExecute the skills CLI command in your project's root directory to begin installation:
Fetches ralph from supercent-io/skills-template and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ralph. Access via /ralph in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Stop prompting. Start specifying.
"The beginning is the end, and the end is the beginning." The serpent doesn't repeat β it evolves.
Interview β Seed β Execute β Evaluate
β β
βββββ Evolutionary Loop βββββ
Each cycle evolves, not repeats. Evaluation output feeds back as input for the next generation until the system converges.
β Wonder β Design
β± (diverge) β± (diverge)
β± explore β± create
β± β±
β ββββββββββββ β ββββββββββββ β
β² β²
β² define β² deliver
β² (converge) β² (converge)
β Ontology β 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.
| Command | Trigger Keywords | What It Does |
|---|---|---|
ooo interview |
ooo interview, interview me, clarify requirements, socratic questioning |
Socratic questioning β expose hidden assumptions |
ooo seed |
ooo seed, crystallize, generate seed, freeze requirements |
Crystallize interview into immutable spec (Ambiguity β€ 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 β Semantic β Multi-Model Consensus |
ooo evolve |
ooo evolve, evolutionary loop, iterate until converged |
Evolutionary loop until ontology converges (similarity β₯ 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 β The boulder never stops |
ooo setup |
ooo setup |
Register MCP server (one-time) |
ooo help |
ooo help |
Full reference |
Wonder β "How should I live?" β "What IS 'live'?" β Ontology β Socrates
Wonder Ontology
π‘ π¬
"What do I want?" β "What IS the thing I want?"
"Build a task CLI" β "What IS a task? What IS priority?"
"Fix the auth bug" β "Is this the root cause, or a symptom?"
ooo interview "I want to build a task management CLI"
The Socratic Interviewer asks questions until Ambiguity β€ 0.2.
Ambiguity formula:
Ambiguity = 1 β Ξ£(clarityα΅’ Γ weightα΅’)
Greenfield: Goal(40%) + Constraint(30%) + Success(30%)
Brownfield: Goal(35%) + Constraint(25%) + Success(25%) + Context(15%)
Threshold: Ambiguity β€ 0.2 β ready for Seed
Example scoring:
Goal: 0.9 Γ 0.4 = 0.36
Constraint: 0.8 Γ 0.3 = 0.24
Success: 0.7 Γ 0.3 = 0.21
ββββββ
Clarity = 0.81
Ambiguity = 1 β 0.81 = 0.19 β€ 0.2 β β Ready for Seed
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
ooo run seed.yaml
ooo run # uses seed from conversation context
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 β 0.15 β Excellent: on track0.15 β 0.30 β Acceptable: monitor closely0.30+ β Exceeded: course correction neededooo 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.
βββββββββββββββββββββββββββββββββββ
β 1. EXECUTE (parallel) β
β Independent tasks β
β concurrent scheduling β
βββββββββββββββββββββββββββββββββββ€
β 2. VERIFY β
β Check completion β
β Validate tests pass β
β Measure drift vs seed β
βββββββββββββββββββββββββββββββββββ€
β 3. LOOP (if failed) β
β Analyze failure β
β Fix identified issues β
β Repeat from step 1 β
βββββββββββββββββββββββββββββββββββ€
β 4. PERSIST (checkpoint) β
β .omc/state/ralph-state.json β
β Resume after interruption β
βββββββββββββββββββββββββββββββββββ
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": []
}
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}")
[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)
| Action | Command |
|---|---|
| Save checkpoint & exit | /ouroboros:cancel |
| Force clear all state | /ouroboros:cancel --force |
| Resume after interruption | ooo ralph continue or ralph continue |
ooo evolve "build a task management CLI"
ooo evolve "build a task management CLI" --no-execute # ontology-only, fast mode
Gen 1: Interview β Seed(Oβ) β Execute β Evaluate
Gen 2: Wonder β Reflect β Seed(Oβ) β Execute β Evaluate
Gen 3: Wonder β Reflect β Seed(Oβ) β Execute β Evaluate
...until ontology converges (similarity β₯ 0.95) or max 30 generations
Similarity = 0.5 Γ name_overlap + 0.3 Γ type_match + 0.2 Γ exact_match
Threshold: Similarity β₯ 0.95 β CONVERGED
Gen 1: {Task, Priority, Status}
Gen 2: {Task, Priority, Status, DueDate} β similarity 0.78 β CONTINUE
Gen 3: {Task, Priority, Status, DueDate} β similarity 1.00 β CONVERGED β
| Signal | Condition | Meaning |
|---|---|---|
| Stagnation | Similarity β₯ 0.95 for 3 consecutive gens | Ontology has stabilized |
| Oscillation | Gen N β Gen N-2 (period-2 cycle) | Stuck bouncing between two designs |
| Repetitive feedback | β₯ 70% question overlap across 3 gens | Wonder asking the same things |
| Hard cap | 30 generations reached | Safety valve |
Ralph Cycle 1: evolve_step(lineage, seed) β Gen 1 β action=CONTINUE
Ralph Cycle 2: evolve_step(lineage) β Gen 2 β action=CONTINUE
Ralph Cycle 3: evolve_step(lineage) β Gen 3 β action=CONVERGED β
βββ Ralph stops.
The ontology has stabilized.
ooo evolve --status <lineage_id> # check lineage status
ooo evolve --rewind <lineage_id> <gen_N> # roll back to generation N
Loaded on-demand β 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?" |
When blocked after repeated failures, choose a persona:
ooo unstuck # auto-select based on situation
ooo unstuck simplifier # cut scope to MVP β "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:
contrarian (challenge assumptions)simplifier (reduce scope)researcher (seek data)hacker (just make it work)architect (redesign)# 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 β keyword-detector.mjs detects triggersPostToolUse(Write|Edit) β drift-monitor.mjs tracks deviationSessionStart β session initializationClaude Code hooks.json (installed at ${CLAUDE_PLUGIN_ROOT}/hooks/hooks.json):
{
"hooks": {
"SessionStart": [Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
β Do
β Don't
π‘ Pro Tips
β Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
β 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.
supercent-io/skills-template
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
We added ralph from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: ralph is focused, and the summary matches what you get after install.
Useful defaults in ralph β fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
ralph is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend ralph for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for ralph matched our evaluation β installs cleanly and behaves as described in the markdown.
ralph is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ralph fits our agent workflows well β practical, well scoped, and easy to wire into existing repos.
ralph reduced setup friction for our internal harness; good balance of opinion and flexibility.
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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