seed-library

Seed Module Library

Manage and apply the 10 Dojo Seed Patches plus 3 field seeds as reusable thinking modules. The core seeds derive from Dataiku research; the field seeds emerged from direct practice. Make enterprise-grade agent patterns accessible, consistent, and evolvable.

When to Use This Skill

Trigger this skill when:

• User asks "What seed applies here?" or "Which pattern should I use?"
• Keywords match seed triggers (governance, multi-agent, cost, context, routing, etc.)
• Architecting new features or systems
• Debugging complex issues
• Optimizing costs or performance
• Building trust through transparency

The 10 Core Seeds

1. Three-Tiered Governance - Strategic/Tactical/Operational framework
2. Harness Trace - Nested spans + events for traceability
3. Context Iceberg - 6x token multiplier, hierarchical loading
4. Agent Connect - Routing-first, not swarm-first
5. Go-Live Bundles - Reusable artifacts (DojoPacket)
6. Cost Guard - Budget for full iceberg (5-10x multiplier)
7. Safety Switch - Fallback to conservative mode
8. Implicit Perspective Extraction - Extract from constraints/metaphors
9. Mode-Based Complexity Gating - 3-question test
10. Shared Infrastructure - Build once, reuse everywhere

Meta-Seed: Governance Multiplies Velocity

Field Seeds (From Practice)

11. Voice Before Structure - Read design language before writing structural artifacts
12. Pointer Directories - Empty directories are references, not gaps
13. Granular Visibility - Progress tracking serves the user, not the agent

Core Workflow

1. Suggest Relevant Seeds

Based on keywords or task context:

python3.11 /home/ubuntu/skills/seed-library/scripts/suggest_seeds.py <keywords...>

Example:

python3.11 suggest_seeds.py multi-agent architecture coordination

Output:

• Top 3 relevant seeds ranked by relevance score
• Brief description of each seed
• File path to full seed content

Saved to: /home/ubuntu/seed-suggestions.md

2. Apply a Seed

Load full seed content and application guide:

python3.11 /home/ubuntu/skills/seed-library/scripts/apply_seed.py <seed_id> [session_id]

Example:

python3.11 apply_seed.py 04_agent_connect session_123

Output:

• Full seed content (What It Is, Why It Matters, The Pattern, etc.)
• Application checklist
• Next steps
• Usage tracked automatically

Saved to: /home/ubuntu/seed-<seed_id>-applied.md

Script Reference

suggest_seeds.py

Purpose: Suggest relevant seeds based on keywords

Usage:

python3.11 suggest_seeds.py <keywords...>

How it works:

1. Matches keywords to seed triggers
2. Calculates relevance score
3. Returns top 3 suggestions

Trigger Keywords by Seed:

• 01 (Governance): governance, capabilities, complexity, multi-agent, coordination, policy, standards
• 02 (Trace): debugging, trace, transparency, performance, evaluation, logging, monitoring
• 03 (Context): token, cost, context, window, limit, budget, pruning, memory, overhead
• 04 (Agent Connect): multi-agent, routing, coordination, specialized, handoff, permission, swarm
• 05 (Bundles): export, sharing, reuse, artifact, package, bundle, repeatability, trust
• 06 (Cost Guard): cost, budget, estimation, planning, infrastructure, investment, pricing
• 07 (Safety Switch): fallback, conservative, alert, drift, failure, recovery, validation, error
• 08 (Perspective): perspective, constraint, metaphor, scope, extraction, implicit, natural
• 09 (Complexity): mode, complexity, routing, simple, query, reasoning, adaptive
• 10 (Infrastructure): infrastructure, reuse, duplication, foundation, shared, common, service
• 11 (Voice): voice, philosophy, design-language, manifest, description, ecosystem, grounding, plugin
• 12 (Pointers): empty, missing, pointer, provenance, registry, audit, gap, coverage, directory
• 13 (Visibility): progress, tracking, visibility, todo, granular, steering, trust, delegation

apply_seed.py

Purpose: Load and explain how to apply a seed

Usage:

python3.11 apply_seed.py <seed_id> [session_id]

What it does:

1. Loads full seed content from /seeds/<seed_id>.md
2. Generates application guide with checklist
3. Tracks usage in /home/ubuntu/.seed-usage.json
4. Saves guide to file

Seed IDs:

• 01_three_tiered_governance
• 02_harness_trace
• 03_context_iceberg
• 04_agent_connect
• 05_go_live_bundles
• 06_cost_guard
• 07_safety_switch
• 08_implicit_perspective_extraction
• 09_mode_based_complexity_gating
• 10_shared_infrastructure
• meta_governance_multiplies_velocity
• 11_voice_before_structure
• 12_pointer_directories
• 13_granular_visibility

Seed Files

All seeds are stored in /home/ubuntu/skills/seed-library/seeds/ as markdown files.

Seed File Structure

---
seed_id: 01
name: Three-Tiered Governance
version: 1.0
created: 2026-01-12
source: Dataiku Research
status: active
---

# Seed Name

## What It Is
{description}

## Why It Matters
{rationale}

## The Pattern
{pattern description}

## Revisit Trigger
{when to apply this seed}

## Dojo Application
{how Dojo uses this pattern}

## What It Refuses
{what this pattern explicitly rejects}

## Checks
- [ ] {validation check 1}
- [ ] {validation check 2}

## Related Seeds
- {seed name}: {relationship}

Usage Tracking

The skill automatically tracks seed usage in /home/ubuntu/.seed-usage.json:

{
  "seeds": {
    "04_agent_connect": {
      "usage_count": 3,
      "last_used": "2026-01-29T12:00:00Z",
      "sessions": ["session_123", "session_456"]
    }
  },
  "session_seeds": {
    "session_123": ["04_agent_connect", "10_shared_infrastructure"]
  }
}

Reference Documents

seed_catalog.md

Quick reference to all 10 seeds plus meta-seed:

• One-line descriptions
• Triggers and refusals
• Seed relationships
• Usage patterns (5 common scenarios)
• Maintenance guidelines

Read this when:

• Getting overview of all seeds
• Understanding seed relationships
• Learning usage patterns

Common Use Cases

Use Case 1: Architecting Multi-Agent System

User says: "How should we architect the multi-agent system?"

Workflow:

1. Run suggest_seeds.py multi-agent architecture coordination
2. Top suggestions: Agent Connect, Shared Infrastructure, Three-Tiered Governance
3. Apply Seed 04 (Agent Connect)
4. Follow application checklist
5. Track usage

Output:

Based on your query, I recommend Agent Connect (Seed 04). This seed provides a routing-first pattern where the Supervisor acts as a single entry point, routing to specialized agents (Dojo, Librarian, Debugger, Builder) based on context. This prevents agent sprawl and keeps coordination manageable.

Use Case 2: Optimizing Token Usage

User says: "Token usage is spiking, how do we optimize?"

Workflow:

1. Run suggest_seeds.py token cost context optimization
2. Top suggestions: Context Iceberg, Cost Guard, Mode-Based Complexity Gating
3. Apply Seed 03 (Context Iceberg)
4. Implement hierarchical context loading (Tier 1-4)
5. Set pruning triggers (80%, 90%, 95%)

Output:

The Context Iceberg pattern (Seed 03) explains the 6x token multiplier from demo to production. Implement hierarchical context loading with 4 tiers, and set pruning triggers at 80% (prune Tier 4), 90% (prune Tier 3), and 95% (alert user).

Use Case 3: Debugging Unexpected Behavior

User says: "Why did Dojo choose Scout instead of Mirror?"

Workflow:

1. Run suggest_seeds.py debugging trace transparency
2. Top suggestion: Harness Trace
3. Apply Seed 02 (Harness Trace)
4. Review trace spans for mode_selection
5. Read metadata.reasoning

Output:

Use the Harness Trace (Seed 02) to inspect the decision path. Check the mode_selection span in the trace JSON. The metadata.reasoning field will explain why Scout was chosen over Mirror.

Use Case 4: Building Trust with Users

User says: "Users don't understand our recommendations"

Workflow:

1. Run suggest_seeds.py trust transparency trace
2. Top suggestions: Harness Trace, Go-Live Bundles, Governance Multiplies Velocity
3. Apply Seed 02 (Harness Trace) for transparency
4. Apply Seed 05 (Go-Live Bundles) for repeatability
5. Apply Meta-Seed for philosophy

Output:

Build trust through Harness Trace (transparency), Go-Live Bundles (repeatability), and the Governance Multiplies Velocity philosophy. Show users the decision path, make sessions exportable and inspectable, and emphasize that governance accelerates delivery.

Use Case 5: Planning New Feature

User says: "Let's plan the Workbench feature"

Workflow:

1. Run suggest_seeds.py planning feature governance
2. Top suggestions: Three-Tiered Governance, Cost Guard, Shared Infrastructure
3. Apply Seed 01 (Three-Tiered Governance)
4. Define strategic (principles), tactical (standards), operational (tools)
5. Apply Seed 06 (Cost Guard) to budget for 5-10x multiplier

Output:

Start with Three-Tiered Governance (Seed 01). Define strategic principles (does this align with "no autopilot"?), tactical standards (DojoPacket schema, routing logic), and operational tools (tests, traces). Then apply Cost Guard (Seed 06) to budget for the full iceberg, not just API costs.

Integration with Dojo Agent

When Dojo is reasoning, it can reference seeds:

Example:

"Considering Agent Connect (Seed 04) for this multi-agent architecture. Using Supervisor as router with transparent handoffs."

Example:

"Applying Context Iceberg (Seed 03) to manage token usage. Loading Tier 1 + Tier 2 context for this query."

Seed Relationships

Foundational Seeds (Start Here)

• 01. Three-Tiered Governance - Framework for all other seeds
• Meta. Governance Multiplies Velocity - Philosophy

Operational Seeds (Day-to-Day)

• 02. Harness Trace - Transparency and debugging
• 03. Context Iceberg - Token and cost management
• 07. Safety Switch - Error handling
• 09. Mode-Based Complexity Gating - Query routing

Architectural Seeds (System Design)

• 04. Agent Connect - Multi-agent coordination
• 10. Shared Infrastructure - Reusable services

Delivery Seeds (Shipping)

• 05. Go-Live Bundles - Packaging and reuse
• 06. Cost Guard - Budgeting and planning

UX Seeds (User Experience)

• 08. Implicit Perspective Extraction - Reduce friction

Field Seeds (From Practice)

• 11. Voice Before Structure - Ground in philosophy before writing
• 12. Pointer Directories - Understand provenance before filling gaps
• 13. Granular Visibility - Track progress for the user, not the agent

Seed Maintenance

When to Update a Seed

• Pattern evolves based on new learnings
• Better approach discovered
• User feedback suggests improvement

When to Archive a Seed

• Pattern no longer applies
• Superseded by better approach
• Context has fundamentally changed

Versioning Convention

• 1.0 - Initial version from Dataiku research
• 1.1 - Minor update (clarification, example added)
• 2.0 - Major update (pattern changed)

Update Process

1. Identify need for update
2. Draft new version
3. Test with real scenarios
4. Update seed file with "Revised:" section
5. Update seed_catalog.md
6. Notify users of change

Best Practices

Frequency

• Proactive: Suggest seeds during planning and design
• Reactive: Apply seeds when debugging or optimizing
• Continuous: Track usage to identify most valuable seeds

Application

• Read the full seed before applying (don't just skim)
• Follow the checklist to validate application
• Note what it refuses to avoid anti-patterns
• Track usage to measure effectiveness

Evolution

• Update when patterns evolve (not just for the sake of updating)
• Archive when obsolete (don't hoard outdated seeds)
• Version carefully (breaking changes = major version bump)

Limitations

What This Skill Does

• Suggest relevant seeds based on keywords
• Load and explain how to apply seeds
• Track usage over time
• Provide quick reference to all seeds

What This Skill Doesn't Do

• Automatically apply seeds (you still need to implement)
• Make architectural decisions for you
• Guarantee success (seeds are patterns, not silver bullets)
• Replace deep thinking (seeds guide, they don't replace reasoning)

Quick Reference

Task	Command
Suggest seeds	python3.11 suggest_seeds.py <keywords...>
Apply seed	python3.11 apply_seed.py <seed_id>
View catalog	file read references/seed_catalog.md
Check usage	cat /home/ubuntu/.seed-usage.json
List all seeds	ls /home/ubuntu/skills/seed-library/seeds/

Success Metrics

• Accessibility: Find relevant seed in < 10 seconds
• Consistency: Seeds applied correctly 90%+ of the time
• Evolution: Seeds updated when patterns improve
• Reuse: Seeds used across multiple sessions
• Learning: New seeds added as patterns emerge

Output

• A seed recommendation saved to /home/ubuntu/seed-suggestions.md — top 3 ranked seeds with relevance scores and file paths
• A seed application guide saved to /home/ubuntu/seed-<seed_id>-applied.md — full seed content, application checklist, and next steps
• Usage tracked automatically in /home/ubuntu/.seed-usage.json

Examples

Scenario 1: User says "which seed applies to multi-agent coordination?" → runs suggest_seeds.py multi-agent coordination, returns top 3 (Agent Connect, Three-Tiered Governance, Shared Infrastructure) with relevance scores, saves to seed-suggestions.md.

Scenario 2: User says "apply the Context Iceberg seed" → runs apply_seed.py 03_context_iceberg, loads full seed content, generates application checklist for the current task, saves guide to seed-03_context_iceberg-applied.md.

Edge Cases

• If no keywords match any seed triggers, prompt the user for the core problem being solved (cost? coordination? trust?) rather than returning an empty result.
• If the user requests a seed by name that does not exist in the library, list the 13 available seeds and ask for clarification.

Anti-Patterns

• Suggesting a seed without running the suggestion script — keyword matching is essential for relevance; do not rely on recall alone.
• Applying a seed without reading the full seed content — skimming the trigger and skipping "What It Refuses" leads to misapplication.