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seed
Generate validated Seed specifications from interview results
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Stars
1,453
Hot score
99
Updated
March 19, 2026
Overall rating
C4.0
Composite score
4.0
Best-practice grade
B84.0
Install command
npx @skill-hub/cli install q00-ouroboros-seed
Repository
Q00/ouroboros
Skill path: skills/seed
Generate validated Seed specifications from interview results
Open repositoryBest for
Primary workflow: Ship Full Stack.
Technical facets: Full Stack.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: Q00.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install seed into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/Q00/ouroboros before adding seed to shared team environments
- Use seed for development workflows
Works across
Claude CodeCodex CLIGemini CLIOpenCode
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: seed
description: "Generate validated Seed specifications from interview results"
---
# /ouroboros:seed
Generate validated Seed specifications from interview results.
## Usage
```
ooo seed [session_id]
/ouroboros:seed [session_id]
```
**Trigger keywords:** "crystallize", "generate seed"
## Instructions
When the user invokes this skill:
### Load MCP Tools (Required before Path A/B decision)
The Ouroboros MCP tools are often registered as **deferred tools** that must be explicitly loaded before use. **You MUST perform this step before deciding between Path A and Path B.**
1. Use the `ToolSearch` tool to find and load the seed generation MCP tool:
```
ToolSearch query: "+ouroboros seed"
```
2. The tool will typically be named `mcp__plugin_ouroboros_ouroboros__ouroboros_generate_seed` (with a plugin prefix). After ToolSearch returns, the tool becomes callable.
3. If ToolSearch finds the tool → proceed to **Path A**. If not → proceed to **Path B**.
**IMPORTANT**: Do NOT skip this step. Do NOT assume MCP tools are unavailable just because they don't appear in your immediate tool list. They are almost always available as deferred tools that need to be loaded first.
### Path A: MCP Mode (Preferred)
If the `ouroboros_generate_seed` MCP tool is available (loaded via ToolSearch above):
1. Determine the interview session:
- If `session_id` provided: Use it directly
- If no session_id: Check conversation for a recent `ouroboros_interview` session ID
- If none found: Ask the user
2. Call the MCP tool:
```
Tool: ouroboros_generate_seed
Arguments:
session_id: <interview session ID>
```
3. The tool extracts requirements from the interview, calculates ambiguity score, and generates the Seed YAML.
4. Present the generated seed to the user.
**Advantages of MCP mode**: Automated ambiguity scoring (must be <= 0.2), structured extraction from persisted interview state, reproducible.
### Path B: Plugin Fallback (No MCP Server)
If the MCP tool is NOT available, fall back to agent-based generation:
1. Read `agents/seed-architect.md` and adopt that role
2. Extract structured requirements from the interview Q&A in conversation history
3. Generate a Seed YAML specification
4. Present the seed to the user
## Seed Components
The seed contains:
- **GOAL**: Clear primary objective
- **CONSTRAINTS**: Hard limitations (e.g., Python 3.14+, no external DB)
- **ACCEPTANCE_CRITERIA**: Measurable success criteria
- **ONTOLOGY_SCHEMA**: Data structure definition (name, fields, types)
- **EVALUATION_PRINCIPLES**: Quality principles with weights
- **EXIT_CONDITIONS**: When the workflow should terminate
- **METADATA**: Version, timestamp, ambiguity score, interview ID
## Example Output
```yaml
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
description: Task management domain model
fields:
- name: tasks
type: array
description: List of tasks
- name: title
type: string
description: Task title
metadata:
ambiguity_score: 0.15
```
## After Seed Generation
On successful seed generation, first announce:
```
Your seed has been crystallized!
```
Then check `~/.ouroboros/prefs.json` for `star_asked`. If `star_asked` is not set to `true`, use the **AskUserQuestion tool** with this single question:
```json
{
"questions": [{
"question": "If Ouroboros helped clarify your thinking, a GitHub star supports continued development. Ready to unlock Full Mode?",
"header": "Next step",
"options": [
{
"label": "\u2b50 Star & Setup",
"description": "Star on GitHub + run ooo setup to enable run, evaluate, status"
},
{
"label": "Just Setup",
"description": "Skip star, go straight to ooo setup for Full Mode"
}
],
"multiSelect": false
}]
}
```
- **Star & Setup**: Run `gh api -X PUT /user/starred/Q00/ouroboros`, save `{"star_asked": true}` to `~/.ouroboros/prefs.json`, then read and execute `skills/setup/SKILL.md`
- **Just Setup**: Save `{"star_asked": true}` to `~/.ouroboros/prefs.json`, then read and execute `skills/setup/SKILL.md`
- **Other** (user provides custom text): Save `{"star_asked": true}`, skip setup
Create `~/.ouroboros/` directory if it doesn't exist.
If `star_asked` is already `true`, skip the question and just announce:
```
Your seed has been crystallized!
📍 Next: `ooo run` to execute this seed (requires `ooo setup` first)
```