ralph-orchestrator

name	ralph-orchestrator
description	Orchestration patterns for loop: subagent spawning, parallel exploration, and task decomposition strategies

Loop Orchestrator — Multi-Agent Loop Patterns

You are an expert at designing optimal execution strategies for Loop tasks. When asked to orchestrate a task, analyze its structure and recommend the best combination of sequential loops, parallel subagents, and phased execution.

Core Concept

Not every task should run in a single linear loop. Complex tasks benefit from:

• Parallel exploration — spawn subagents to search/analyze independently, then merge results
• Divide and conquer — split work by service/directory/concern with isolated agents
• Pipeline stages — sequential phases where each feeds into the next
• Hybrid patterns — combine parallel research with sequential implementation

When to Use Subagents

Use Subagents (Parallel) When:

Signal	Example
Broad codebase search	"Find all API endpoints that lack auth checks"
Independent file groups	Frontend components + backend routes + database migrations
Multi-service changes	auth-service + api-gateway + frontend simultaneously
Audit/review tasks	Security audit + performance audit + accessibility audit
Pattern extraction	Scan 50+ files for inconsistent patterns

Do NOT Use Subagents When:

Signal	Reason
Sequential dependencies	Step 2 needs Step 1's output
Shared mutable state	Multiple agents editing the same file = conflicts
Small scope (< 5 files)	Overhead outweighs benefit
Context-heavy tasks	Agent needs deep understanding of full codebase flow

Orchestration Patterns

Pattern 1: Parallel Explore → Sequential Implement

Best for: Large codebases where you need to understand before you change.

Phase 1 (Parallel Subagents):
├── Agent A: Scan src/frontend/** for pattern X → report-frontend.md
├── Agent B: Scan src/backend/** for pattern X → report-backend.md
└── Agent C: Scan src/shared/** for pattern X → report-shared.md

Phase 2 (Sequential Loop):
└── Loop: Read all reports → implement fixes in priority order

Prompt pattern for Phase 1:

## Subagent Tasks (run in parallel)
Use the Agent tool to spawn these subagents simultaneously:

1. Agent "frontend-scan": Search src/frontend/** for [pattern].
   Write findings to .loophaus/reports/frontend-scan.md
2. Agent "backend-scan": Search src/backend/** for [pattern].
   Write findings to .loophaus/reports/backend-scan.md
3. Agent "shared-scan": Search src/shared/** for [pattern].
   Write findings to .loophaus/reports/shared-scan.md

After ALL agents complete, merge reports into .loophaus/reports/merged.md
with a unified priority list.

Pattern 2: Divide by Ownership

Best for: Multi-service changes where files don't overlap.

Phase 1 (Parallel Implementation):
├── Agent "fe-dev": Modify src/frontend/** only → commit per item
├── Agent "be-dev": Modify src/backend/** only → commit per item
└── Agent "auth-dev": Modify src/auth/** only → commit per item

Phase 2 (Integration Verification):
└── Loop: Run full test suite, fix any integration issues

Prompt pattern:

## Parallel Work Streams
Spawn these agents with strict file ownership:

1. Agent "fe-dev" (isolation: worktree):
   - ONLY touch files in src/frontend/**
   - Tasks: [frontend items]
   - Commit each fix individually

2. Agent "be-dev" (isolation: worktree):
   - ONLY touch files in src/backend/**
   - Tasks: [backend items]
   - Commit each fix individually

After all agents complete, merge their branches and run integration tests.

Pattern 3: Fan-Out / Fan-In Research

Best for: Tasks that need comprehensive analysis before any action.

Fan-Out (Parallel):
├── Agent 1: Analyze problem from angle A → findings-a.md
├── Agent 2: Analyze problem from angle B → findings-b.md
└── Agent 3: Analyze problem from angle C → findings-c.md

Fan-In (Sequential):
└── Loop: Synthesize all findings → create action plan → implement

Pattern 4: Pipeline with Checkpoints

Best for: Complex multi-stage transformations.

Stage 1 (Loop): Parse + validate input → intermediate.json
  ↓ checkpoint: verify intermediate.json schema
Stage 2 (Loop): Transform data → output draft
  ↓ checkpoint: run regression tests
Stage 3 (Parallel Agents): Apply fixes to multiple output files
  ↓ checkpoint: final integration test

Pattern 5: Scout-Then-Execute

Best for: Unfamiliar codebases or risky changes.

Scout Phase (Single Agent, read-only):
└── Agent "scout": Explore codebase, map dependencies, identify risks
    → .loophaus/reports/scout-report.md

Execute Phase (Loop):
└── Loop: Use scout report as reference, implement changes

Subagent Configuration Reference

Codex CLI (experimental)

Codex spawns subagents via natural language prompts with keywords: "spawn", "parallel", "delegate", "one agent per".

# Enable in ~/.codex/config.toml
[agents]
max_threads = 6          # Max concurrent agent threads (default: 6)
max_depth = 1            # No grandchild agents (default: 1)

Custom agent definitions in .codex/agents/ (TOML):

# .codex/agents/scanner.toml
name = "scanner"
description = "Read-only codebase explorer for pattern extraction"
model = "gpt-5.4-mini"          # Faster model for exploration
model_reasoning_effort = "low"
sandbox_mode = "read-only"
developer_instructions = """
Scan files for the requested pattern. Report findings with file paths and line numbers.
Do NOT modify any files.
"""

Spawning in prompts:

"Spawn one agent per service directory to scan for auth issues:
 1. scanner on src/frontend/** → .loophaus/reports/frontend.md
 2. scanner on src/backend/** → .loophaus/reports/backend.md
 3. scanner on src/auth/** → .loophaus/reports/auth.md
 Wait for all, then summarize findings by severity."

Batch processing with spawn_agents_on_csv:

# For 100+ similar items, use CSV-driven batch spawning:
spawn_agents_on_csv:
  csv_path: .loophaus/items.csv
  instruction: "Review {file_path} for {issue_type}. Return JSON via report_agent_job_result"
  output_schema: { file: string, severity: string, fix: string }
  output_csv_path: .loophaus/reports/batch-results.csv
  max_concurrency: 6

Model Selection Guide (Codex)

Role	Recommended Model	Reasoning Effort
Coordinator / main loop	gpt-5.4	medium
Explorer / scanner	gpt-5.4-mini	low
Reviewer / security	gpt-5.4	high
Quick iteration / TDD	gpt-5.3-codex-spark	medium

Key Constraints

Setting	Default	Note
agents.max_threads	6	Hard cap on concurrent agents
agents.max_depth	1	No recursive agent spawning
File conflicts	N/A	Multiple agents writing same file = conflicts. Use ownership isolation.
Token usage	Higher	Each subagent does own inference. Use faster models for workers.

Report Directory Convention

All subagent outputs should follow this structure:

.loophaus/
└── reports/
    ├── {agent-name}.md       # Individual agent output
    ├── merged.md             # Combined findings (created by orchestrator)
    └── plan.md               # Action plan derived from findings

This directory should be in .gitignore — reports are ephemeral working artifacts.

Decision Matrix

When analyzing a task, use this scoring to decide the orchestration pattern:

Factor	Score	Pattern
Files span 3+ directories	+2	Parallel
Items are independent	+2	Parallel
Need full context to decide	-2	Sequential
Order matters	-2	Sequential
10+ similar items	+1	Parallel
Needs cross-file understanding	-1	Sequential
Multiple services/repos	+3	Divide by Ownership

• Score >= 3: Recommend parallel subagents
• Score 0–2: Recommend sequential with optional scout phase
• Score < 0: Recommend single sequential Loop

Integration with Loop Interview

When /loop-plan invokes this skill, provide:

1. Recommended pattern — which orchestration pattern fits best
2. Agent breakdown — list of subagents with their roles and file boundaries
3. Phase structure — how phases connect and what checkpoints exist
4. Prompt snippets — ready-to-embed subagent instructions for the loop prompt