plan-party

name	plan-party
description	Parallel strategy generation using G-5 Planning. Deploy 10 planning probes for multi-perspective implementation planning. Use for complex task planning after reconnaissance.
model_tier	opus
parallel_hints	{"can_parallel_with":["search-party"],"must_serialize_with":["SCHEDULING","safe-schedule-generation"],"preferred_batch_size":10}
context_hints	{"max_file_context":100,"compression_level":1,"requires_git_context":true,"requires_db_context":false}
escalation_triggers	[{"pattern":"production|deployment","reason":"Production changes require human approval"},{"pattern":"breaking.*change","reason":"Breaking changes need careful planning"},{"keyword":["high-stakes","compliance","ACGME"],"reason":"High-stakes changes require human oversight"}]

PLAN_PARTY Skill

Purpose: Coordinated parallel planning with 10 strategy probes Created: 2025-12-31 Trigger: /plan-party command Aliases: /plan, /strategy, /pp Owner: G5_PLANNING (G-5 Staff)

---

When to Use

Deploy PLAN_PARTY when you need multi-perspective implementation strategies:

• Complex multi-phase implementations
• High-stakes changes (production, compliance)
• Multiple valid approaches exist
• User asked for a plan before execution
• Task touches 3+ coordinator domains
• Previous similar task had issues
• After SEARCH_PARTY reconnaissance

Do NOT use for:

• Simple, obvious tasks (just do it)
• Emergency/time-critical (P0 - no time for planning)
• Already have a clear, validated plan
• Single-domain, single-agent work

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Economics: Zero Marginal Wall-Clock Cost

Critical Understanding: Parallel planners with the same timeout cost nothing extra in wall-clock time.

Sequential (BAD):        Parallel (GOOD):
10 probes x 60s each     10 probes x 60s in parallel
Total: 600s              Total: 60s (10x faster)

Implication: Always spawn all 10 probes. There is no cost savings from running fewer.

---

The Ten Planning Probes

Probe	Framing	What It Produces
CRITICAL_PATH	Time-optimal	Minimum steps, dependency chain, timeline
RISK_MINIMAL	Safety-first	Conservative approach, rollback plan
PARALLEL_MAX	Concurrency	Maximum parallel streams, sync points
RESOURCE_MIN	Lean	Smallest agent count, reuse specialists
QUALITY_GATE	Test-driven	Verification at each step
INCREMENTAL	Progressive	Small PRs, feature flags
DOMAIN_EXPERT	Specialist-led	Route to coordinators
PRECEDENT	Pattern-matching	Apply proven patterns
ADVERSARIAL	Red team	Failure modes, edge cases
SYNTHESIS	Multi-objective	Pareto-optimal balance

Probe Details

CRITICAL_PATH Probe

Focus: What's the fastest route to completion?

• Dependency graph (DAG)
• Minimum steps to completion
• Critical path identification
• Timeline estimate, bottleneck warnings

RISK_MINIMAL Probe

Focus: How do we minimize blast radius?

• Risk matrix for each step
• Fallback and rollback procedures
• Safety gates, escalation triggers

PARALLEL_MAX Probe

Focus: Maximum concurrency extraction

• Parallelization opportunities
• Stream assignments (A, B, C...)
• Synchronization points, merge strategy

RESOURCE_MIN Probe

Focus: Lean execution, minimal overhead

• Minimum agent count
• Specialist reuse plan
• "Do it yourself" vs delegate decision

QUALITY_GATE Probe

Focus: Test-driven, verification-first

• Test strategy per step
• Acceptance criteria
• Coverage requirements, CI/CD integration

INCREMENTAL Probe

Focus: Progressive delivery, fast feedback

• Smallest viable increments
• PR strategy (many small vs few large)
• Feature flag strategy

DOMAIN_EXPERT Probe

Focus: Leverage existing coordinator expertise

• Coordinator assignment matrix
• Domain boundary respect
• Expert agent selection

PRECEDENT Probe

Focus: Apply proven patterns from history

• Similar past tasks/sessions
• Applicable patterns
• Lessons learned, anti-patterns to avoid

ADVERSARIAL Probe

Focus: Red team the plan, find weaknesses

• Failure mode analysis
• Edge case inventory
• Stress points, "What kills us?" analysis

SYNTHESIS Probe

Focus: Balance all concerns, find Pareto frontier

• Multi-objective scoring
• Trade-off analysis
• Balanced recommendation

---

Invocation

Full Deployment (10 probes)

/plan-party

Deploys all 10 planning probes on current intel.

With Specific Goal

/plan-party "Implement batch swap support"

Deploys probes with explicit goal framing.

After SEARCH_PARTY

# Recommended workflow
/search-party backend/app/scheduling/
# Review intel brief
/plan-party

---

Decision Tree: SEARCH_PARTY vs PLAN_PARTY

Scenario	Protocol	Example
Need codebase intel only	SEARCH_PARTY	"What's the state of resilience?"
Have intel, need strategy	PLAN_PARTY	"Plan implementation for discussed issue"
Complex task, no context	SEARCH_PARTY then PLAN_PARTY	"Add batch swap support"
Simple task	Neither	"Fix typo in README"

Decision Rule

def choose_protocol(task: Task) -> str:
    if task.complexity <= 5:
        return "DIRECT_EXECUTION"

    if not task.has_reconnaissance:
        return "SEARCH_PARTY"

    if task.complexity > 10 or task.touches_3_plus_domains:
        return "PLAN_PARTY"

    return "DIRECT_EXECUTION"

---

IDE Crash Prevention (CRITICAL)

DO NOT have ORCHESTRATOR spawn 10 planning probes directly. This causes IDE seizure and crashes.

CORRECT Pattern:

ORCHESTRATOR -> spawns 1 G5_PLANNING (G-5 Commander)
                    |
              G5_PLANNING deploys 10 probes internally
              (manages parallelism, synthesizes results)

WRONG Pattern:

ORCHESTRATOR -> spawns 10 planners directly -> IDE CRASH

The G-5 Commander (G5_PLANNING) absorbs the parallelism complexity. ORCHESTRATOR only ever spawns 1 coordinator.

---

Spawn Pattern via G5_PLANNING Commander

Via G5_PLANNING Commander (CORRECT)

# ORCHESTRATOR spawns G5_PLANNING who manages the 10 planning probes
Task(
    subagent_type="general-purpose",
    description="G5_PLANNING: PLAN_PARTY Commander",
    prompt="""
## Agent: G5_PLANNING (G-5 Commander)

You are the G-5 Plans Commander for PLAN_PARTY deployment.

## Mission
Deploy 10 planning probes in parallel. Each probe applies a different strategic framing.
Collect all plans and synthesize into unified execution plan.

## Intel Brief
[Insert G2_RECON intel brief here]

## Your Planning Probes to Deploy
1. CRITICAL_PATH - Time-optimal planning
2. RISK_MINIMAL - Safety-first approach
3. PARALLEL_MAX - Maximum concurrency
4. RESOURCE_MIN - Lean execution
5. QUALITY_GATE - Test-driven approach
6. INCREMENTAL - Progressive delivery
7. DOMAIN_EXPERT - Specialist-led routing
8. PRECEDENT - Pattern matching
9. ADVERSARIAL - Red team analysis
10. SYNTHESIS - Multi-objective balance

## Spawn each using Task tool with subagent_type="Explore"

## After all report back:
1. Cross-reference plans
2. Calculate convergence score
3. Identify trade-offs
4. Generate execution plan
5. Report to ORCHESTRATOR
"""
)

Direct Deployment (Only if G5_PLANNING unavailable)

# Deploy all 10 probes in parallel
# WARNING: Only use if spawning from within a coordinator, NOT from ORCHESTRATOR
# Total: 10 probes, wall-clock = single probe timeout

spawn_parallel([
    Task(subagent_type="Explore", description="CRITICAL_PATH",
         prompt="Plan with time-optimal framing: minimum steps, dependencies"),
    Task(subagent_type="Explore", description="RISK_MINIMAL",
         prompt="Plan with safety-first framing: rollback at every step"),
    Task(subagent_type="Explore", description="PARALLEL_MAX",
         prompt="Plan with concurrency framing: maximum parallel streams"),
    Task(subagent_type="Explore", description="RESOURCE_MIN",
         prompt="Plan with lean framing: minimal agents, reuse specialists"),
    Task(subagent_type="Explore", description="QUALITY_GATE",
         prompt="Plan with test-driven framing: verification at each step"),
    Task(subagent_type="Explore", description="INCREMENTAL",
         prompt="Plan with progressive framing: small PRs, feature flags"),
    Task(subagent_type="Explore", description="DOMAIN_EXPERT",
         prompt="Plan with specialist framing: route to domain coordinators"),
    Task(subagent_type="Explore", description="PRECEDENT",
         prompt="Plan with pattern framing: apply proven approaches"),
    Task(subagent_type="Explore", description="ADVERSARIAL",
         prompt="Plan with red team framing: failure modes, edge cases"),
    Task(subagent_type="Explore", description="SYNTHESIS",
         prompt="Plan with balanced framing: Pareto-optimal trade-offs"),
])

---

Plan Synthesis

After all 10 probes report back:

1. Cross-reference plans across framings
2. Calculate convergence score (N/10 probes agree)
3. Identify trade-offs (speed vs safety, parallel vs lean)
4. Generate execution plan

Convergence Analysis

Key Insight: Same goal, different strategies. Convergence reveals high-confidence decisions:

Convergence Type	Signal Meaning
10/10 agree on step	High-confidence critical step
CRITICAL_PATH vs RISK_MINIMAL disagree	Speed/safety trade-off to surface
PARALLEL_MAX and DOMAIN_EXPERT align	Coordinator assignment validated
ADVERSARIAL flags unique concern	Hidden risk discovered
PRECEDENT matches approach	Pattern is proven

Strategy Selection Matrix

Scenario	User Risk Tolerance	Selection
High convergence (8+/10)	Any	Execute consensus plan
Speed vs Safety split	LOW	RISK_MINIMAL approach
Speed vs Safety split	HIGH	CRITICAL_PATH approach
Parallelism debate	Time-critical	PARALLEL_MAX approach
Parallelism debate	Resource-limited	RESOURCE_MIN approach

---

Output Format

Execution Plan

## PLAN_PARTY Execution Plan

### Mission: [What was asked]

### Selected Strategy: [PROBE_NAME] with modifications from [OTHER_PROBES]

### Convergence Score: [N/10 probes agreed on core approach]

### Execution Plan

#### Phase 1: [Name] (Parallel)
| Stream | Owner | Task | Depends On |
|--------|-------|------|------------|
| A | COORD_ENGINE | [task] | - |
| B | COORD_PLATFORM | [task] | - |

#### Phase 2: [Name] (Sequential)
| Step | Owner | Task | Gate |
|------|-------|------|------|
| 1 | [agent] | [task] | [test/review] |
| 2 | [agent] | [task] | [test/review] |

### Risk Mitigations (from ADVERSARIAL)
- [Risk 1]: [Mitigation baked into plan]

### Quality Gates (from QUALITY_GATE)
- [ ] Gate 1: [criteria]

### Rollback Plan (from RISK_MINIMAL)
- Checkpoint 1: [what to save]
- Rollback procedure: [how to undo]

### Trade-offs Accepted
- Chose [X] over [Y] because [rationale]

### Estimated Timeline
- Phase 1: [time]
- Phase 2: [time]
- Total: [time]

### Confidence: [HIGH/MEDIUM/LOW]

---

Integration with SEARCH_PARTY

Full Intelligence-to-Execution Pipeline

User Request
    |
ORCHESTRATOR receives task
    |
G2_RECON deploys SEARCH_PARTY (10 recon probes)
    |--- PERCEPTION, INVESTIGATION, ARCANA
    |--- HISTORY, INSIGHT, RELIGION
    |--- NATURE, MEDICINE, SURVIVAL, STEALTH
    |
G2_RECON synthesizes Intel Brief
    |
G5_PLANNING deploys PLAN_PARTY (10 planning probes)
    |--- CRITICAL_PATH, RISK_MINIMAL, PARALLEL_MAX
    |--- RESOURCE_MIN, QUALITY_GATE, INCREMENTAL
    |--- DOMAIN_EXPERT, PRECEDENT, ADVERSARIAL, SYNTHESIS
    |
G5_PLANNING synthesizes Execution Plan
    |
ORCHESTRATOR reviews, approves, or escalates to user
    |
Parallel Execution
    |
Result Synthesis
    |
User Delivery

Signal Propagation

SEARCH_PARTY -> Intel Brief -> PLAN_PARTY -> Execution Plan -> ORCHESTRATOR
     |                            |                              |
(10 recon signals)          (10 plan signals)          (execution signals)
     |                            |                              |
Synthesis                    Synthesis                      Synthesis
(G2_RECON)                 (G5_PLANNING)                  (COORD_AAR)

---

Timeout Profiles

Profile	Duration	Best For
DASH	60s	Quick planning, simple tasks
STANDARD	90s	Normal planning (default)
DEEP	180s	Complex multi-domain planning

---

Failure Recovery

Minimum Viable Plan

Mission can proceed if:

• CRITICAL_PATH (baseline plan) present
• RISK_MINIMAL (safety) present
• ADVERSARIAL (red team) present
• At least 4 of remaining 7 probes

Circuit Breaker

If > 3 consecutive probe failures: Trip to OPEN state, fall back to single-planner mode.

---

Protocol Reference

Full protocol documentation: .claude/protocols/PLAN_PARTY.md

---

Related Skills

Skill	When to Use
search-party	Upstream reconnaissance before planning
qa-party	Downstream validation after execution
startup	Session initialization
startupO	ORCHESTRATOR mode initialization
systematic-debugger	Post-execution debugging if issues

---

PLAN_PARTY: Ten strategies, one goal, signal-amplified planning. Convergence is confidence.