Design Orchestration (Meta-Skill)

Purpose

Ensure that ideas become designs, designs are reviewed, and only validated designs reach implementation.

This skill does not generate designs. It controls the flow between other skills.

Operating Model

This is a routing and enforcement skill, not a creative one.

It decides:

• which skill must run next
• whether escalation is required
• whether execution is permitted

Controlled Skills

This meta-skill coordinates the following:

• brainstorming — design generation
• multi-agent-brainstorming — design validation
• downstream implementation or planning skills

Entry Conditions

Invoke this skill when:

• a user proposes a new feature, system, or change
• a design decision carries meaningful risk
• correctness matters more than speed

Routing Logic

Step 1 — Brainstorming (Mandatory)

If no validated design exists:

• Invoke brainstorming
• Require:
  • Understanding Lock
  • Initial Design
  • Decision Log started

You may NOT proceed without these artifacts.

Step 2 — Risk Assessment

After brainstorming completes, classify the design as:

• Low risk
• Moderate risk
• High risk

Use factors such as:

• user impact
• irreversibility
• operational cost
• complexity
• uncertainty
• novelty

Step 3 — Conditional Escalation

• Low risk
  → Proceed to implementation planning

• Moderate risk
  → Recommend multi-agent-brainstorming

• High risk
  → REQUIRE multi-agent-brainstorming

Skipping escalation when required is prohibited.

Step 4 — Multi-Agent Review (If Invoked)

If multi-agent-brainstorming is run:

Require:

• completed Understanding Lock
• current Design
• Decision Log

Do NOT allow:

• new ideation
• scope expansion
• reopening problem definition

Only critique, revision, and decision resolution are allowed.

Step 5 — Execution Readiness Check

Before allowing implementation:

Confirm:

• design is approved (single-agent or multi-agent)
• Decision Log is complete
• major assumptions are documented
• known risks are acknowledged

If any condition fails:

• block execution
• return to the appropriate skill

Enforcement Rules

• Do NOT allow implementation without a validated design
• Do NOT allow skipping required review
• Do NOT allow silent escalation or de-escalation
• Do NOT merge design and implementation phases

Exit Conditions

This meta-skill exits ONLY when:

• the next step is explicitly identified, AND
• all required prior steps are complete

Possible exits:

• “Proceed to implementation planning”
• “Run multi-agent-brainstorming”
• “Return to brainstorming for clarification”
• "If a reviewed design reports a final disposition of APPROVED, REVISE, or REJECT, you MUST route the workflow accordingly and state the chosen next step explicitly."

Design Philosophy

This skill exists to:

• slow down the right decisions
• speed up the right execution
• prevent costly mistakes

Good systems fail early. Bad systems fail in production.

This meta-skill exists to enforce the former.

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags design-orchestration <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

• Strategy: This skill communicates via the shared memory system.
• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns