Deep Plan

Multi-perspective architecture planning. Spawns specialist agents for different quality-attribute concerns that evaluate trade-offs before you write code.

When This Skill Activates

Trigger on architecture and planning requests:

• User says: "plan", "architect", "design", "how should I architect..."
• User asks: "deep plan", "plan the architecture", "technical design"
• User wants pre-implementation architecture evaluation

Do NOT activate on every task. This is for deliberate architecture planning, not quick feature additions. Simple features don't need a 6-agent swarm.

Phase 1: Context Gathering

Before spawning agents, establish scope:

1. Check for deep-research artifact: Scan recent conversation context for the structured JSON artifact from deep-research (contains task, tech_stack, approaches, findings, verdict). Validate the artifact has at minimum: task, tech_stack, and at least one approach with a summary. If any required field is missing or empty, treat as if no artifact was found and fall through to step 2. If valid, use it as primary input — skip step 2 and use the recommended approach as the starting architecture to evaluate.
2. If no artifact: Gather context manually:
   • Run git rev-parse --show-toplevel for {repo_root}
   • Check for package manifests to identify {tech_stack}. If no manifests are found, infer tech_stack from primary file extensions (.py, .ts, .go, etc.) or ask the user. Never pass an empty tech_stack to planning agents.
   • Ask the user what they're building and what constraints matter most
   • Read existing code in the affected area to understand current architecture
3. speak-memory: If .speak-memory/index.md exists and an active story matches, read it for context. Skip if .speak-memory/ does not exist.
4. Frame the architecture: State what is being planned:
   • Feature/System: What is being designed
   • Scope: Which components, modules, or layers are affected
   • Constraints: Performance targets, security requirements, compatibility needs
   • Starting approach: From deep-research artifact, or from user description
5. Set scope budget: "Budget: 5 planning specialists + 1 trade-off arbiter = 6 agent calls." Present to user:

Planning: {feature/system}
Scope: {components affected}
Constraints: {key constraints}
Starting approach: {from research or user}

Agents: API design, data architecture, security, performance, maintainability
        + 1 trade-off arbiter = 6 calls.
Proceed?

Wait for user confirmation before spawning agents. If the user declines or asks to revise, update the scope, constraints, or starting approach and re-present. Do not spawn agents until the user confirms. If the user cancels entirely, output: "Planning cancelled by user." and stop.

Phase 2: Parallel Planning

Spawn all 5 specialist agents in a single message (parallel execution). Each agent analyzes the architecture from their quality-attribute perspective and grounds recommendations in the existing codebase.

See references/agent-roles.md for full prompts.

Agent tool grants: All planning agents get Read, Glob, Grep (read-only codebase exploration). They analyze the existing codebase to ground their recommendations in what already exists.

Required output format (every recommendation must include priority):

[
  {
    "priority": "critical|important|nice-to-have",
    "concern": "which quality attribute this addresses",
    "recommendation": "what the architecture should do",
    "rationale": "why, grounded in codebase evidence or established practice",
    "trade_offs": "what this costs or constrains",
    "affected_files": ["files that would be created or modified"],
    "references": ["codebase files or patterns referenced"]
  }
]

Priority levels:

• critical: Architecture cannot succeed without this. Missing it causes failure.
• important: Significantly improves the architecture. Missing it creates tech debt.
• nice-to-have: Good practice but not essential for initial implementation.

Return empty array [] if no recommendations for that perspective.

Each agent: max 10 recommendations, prioritized. Never tag nice-to-have as critical.

Error handling: If an agent returns non-JSON output, strip code fences and attempt JSON extraction. If extraction fails or the agent times out, treat as empty array [] and log a warning. Continue with results from agents that succeeded.

Phase 3: Synthesis

Merge all 5 recommendation arrays. Apply synthesis rules from references/synthesis.md:

1. Merge: Collect all 5 arrays, tag each with source agent name. Note which agents failed or returned empty.
2. Deduplicate: Merge overlapping recommendations; note corroboration from multiple agents as a confidence signal.
3. Identify tensions: Flag where agents' recommendations conflict — Agent A wants X but Agent B wants Y and they are mutually exclusive or in friction.
4. Resolve or surface: Resolve clear tensions (with rationale). Surface genuine trade-offs as explicit decision points the user must weigh.
5. Rank: Sort by priority (critical first), then corroboration count. Cap at 25 recommendations — drop low-priority nice-to-have items first.
6. Generate architecture: Propose the architecture as a coherent design that integrates the ranked recommendations. Include 1-2 alternatives for major decision points where unresolved tensions exist.

Phase 4: Trade-off Arbiter

Spawn one trade-off arbiter agent (subagent_type: "general-purpose", model: "opus").

Give it:

• The synthesized architecture from Phase 3
• All unresolved tensions
• The original constraints and scope

The arbiter's job is NOT to disprove the plan (unlike a devil's advocate). It is to:

• Identify cross-cutting tensions where improving one quality attribute degrades another
• Find missing failure modes — what happens when things go wrong?
• Challenge complexity assumptions — is this over-engineered for the actual requirements?
• Suggest simplifications — can anything be deferred to a later iteration?

See references/agent-roles.md for the full arbiter prompt.

Handle the arbiter result:

• plan_sound: The architecture holds. Note any flagged tensions as known trade-offs. Still integrate non-empty missing_failure_modes into the Risks section and codebase_contradictions as caveats in the relevant Key Decisions.
• plan_over_engineered: Simplify. For each item in over_engineering, remove or defer the flagged component from the architecture. Directly rewrite the affected architecture sections — do not re-run synthesis or spawn new agents.
• plan_has_gaps: Critical gaps found. Map arbiter fields to plan sections: missing_failure_modes → add to Risks and Mitigations table; codebase_contradictions → add corrective notes to the relevant Key Decisions. Mark all additions with "[arbiter]" tags.

If arbiter_result is not one of the three recognized values, treat as the arbiter error case below.

Error handling: If the arbiter fails (non-JSON output, timeout, invalid structure, or unrecognized arbiter_result), log a warning: "Trade-off arbiter failed — plan not stress-tested." Add a risk item to the plan: "Architecture not stress-tested due to arbiter failure — increased risk of over-engineering or missing failure modes." Set metadata.arbiter_completed to false in the structured artifact. Continue to Phase 5.

Phase 5: Plan Delivery

Output the architecture plan using assets/plan-template.md format (MADR-inspired):

1. Context and problem statement — what is being built and why
2. Decision drivers — constraints and quality attributes that matter most
3. Architecture overview — the recommended design (components, data model, API surface)
4. Key decisions — each major choice with rationale and alternatives considered
5. Implementation checklist — ordered steps for building it
6. Risks and mitigations — what could go wrong and how to handle it
7. Security requirements — from security-reliability specialist (omit if none apply)
8. Trade-offs accepted — known costs of this approach
9. Trade-off arbiter assessment — result and any tensions, gaps, or simplifications
10. Future considerations — deferred nice-to-have items for later iterations

After the human-readable plan, output the structured artifact for downstream skills:

{
  "task": "what is being planned",
  "tech_stack": ["identified technologies"],
  "architecture": {
    "overview": "one-paragraph summary",
    "components": [
      {
        "name": "component name",
        "responsibility": "what it does",
        "interfaces": ["public API surface"],
        "dependencies": ["what it depends on"]
      }
    ],
    "data_model": {
      "entities": [{"name": "...", "fields": ["..."], "relationships": ["..."]}]
    },
    "api_surface": [
      {"endpoint_or_interface": "...", "method": "...", "description": "..."}
    ]
  },
  "decisions": [
    {
      "title": "decision name",
      "decision": "what was decided",
      "rationale": "why",
      "alternatives_considered": ["what else was evaluated"],
      "trade_offs": ["known costs"]
    }
  ],
  "implementation_plan": [
    {
      "step": 1,
      "description": "what to do",
      "files": ["files to create or modify"],
      "tests_needed": ["what to test"],
      "complexity": "low|medium|high"
    }
  ],
  "risks": [
    {
      "risk": "what could go wrong",
      "probability": "low|medium|high",
      "impact": "low|medium|high",
      "mitigation": "how to handle it"
    }
  ],
  "security_requirements": [
    {"requirement": "...", "implementation": "..."}
  ],
  "metadata": {
    "agents_completed": [],
    "agents_failed": [],
    "arbiter_completed": true,
    "total_recommendations": 0,
    "tensions_resolved": 0,
    "tensions_surfaced": 0,
    "timestamp": "ISO-8601"
  }
}

No-recommendations edge case: If all agents return empty arrays, output the artifact with empty fields and note: "Planning agents found no specific architectural concerns. The feature may be straightforward enough to implement directly without formal planning."

speak-memory: If an active story was loaded in Phase 1, update it with the architecture decisions and implementation plan summary.

Key Constraints

• All agents: Opus for maximum planning quality.
• Agent execution caps: Planning agents: max_turns: 30. Trade-off arbiter: max_turns: 20.
• Max 2 levels of nesting: orchestrator → specialist. Specialists never spawn agents.
• Scope budget: 5 planning agents + 1 trade-off arbiter = 6 total. Do not expand.
• Recommendation cap: Max 25 recommendations enter synthesis (after merge + dedup). Drop low-priority nice-to-have items first.
• All sub-agents are read-only — no code modifications, no git changes.
• Orchestrator write scope: Write and Edit tools MUST only target paths under .speak-memory/. Before any Write/Edit call, verify the target path starts with {repo_root}/.speak-memory/. Bash limited to: git rev-parse, package manifest reads. No other Bash commands from the orchestrator.
• The structured artifact stays in conversation context — no file writing.
• If deep-research artifact is available, use it. Do not re-run research.

Closing Checklist

Do not declare the plan done until all boxes are checked:

• Architecture scope stated with constraints and starting approach
• All 5 planning agents completed (returned valid JSON) or failed (logged as warning)
• Every recommendation tagged with priority (critical/important/nice-to-have)
• Tensions identified and resolved or surfaced as decision points
• Trade-off arbiter challenged the architecture
• MADR-inspired plan delivered with structured artifact
• Implementation checklist is ordered and actionable
• speak-memory story updated (if applicable)

Reference Files

Load only when needed:

• references/agent-roles.md — Full prompt templates for each planning specialist + trade-off arbiter
• references/synthesis.md — Trade-off evaluation, tension resolution, architecture generation
• assets/plan-template.md — MADR-inspired plan output format