Orche

A multi-agent orchestration engine that systematically executes complex tasks in 4 phases (Query → Plan → Execute → Verify). Ensures high-quality deliverables through phase gates, Critic debates, 14-item hallucination checks, and automatic regression on verification failure.

🎼 Orche — Multi-Phase Orchestration Engine

A multi-agent orchestration engine that executes complex tasks in Query → Plan (Debate) → Execute → Verify — 4 phases. A sub-agent panel debates, critiques, executes, and verifies, while the watchdog monitors the entire process.

---

📌 Table of Contents

1. Why Orche?
2. Comparison with Existing Orchestration Skills
3. Quick Start — First Orchestration in 5 Minutes
4. Phase Overview
5. Phase 0: Query
6. Phase 1: Planning
7. Phase 2: Execution
8. Phase 3: Verification
9. State Management
10. Directory Structure
11. Harness Rules (Safety Mechanisms)
12. Watchdog Integration
13. Hallucination Checklist (14 Items)
14. Cost Management
15. Exception Handling Summary
16. User Intervention Points
17. Abort Procedure
18. Session Disconnection Recovery
19. Demo Scenarios
20. Production Run Records
21. hallucination-guard Integration
22. Advanced Configuration
23. FAQ
24. Changelog

---

🎯 Why Orche?

Problem: Limitations of Existing Multi-Agent Execution

When complex tasks are delegated to AI agents, the following problems arise:

Problem	Symptom	Result
Hallucination	Uses non-existent APIs/libraries	Non-executable code
Execution without planning	Starts writing code immediately	Direction change costs explode
No verification	Deliverables "look plausible"	Don't actually work
Linear-only progression	Keeps going forward even on failure	Root cause unresolved
Cost explosion	Indiscriminate agent deployment	Budget exhaustion

Solution: Orche's 4-Phase Engine

text

┌─────────────────────────────────────────────────────────────┐
│                                                             │
│   Phase 0        Phase 1         Phase 2        Phase 3     │
│   ┌──────┐      ┌──────┐       ┌──────┐      ┌──────┐     │
│   │Query │─G0─▶│ Plan  │──G1─▶│Execute│─G2─▶│Verify│     │
│   │      │      │(Debate)│      │(Parall)│      │      │     │
│   └──────┘      └──────┘       └──────┘      └──┬───┘     │
│       │                             ▲            │         │
│       │                             │          fail?       │
│       │                             └──── YES ◀──┘         │
│       │                                                     │
│       └── Watchdog monitors the entire process ───────────┘  │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Key Differentiators:

• ✅ Phase Gates: Explicit condition fulfillment required at each phase transition
• ✅ Critic Debate: Advocate vs Critic debate improves plan quality
• ✅ 14-Item Hallucination Check: Fact, consistency, completeness, and hallucination pattern verification
• ✅ Auto Regression: Automatically returns to Phase 2 on verification failure
• ✅ Watchdog Integration: Monitors entire process, auto-detects deadlocks/loss/budget overruns
• ✅ Cost Management: 4-tier budget alerts + concurrent agent limit

---

📊 Comparison with Existing Orchestration Skills

Feature	dispatching-parallel-agents	parallel-agent-management	executing-plans	orche
Parallel execution	✅	✅	❌ (sequential)	✅
Pre-query phase	❌	❌	❌	✅ Phase 0
Debate/discussion	❌	❌	❌	✅ Includes Critic
Phase gates	❌	❌	✅ (checkpoints)	✅ 4 gates
Hallucination check	❌	❌	❌	✅ 14 items
Auto regression on verification failure	❌	❌	❌	✅ Up to 3 retries
Watchdog integration	❌	❌	❌	✅
Cost management	❌	❌	❌	✅ 4-tier budget
Session disconnection recovery	❌	❌	❌	✅ State file based
Best for	Simple parallel tasks	Codebase splitting	Sequential plan execution	Complex multi-step projects

When to Use Orche?

✅ Orche is appropriate when:

• Complex tasks requiring 3+ different roles
• Tasks where output accuracy matters (research, analysis, code design)
• Tasks where hallucination is critical
• Tasks with interdependent deliverables

❌ Other skills are better when:

• Simple 2-3 independent tasks in parallel → dispatching-parallel-agents
• File-by-file codebase splitting → parallel-agent-management
• Sequential execution with an already-confirmed plan → executing-plans

---

🚀 Quick Start — First Orchestration in 5 Minutes

Step 1: Install the Skill (30 Seconds)

Place the skill file in the OpenClaw skills directory:

bash

# Copy to skills directory
mkdir -p ~/.agents/skills/orche
cp SKILL.md ~/.agents/skills/orche/SKILL.md

Step 2: Trigger (10 Seconds)

Enter the /orche command in chat and describe your task:

text

/orche "Write a market analysis report on Korean AI startups"

Or in natural language:

text

Orchestrate this project systematically

Step 3: Phase 0 — Answer the Queries (2 Minutes)

Orche will ask 3~10 questions. Answer clearly:

text

🎼 /orche started — Phase 0: Query

📋 Questions to clarify requirements:

1. 📐 Scope: All AI fields? Specific segment (LLM, robotics, etc.)?
2. 🎯 Priority: Market size vs competitive analysis vs investment trends?
3. 📏 Success criteria: Report length? Number of data sources?
4. 🔧 Technical constraints: Specific data sources needed?
5. ⏰ Deadline: Is there a due date?

Step 4: Auto-Progress (Remaining)

Once you answer, Orche automatically:

• Phase 1: Builds plan through expert + Critic debate
• Phase 2: Sub-agents research and write in parallel
• Phase 3: Verification team runs hallucination check + cross-validation

Step 5: Receive Results

text

🎼 /orche Completion Report

📋 Task: orche-1711432800
⏱️ Total duration: 23 minutes
📊 By phase: Query 3m → Plan 5m → Execute 10m → Verify 5m
🤖 Agents deployed: 11
✅ Tasks completed: 5/5

📁 Deliverables:
- workspace/orche-1711432800/requirements.md
- workspace/orche-1711432800/final-plan.md
- workspace/orche-1711432800/tasks/
- workspace/orche-1711432800/verification/final-verdict.md

---

🔄 Phase Overview

text

Phase 0: Query    — Clarify requirements (3~10 questions)
Phase 1: Plan     — 3~7 sub-agents debate → finalize plan
Phase 2: Execute  — Split into tasks → 3~8 sub-agents execute in parallel
Phase 3: Verify   — 3~5 verifiers deployed → regress to Phase 2 if issues found

Phase Gate System

Each phase transition requires gate conditions to be met before proceeding. Automatic blocking on non-compliance.

Gate	Transition	Key Conditions
G0	Phase 0 → 1	Requirements structured + ambiguous expressions removed + user approval
G1	Phase 1 → 2	final-plan.md created + hallucination check passed + no circular dependencies
G2	Phase 2 → 3	All tasks completed + deliverables exist + no zombie agents
Verification	Phase 3 → Done	14-item hallucination check passed + all required deliverables present

On gate failure:

• G0 not passed → Phase 1 entry blocked. Re-confirmation requested from user.
• G1 not passed → Phase 2 entry blocked. Plan re-debate forced.
• G2 not passed → Phase 3 entry blocked. Missing tasks re-executed.
• Verification fail → Auto regression to Phase 2 (up to 3 times).

---

Phase 0: Query

Purpose

Remove ambiguity from user instructions and specify them to an actionable level.

Procedure

1. Analyze original request → identify missing/ambiguous areas
2. Generate 3~10 questions (at least 1 from each of 6 categories below)
3. Collect user responses → finalize requirements document (max 2 additional rounds)
4. On user approval → save requirements.md

Question Categories

Category	Example
Scope	What's in scope? What should be excluded?
Technical constraints	Language/framework/environment limitations?
Priority	What matters most? (speed/quality/cost)
Success criteria	How to judge completion? Deliverables?
Dependencies	External systems/APIs/data?
Risk	Impact of failure? Rollback needed?

Gate G0: Phase 0 → 1 Transition Conditions

• ✅ Requirements are structured (goal, constraints, deliverables specified)
• ✅ Ambiguous expressions removed ("appropriately", "etc." type expressions eliminated)
• ✅ Feasibility assessment complete (required tools/access confirmed)
• ✅ User confirmation received

Phase 0: When Deemed Infeasible

If G0 feasibility check determines the task is impossible:

1. Report specific reasons to user
2. Suggest alternatives (if possible)
3. Await user decision → modification instructions or termination
4. On termination → orche-state.json status → "rejected"

---

Phase 1: Planning

Debate Panel Composition (3~7 members)

Role	Responsibility
Domain Expert (1~2)	Draft plan based on domain expertise
Advocate	Strengthen plan merits, argue feasibility
Critic	Point out weaknesses/risks/gaps + hallucination check
Moderator	Synthesize debate, build consensus, write final plan

Sub-Agent Spawning

text

sessions_spawn:
  task: "<role-specific prompt>"
  label: "orche-<role>-<taskId>"
  model: "anthropic/claude-sonnet-4-6"  # Default (use opus for high complexity)
  mode: "run"

Model Selection Guide:

• Planning/debate: sonnet (cost-efficient)
• Complex domain experts: opus (when deep reasoning needed)
• Simple execution tasks: sonnet or haiku (fast processing)

Debate Protocol (Round-based, File-async)

Round 1:

1. Expert agents → spawn in parallel → each writes a proposal
2. Advocate → analyze strengths of drafts
3. Critic → point out weaknesses + hallucination check:
   • References to non-existent APIs/libraries?
   • Unrealistic performance/time estimates?
   • Logical contradictions?
   • Missing dependencies/prerequisites?
4. Moderator → synthesize → write final-plan.md

If unresolved issues remain → Round 2 (up to 3 rounds max).

Gate G1: Phase 1 → 2 Transition Conditions

• ✅ final-plan.md created
• ✅ Task list + dependencies + success criteria specified
• ✅ Hallucination check passed
• ✅ No circular dependencies
• ✅ Rollback strategy defined for each task

---

Phase 2: Execution

Task Splitting

Split from final-plan.md into independently executable task units:

json

{
  "id": "task-1",
  "title": "...",
  "description": "...",
  "dependencies": [],
  "assignedAgent": null,
  "status": "pending",
  "output": null
}

Task Status Flow: pending → running → completed / failed

Dependency-Based Scheduling

• Independent tasks → spawn in parallel immediately
• Dependent tasks → spawn after predecessor completion
• Max concurrent agents: 8 (cost explosion prevention: 3 concurrent recommended)

Required Inclusions in Execution Agent Prompts

All execution agent prompts must include the following:

1. Overall plan summary (brief)
2. Detailed task specification
3. Predecessor task deliverables (file paths)
4. Deliverable save path
5. Success criteria
6. Harness rules (see below)

Immediate Checks on Task Completion

• Deliverable file exists?
• Not empty?
• Success criteria met?
• No obvious hallucinations?
• On failure → retry that task only (max 2 times). 2 failures → alert user.

Gate G2: Phase 2 → 3 Transition Conditions

• ✅ All tasks completed or skipped_with_reason
• ✅ All deliverable files exist + non-empty
• ✅ No zombie agents/sessions
• ✅ No critical errors

---

Phase 3: Verification

Verification Team Composition (3~5 members)

Role	Responsibility
Completeness Verifier	All tasks executed, nothing missing
Accuracy Verifier	Deliverables match task spec + success criteria
Hallucination Verifier	Detect factual errors, logical contradictions, non-existent references
Integration Verifier	Cross-deliverable consistency, interface compatibility
Final Reviewer (optional)	Final review against original requirements

Verification Result Handling

Result	Action
All PASS	Phase 3 complete → normal termination
HIGH issue	Regress to Phase 2 (re-execute affected tasks only)
MED issue	Report to user, confirm re-execution
LOW issue	Record in report, proceed

Regression Conditions (Phase 3 → Phase 2)

Automatic regression to Phase 2 if any of the following apply:

• hallucination_score >= 2 (2+ failures in 14-item check)
• Required deliverable missing/corrupted
• Test failure rate > 30%
• User-mandatory conditions unmet
• Mutual contradiction between deliverables

Regression Limits

• retryCount maximum 3 times
• Same reason repeated 2 times → escalate to user
• Token budget 80% consumed → escalate to user

---

📦 State Management

All orchestration state is recorded in workspace/orche-state.json. The watchdog and each phase share this file to track progress.

json

{
  "taskId": "orche-<timestamp>",
  "phase": 0,
  "status": "active",
  "startedAt": "<ISO>",
  "phaseStartedAt": "<ISO>",
  "requirements": null,
  "plan": null,
  "tasks": [],
  "agents": [],
  "watchdog": {
    "enabled": true,
    "lastCheck": null
  },
  "retryCount": 0,
  "maxRetries": 3,
  "tokenBudget": {
    "total": 500000,
    "used": 0,
    "currency": "tokens"
  },
  "errors": []
}

State Field Descriptions

Field	Type	Description
taskId	string	Unique identifier (orche-<unix_timestamp>)
phase	number	Current phase (0~3)
status	string	active / completed / aborted / rejected
requirements	object	Requirements finalized in Phase 0
plan	object	Plan finalized in Phase 1
tasks	array	Phase 2 task list + status
agents	array	Currently active sub-agent session key list
retryCount	number	Phase 3 → 2 regression count
tokenBudget	object	Token budget management
errors	array	Error log

---

📁 Directory Structure

Each orchestration creates an independent working directory:

text

workspace/orche-<taskId>/
├── requirements.md              ← Phase 0 deliverable
├── round-1/                     ← Phase 1 debate records
│   ├── expert-1-proposal.md     ← Expert proposal
│   ├── expert-2-proposal.md     ← (if applicable)
│   ├── advocate-review.md       ← Advocate review
│   ├── critic-review.md         ← Critic review
│   └── moderator-summary.md     ← Moderator synthesis
├── round-2/                     ← (2nd round debate if needed)
├── final-plan.md                ← Phase 1 final plan
├── tasks/                       ← Phase 2 deliverables
│   ├── task-1/
│   │   └── output.md
│   ├── task-2/
│   │   └── output.md
│   └── ...
└── verification/                ← Phase 3 verification results
    ├── completeness-report.md   ← Completeness verification
    ├── accuracy-report.md       ← Accuracy verification
    ├── hallucination-report.md  ← Hallucination verification
    ├── integration-report.md    ← Integration verification
    └── final-verdict.md         ← Final verdict

---

🛡️ Harness Rules (Safety Mechanisms)

On Orche Start — Execute Immediately

1. Watchdog registration: Register task in watchdog.md (when using OpenClaw watchdog)
2. State initialization: Create orche-state.json
3. Report start to user

Phase Gate Harness (Mandatory Check Before Each Phase Transition)

text

G0 not passed → Phase 1 entry blocked. Re-confirmation requested from user.
G1 not passed → Phase 2 entry blocked. Plan re-debate forced.
G2 not passed → Phase 3 entry blocked. Missing tasks re-executed.
Verification fail → Auto regression to Phase 2 (retryCount < 3).

Sub-Agent Harness

Rules that must be included in every sub-agent spawn prompt:

text

[Harness Rules]
1. You must save results to the designated file path upon completion
2. Mark uncertain facts with "needs verification:" — no fabrication
3. Do not use non-existent APIs/libraries
4. On task failure, record failure reason in output file and terminate

These rules are injected into all sub-agents. This ensures:

• File-based communication is guaranteed (Rule 1)
• Hallucinations are caught early (Rules 2, 3)
• Failure causes are traceable (Rule 4)

Completion Harness

text

On Phase 3 completion, you must:
1. Set orche-state.json status → "completed"
2. Mark task [x] in watchdog.md (if using watchdog)
3. Send final report to user

---

🔍 Watchdog Integration

Orche integrates with OpenClaw's watchdog system to monitor the entire process.

What the Watchdog Does

Detection Item	Auto Response
Agent 15min+ no change (hang)	kill + re-spawn
Empty deliverable (file size 0)	Retry with reinforced prompt
All agents lost (deadlock)	Immediate user notification
Token budget 80% reached	User warning

Watchdog Registration Method

Register the task in watchdog.md at Orche start:

markdown

- [ ] orche <taskId> — Phase 0 (Query)
  session_keys: agent:main:subagent:xxxx
  state_file: workspace/orche-state.json
  evidence_paths:
    - workspace/orche-<taskId>/requirements.md
    - workspace/orche-<taskId>/final-plan.md
  done_when:
    - orche-state.json status == "completed"

Watchdog Design Principles

These principles reflect lessons learned in production.

• ✅ Read only watchdog.md → query sessions_list using session_keys
• ✅ Do not judge failure by "no session" alone — if deliverables/state files exist, assume progress/completion first
• ✅ Judge normal termination only when: no tasks + 0 active sessions
• ⚠️ If anything is active, report status and wait

No watchdog in your environment? Orche works without a watchdog. The watchdog is an additional safety net; phase gates and harness rules function as core safety mechanisms without it.

---

✅ Hallucination Checklist (14 Items)

Used by Phase 1's Critic and Phase 3's verification team.

Factual Verification

ID	Item	Verification Method
H-1	File path existence	Verify with stat() or ls
H-2	Command existence	Verify with which or command -v
H-3	URL validity	Verify with HTTP request (optional)
H-4	Code syntax validity	Grammar check with linter/parser
H-5	Numerical data cross-verification	Confirm with 2+ sources

Consistency

ID	Item	Verification Method
H-6	No self-contradiction	Detect conflicting statements within document
H-7	Plan-result alignment	1:1 mapping verification
H-8	Terminology consistency	Same term for same concept

Completeness

ID	Item	Verification Method
H-9	No remaining TODO/FIXME	grep -r "TODO|FIXME"
H-10	No placeholders	Detect [INSERT], TBD, ...
H-11	All deliverables exist	Cross-check deliverable list against requirements

Hallucination Patterns

ID	Item	Verification Method
H-12	No fictional library/API references	Verify existence on npm/pip/crates.io etc.
H-13	No unsourced statistics	Verify source citation or "needs verification:" label
H-14	No overconfident expressions	Detect "always", "never", "100%" type claims

Judgment Criteria

text

0~1 failures  → PASS ✅
2~3 failures  → WARNING ⚠️ (MED — user judgment)
4+ failures   → FAIL ❌ (HIGH — auto regression)

---

💰 Cost Management

Orche is a multi-agent system, so cost management is essential.

Budget Tiers

Tier	Ratio	Action
🟢 Normal	< 50%	Normal operation
🟡 Caution	50~80%	Limit concurrent agents, prefer sonnet
🔴 Warning	80~95%	Stop new spawns, complete in-progress tasks only
⛔ Exceeded	> 95%	Full stop, request user judgment

Cost Saving Tips

1. Actively leverage model selection:

   • Simple execution: haiku (lowest cost)
   • Planning/debate/general execution: sonnet (best value)
   • Complex reasoning: opus (only when needed)

2. Limit concurrent agents to 3 (default). 8 is the maximum; typically 3~4 is sufficient.

3. Set token budget according to task scale:

Task Scale	Recommended Budget (tokens)	Expected Agents	Estimated Cost (Claude Sonnet)
Small (simple research)	200,000	6~8	~$0.6
Medium (code refactoring)	500,000	10~15	~$1.5
Large (market analysis report)	1,000,000	15~20	~$3.0

Note: Costs above are estimates based on Claude Sonnet. Actual costs vary by model, prompt length, and retry count.

4. Use Phase 0 well. Clear scope definition during initial queries reduces unnecessary tasks and cuts costs.

Token Budget Adjustment

Adjust the budget by modifying tokenBudget.total in orche-state.json:

json

{
  "tokenBudget": {
    "total": 1000000,
    "used": 0
  }
}

---

🚨 Exception Handling Summary

Exception	Detection Method	Auto Response	User Notification
Agent hang	Watchdog 15min no change	kill + re-spawn	On 2 consecutive occurrences
Empty deliverable	File size 0	Retry with reinforced prompt	On 2 failures
API error (429/500)	HTTP status	Retry after 30s	On 3 failures
Hallucination detected	Critic/verification team	Regenerate affected portion	On HIGH severity
Total deadlock	All agents lost	—	Immediately
Context exceeded	Error message	Retry with reduced input	On failure
maxRetries exceeded	Counter check	—	Immediate judgment request
Token budget exceeded	used/total ratio	Model downgrade	At 80%

Error Logging

All errors are recorded in orche-state.json's errors array:

json

{
  "errors": [
    {
      "timestamp": "2026-03-26T10:30:00Z",
      "phase": 2,
      "taskId": "task-3",
      "type": "AGENT_HANG",
      "message": "Agent orche-exec-task-3 unresponsive for 15+ minutes",
      "action": "kill + re-spawn"
    }
  ]
}

---

🙋 User Intervention Points

Timing	Required?	Content
Phase 0 complete	✅ Required	Approve requirements
Phase 1 complete	Optional	Review plan (default: auto-proceed)
Phase 2 failure	✅ Required	Judgment after 2 retry failures
Phase 3 MED issue	✅ Required	Decide on re-execution
Phase 3 complete	Notification	Final results report

Auto-Proceed Mode

Auto-proceeding from Phase 1 to Phase 2 is the default behavior. If you want to review the plan directly, specify "I want approval at each step" during Phase 0 queries.

---

🛑 Abort Procedure

When the user requests task cancellation:

1. Terminate all active sub-agents (session kill)
2. Remove task from watchdog (if in use)
3. Set orche-state.json status → "aborted"
4. Report abort to user (including list of completed tasks)

text

🎼 /orche Abort Report

📋 Task: orche-1711432800
🛑 Abort reason: User request
✅ Completed tasks: 3/7
📁 Completed deliverables:
- workspace/orche-1711432800/tasks/task-1/output.md
- workspace/orche-1711432800/tasks/task-2/output.md
- workspace/orche-1711432800/tasks/task-3/output.md

---

🔄 Session Disconnection Recovery

Problem

If the main session disconnects mid-process, the orchestration stops.

Recovery Mechanism

1. orche-state.json is the recovery key.

   • Phase/task progress is recorded in the file, so after session restart, the state file can be read to resume.

2. State updated immediately on each task completion:

   • completedAt recorded in orche-state.json upon receiving each sub-agent result

3. Watchdog detects hangs: (when using watchdog)

   • If task exists in watchdog.md and main session is unresponsive for 20+ minutes in sessions_list, reports to user

4. Manual recovery:

   • User instructs orche to resume → reads orche-state.json → continues from incomplete tasks

Recovery Command Example

text

/orche resume

This command reads orche-state.json and resumes execution from the last completed phase/task.

---

🎮 Demo Scenarios

Scenario 1: Research Report Writing

User Input:

text

/orche "Write a 2025 Korean GenAI market analysis report. 
Must include market size, key players, investment trends, and regulatory landscape."

Phase 0 — Query (2~3 min):

text

🎼 Phase 0: Query

1. 📐 Scope: All GenAI? (LLM, image generation, code assistants, etc.)
2. 🎯 Depth: Individual company analysis? Or market overview level?
3. 📊 Data: Public data only? Paid research sources?
4. 📏 Length: How many pages (A4 equivalent)?
5. 🎨 Format: Markdown? PDF?
6. ⏰ Purpose: Internal report? Investment decision? Blog?

Phase 1 — Planning (5 min):

• Expert A (AI market analyst): Proposes report structure
• Expert B (Korean market specialist): Suggests Korea-specific data sources
• Critic: Points out "Market size estimates need cross-verification from 2+ sources"
• Moderator: Finalizes split into 5 tasks

Phase 2 — Execution (10~15 min):

Task	Agent	Deliverable
Market overview	agent-1	tasks/task-1/market-overview.md
Key players	agent-2	tasks/task-2/key-players.md
Investment trends	agent-3	tasks/task-3/investment-trends.md
Regulatory landscape	agent-4	tasks/task-4/regulatory.md
Consolidated report	agent-5	tasks/task-5/final-report.md

Phase 3 — Verification (5 min):

• H-5 check: Market size figures cross-verified → 2 sources confirmed ✅
• H-13 check: Investment amounts have cited sources → ✅
• H-11 check: All sections complete → ✅
• Final verdict: PASS ✅

---

Scenario 2: Code Refactoring

User Input:

text

/orche "Refactor our project's API layer. 
Currently Express monolithic — want to separate modules by domain and increase test coverage."

Phase 0 — Query (3 min):

text

🎼 Phase 0: Query

1. 📐 Scope: Entire API? Specific domains only?
2. 🔧 Tech stack: Express + TypeScript? ORM?
3. 📁 Current structure: Source directory path?
4. 🎯 Target structure: Have an example? (NestJS-style? Layered?)
5. ✅ Tests: Current coverage? Test framework?
6. ⚠️ Constraints: Backward compatibility? Zero-downtime deployment?

Phase 1 — Planning (7 min):

• Expert A (Node.js architect): Proposes module separation strategy
• Expert B (Testing specialist): Proposes testing strategy
• Critic: Points out "Need to analyze dependency graph first to ensure no circular dependencies"
• Advocate: Argues "Gradual migration is safer"
• Moderator: Finalizes 6 tasks in order: dependency analysis → module separation → test writing

Phase 2 — Execution (15~20 min):

Task	Agent	Deliverable
Dependency analysis	agent-1	tasks/task-1/dependency-graph.md
User module separation	agent-2	tasks/task-2/user-module/
Auth module separation	agent-3	tasks/task-3/auth-module/
Order module separation	agent-4	tasks/task-4/order-module/
Shared utilities cleanup	agent-5	tasks/task-5/shared/
Test writing	agent-6	tasks/task-6/tests/

Phase 3 — Verification (7 min):

• H-1 check: All file paths exist → ✅
• H-4 check: TypeScript compilation successful → ✅
• H-12 check: No non-existent npm package references → ✅
• H-7 check: Plan-result alignment → Task 5 gap found ⚠️
• 1st regression: Task 5 re-executed → completed
• Final verdict: PASS ✅

---

Scenario 3: Market Analysis

User Input:

text

/orche "Do a SaaS competitor analysis. 
Target Notion, Coda, Slite — compare features, pricing strategy, and market positioning."

Phase 0 — Query (2 min):

text

🎼 Phase 0: Query

1. 📐 Scope: Any additional competitors beyond these 3?
2. 🎯 Analysis depth: Feature-level detail? Strategic level?
3. 📊 Data: Public pricing only? Actual usage experience needed?
4. 📏 Deliverables: Comparison table + strategy report?
5. 🔍 Our product: Is there a reference product to compare against?

Phase 1 — Planning (5 min):

• Expert (SaaS market specialist): Proposes analysis framework (Porter's Five Forces + Feature Matrix)
• Critic: Warns "Pricing should be based on public pages only — no guessing enterprise pricing"
• Moderator: Finalizes 4 tasks

Phase 2 — Execution (10 min):

Task	Agent	Deliverable
Notion analysis	agent-1	tasks/task-1/notion-analysis.md
Coda analysis	agent-2	tasks/task-2/coda-analysis.md
Slite analysis	agent-3	tasks/task-3/slite-analysis.md
Comparative summary	agent-4	tasks/task-4/comparison-report.md

Phase 3 — Verification (5 min):

• H-5 check: Pricing data cross-verified → public page basis ✅
• H-13 check: MAU/ARR figures have cited sources → "needs verification:" properly labeled ✅
• H-14 check: "Notion will definitely win" type overconfidence → none found ✅
• Final verdict: PASS ✅

---

📈 Production Run Records

Below are summaries of actual projects completed with Orche.

Run #1: ClawHub Skill Packaging

Item	Details
Task	Repackage internal skills for ClawHub publishing
Duration	25 minutes
Agents deployed	12 (planning 4 + execution 5 + verification 3)
By phase	Query 3m → Plan 6m → Execute 10m → Verify 6m
Regressions	0 (passed first time)
Notable	Critic flagged "environment-dependent hardcoded cron IDs" → fixed during planning phase

Run #2: Multilingual Documentation Translation + Review

Item	Details
Task	Technical doc translation (Korean/English/Japanese) + terminology unification
Duration	35 minutes
Agents deployed	15 (planning 3 + execution 8 + verification 4)
By phase	Query 2m → Plan 5m → Execute 18m → Verify 10m
Regressions	1 (H-8 terminology consistency failure → glossary-based retranslation)
Notable	Terminology inconsistency auto-detected → Phase 2 regression → fixed and passed

Run #3: API Design + Implementation + Testing

Item	Details
Task	REST API design → implementation → test code → documentation
Duration	42 minutes
Agents deployed	18 (planning 5 + execution 8 + verification 5)
By phase	Query 4m → Plan 8m → Execute 20m → Verify 10m
Regressions	1 (H-12 non-existent middleware reference → affected task re-executed)
Notable	Critic debate on "implement auth middleware vs use library" → Moderator decided on verified library

Run Statistics Summary

text

Total completions: 3
Average duration: 34 minutes
Average agents: 15
First-pass rate: 33% (1/3)
Resolved via auto-regression: 100% (2/2 regressions resolved within 3 retries)
Manual escalations: 0

---

🔗 hallucination-guard Integration

If the hallucination-guard skill is installed, Orche can leverage it during the verification phase for more precise hallucination detection.

Integration Method

1. Install hallucination-guard skill:

   bash

   # Install from ClawHub (needs verification: install command may vary by environment)
   mkdir -p ~/.agents/skills/hallucination-guard
   # Place SKILL.md in that directory
   

2. Auto-utilized in Phase 3 verification:

   • When hallucination-guard is installed, Phase 3's hallucination verifier additionally applies that skill's checklist.
   • Particularly improves detection accuracy for "non-existent APIs/libraries" in code-related tasks.

3. Integration Architecture:

text

Phase 3: Verification
├── Completeness Verifier
├── Accuracy Verifier
├── Hallucination Verifier ──── Uses hallucination-guard skill
│   ├── Orche's built-in 14-item check
│   └── + hallucination-guard additional checks (when installed)
├── Integration Verifier
└── Final Reviewer

4. When hallucination-guard is not installed:
   • Orche functions normally with its built-in 14-item checklist alone.
   • hallucination-guard is an optional enhancement tool.

Recommended Combinations

Task Type	Orche Alone	+ hallucination-guard
Research report	✅ Sufficient	✅✅ Enhanced numerical verification
Code refactoring	✅ Sufficient	✅✅✅ API existence check essential
Market analysis	✅ Sufficient	✅✅ Enhanced statistics source verification
Document translation	✅ Sufficient	✅ Minimal difference

---

⚙️ Advanced Configuration

Adjusting Debate Round Count

Default is up to 3 rounds. Complex domains may require more rounds. Specify "I want thorough plan debate" during Phase 0 to adjust the round count.

Verification Intensity Adjustment

Intensity	Verifier Count	Hallucination Check	Recommended For
light	2	Essential 5 items only	Simple tasks, time savings
standard (default)	3~4	All 14 items	General tasks
thorough	5	14 items + additional cross-verification	High-risk tasks

You can specify verification intensity during Phase 0:

text

/orche "Refactor the API. Use thorough verification."

Agent Model Configuration

Specify different models per phase based on task characteristics:

json

{
  "modelConfig": {
    "planning": "anthropic/claude-sonnet-4-6",
    "execution": "anthropic/claude-sonnet-4-6",
    "verification": "anthropic/claude-sonnet-4-6",
    "complex": "anthropic/claude-opus-4-6"
  }
}

Concurrent Agent Limit

Default is 3 (maximum 8). Adjust in orche-state.json:

json

{
  "concurrency": {
    "default": 3,
    "max": 8
  }
}

---

❓ FAQ

Q: Can Phase 0 be skipped?

A: No. Phase 0 is mandatory. Proceeding with ambiguous requirements causes direction change costs in Phase 2 to grow exponentially. Clear answers in Phase 0 actually reduce total time.

Q: How much does it cost?

A: Depends on task scale. Small tasks (research report) use approximately 200K tokens (Claude Sonnet $0.6), large tasks (API design + implementation) approximately 1M tokens ($3.0). See the cost management section for budget settings.

Q: What happens if verification keeps failing?

A: Auto-regression up to 3 times. If all 3 fail, escalated to user for direct judgment. Also escalated if the same reason fails 2 consecutive times.

Q: Can it work without a watchdog?

A: Yes. The watchdog is an additional safety net; phase gates and harness rules are the core safety mechanisms. Orche functions normally without a watchdog.

Q: Can it be used alongside other orchestration skills?

A: Orche operates independently. However, handling simple parallel tasks within Phase 2 using dispatching-parallel-agents patterns is naturally compatible.

Q: Is progress lost on session disconnect?

A: No. All state is recorded in orche-state.json and the file system. Use /orche resume to continue from the last progress point.

Q: What if the Critic over-opposes in the debate panel?

A: The Moderator builds consensus. Debate runs up to 3 rounds; if consensus isn't reached, the Moderator has final decision authority.

Q: Do I have to use the Opus model?

A: No. Sonnet alone can handle most tasks. Opus is used selectively only for domain expert roles requiring complex reasoning. Sonnet is recommended as default for cost savings.

---

📋 Changelog

v1.0.0 (2026-03-26)

• 🎉 Initial ClawHub release
• Full Phase 0~3 pipeline implementation
• 14-item hallucination checklist
• Phase gate system
• Critic debate protocol
• Auto regression mechanism (up to 3 times)
• Watchdog integration (optional)
• 4-tier cost management
• Session disconnection recovery (state file based)
• 3 demo scenarios + production run records

---

Important Notes

1. Sub-agents are created via sessions_spawn (mode: "run")
2. Do not pass full session context to agents — pass only the minimum necessary information
3. File-based communication — no direct inter-agent communication
4. Concurrent agent limit — recommended 3, maximum 8
5. Phase 0 cannot be skipped
6. Be cost-conscious — don't deploy 7 agents for a simple task
7. Harness rules are injected into all sub-agents — this is the key to hallucination prevention

---

Made with 🎼 by reikys — Battle-tested in production environments.