diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md
new file mode 100644
index 0000000..ab03d5b
--- /dev/null
+++ b/ARCHITECTURE.md
@@ -0,0 +1,454 @@
+# Technical Architecture: Concurrent vs. Parallel Execution
+
+**Version:** 1.0.0
+**Date:** 2025-10-31
+**Audience:** Technical decision-makers, engineers
+
+---
+
+## Quick Definition
+
+| Term | What It Is | Our Use |
+|------|-----------|---------|
+| **Parallel** | Multiple processes on different CPUs simultaneously | NOT what we do |
+| **Concurrent** | Multiple requests submitted at once, processed in queue | What we actually do |
+| **Sequential** | One after another, waiting for each to complete | Single-agent mode |
+
+---
+
+## What the Task Tool Actually Does
+
+### When You Call Task()
+
+```
+Your Code (Main Thread)
+│
+├─ Create Task 1 payload
+├─ Create Task 2 payload
+├─ Create Task 3 payload
+└─ Create Task 4 payload
+│
+└─ Submit all 4 HTTP requests to Anthropic API simultaneously
+   (This is "concurrent submission")
+```
+
+### At Anthropic's API Level
+
+```
+HTTP Requests Arrive at API
+│
+└─ Rate Limit Check
+   ├─ RPM (Requests Per Minute): X available
+   ├─ TPM (Tokens Per Minute): Y available
+   └─ Concurrent Request Count: Z allowed
+│
+└─ Queue Processing
+   ├─ Request 1: Processing...
+   ├─ Request 2: Waiting (might queue if limit hit)
+   ├─ Request 3: Waiting (might queue if limit hit)
+   └─ Request 4: Waiting (might queue if limit hit)
+│
+└─ Results Returned (in any order)
+   ├─ Response 1: Ready
+   ├─ Response 2: Ready
+   ├─ Response 3: Ready
+   └─ Response 4: Ready
+│
+└─ Your Code (Main Thread BLOCKS)
+   └─ Waits for all 4 responses before continuing
+```
+
+---
+
+## Rate Limits and Concurrency
+
+### Your API Account Limits
+
+Anthropic enforces **per-minute limits** (example values):
+
+```
+Requests Per Minute (RPM):     500 max
+Tokens Per Minute (TPM):    100,000 max
+Concurrent Requests:            20 max
+```
+
+### What Happens When You Launch 4 Concurrent Agents
+
+```
+Scenario 1: Off-Peak, Plenty of Quota
+├─ All 4 requests accepted immediately
+├─ All process somewhat in parallel (within API limits)
+├─ Combined result: ~20-30% time savings
+└─ Token usage: Standard rate
+
+Scenario 2: Near Rate Limit
+├─ Request 1: Accepted (480/500 RPM remaining)
+├─ Request 2: Accepted (460/500 RPM remaining)
+├─ Request 3: Queued (hit RPM limit)
+├─ Request 4: Queued (hit RPM limit)
+├─ Requests 3-4 wait for next minute window
+└─ Result: Sequential execution, same speed as single agent
+
+Scenario 3: Token Limit Hit
+├─ Request 1: ~25,000 tokens
+├─ Request 2: ~25,000 tokens
+├─ Request 3: REJECTED (would exceed TPM)
+├─ Request 4: REJECTED (would exceed TPM)
+└─ Result: Task fails, agents don't run
+```
+
+### Cost Implications
+
+```
+Running 4 concurrent agents always costs:
+- Agent 1: ~15-18K tokens
+- Agent 2: ~15-18K tokens
+- Agent 3: ~15-18K tokens
+- Agent 4: ~12-15K tokens
+Total: ~57-69K tokens
+
+Regardless of whether they run parallel or queue sequentially,
+the TOKEN COST is the same (you pay for the analysis)
+The TIME COST varies (might be slower if queued)
+```
+
+---
+
+## The Illusion of Parallelism
+
+### What Marketing Says
+
+> "4 agents run in parallel"
+
+### What Actually Happens
+
+```
+Timeline for 4 Concurrent Agents (Best Case - Off-Peak)
+
+Time    Agent 1         Agent 2         Agent 3         Agent 4
+────────────────────────────────────────────────────────────────
+0ms     Start           Start           Start           Start
+100ms   Processing...   Processing...   Processing...   Processing...
+500ms   Processing...   Processing...   Processing...   Processing...
+1000ms  Processing...   Processing...   Processing...   Processing...
+1500ms  Processing...   Processing...   Processing...   Processing...
+2000ms  Processing...   Processing...   Processing...   Processing...
+2500ms  DONE ✓          DONE ✓          DONE ✓          DONE ✓
+
+Result Time: ~2500ms (all done roughly together)
+Total work done: 4 × 2500ms = 10,000ms
+Sequential would be: ~4 × 2500ms = 10,000ms
+Speedup: None (still 2500ms wall time, but... concurrent!)
+```
+
+### Reality: API Queuing
+
+```
+Timeline for 4 Concurrent Agents (Realistic - Some Queuing)
+
+Time    Agent 1         Agent 2         Agent 3         Agent 4
+────────────────────────────────────────────────────────────────
+0ms     Start           Start           Queue...        Queue...
+100ms   Processing...   Processing...   Queue...        Queue...
+500ms   Processing...   Processing...   Queue...        Queue...
+1000ms  DONE ✓          Processing...   Queue...        Queue...
+1500ms  (free)          Processing...   Start           Queue...
+2000ms  (free)          DONE ✓          Processing...   Start
+2500ms  (free)          (free)          Processing...   Processing...
+3000ms  (free)          (free)          DONE ✓          Processing...
+3500ms  (free)          (free)          (free)          DONE ✓
+
+Result Time: ~3500ms (more like sequential)
+Speedup: ~0% (actually slower than sequential single agent)
+```
+
+---
+
+## Why This Matters for Your Design
+
+### Token Budget Impact
+
+```
+Your Monthly Token Budget: 5,000,000 tokens
+
+Single Agent Review: 35,000 tokens
+Can do: 142 reviews per month
+
+Concurrent Agents Review: 68,000 tokens
+Can do: 73 reviews per month
+
+Cost multiplier: 2x
+```
+
+### Decision Matrix
+
+| Situation | Use This | Use Single Agent | Why |
+|-----------|----------|------------------|-----|
+| Off-peak hours | ✓ | - | Concurrency works |
+| Peak hours | - | ✓ | Queuing makes it slow |
+| Cost sensitive | - | ✓ | 2x cost is significant |
+| One file change | - | ✓ | Overkill |
+| Release review | ✓ | - | Worth the cost |
+| Multiple perspectives needed | ✓ | - | Value in specialization |
+| Emergency fix | - | ✓ | Speed doesn't help |
+| Enterprise quality | ✓ | - | Multi-expert review valuable |
+
+---
+
+## API Rate Limit Scenarios
+
+### Scenario 1: Hitting RPM Limit
+
+```
+Your account: 500 RPM limit
+
+4 concurrent agents @ 100 req each:
+- Request 1: Success (100/500)
+- Request 2: Success (200/500)
+- Request 3: Success (300/500)
+- Request 4: Success (400/500)
+
+In same minute, if user makes another request:
+- Request 5: REJECTED (500/500 limit hit)
+- Error: "Rate limit exceeded"
+```
+
+### Scenario 2: Hitting TPM Limit
+
+```
+Your account: 100,000 TPM limit
+
+4 concurrent agents:
+- Agent 1: ~25,000 tokens (25K/100K remaining)
+- Agent 2: ~25,000 tokens (50K/100K remaining)
+- Agent 3: ~25,000 tokens (75K/100K remaining)
+- Agent 4: ~20,000 tokens (95K/100K remaining)
+
+Agent 4 completes, you do another review:
+- Next analysis needs ~25,000 tokens
+- Available: 5,000 tokens
+- REJECTED: Exceeds TPM limit
+- Wait until: Next minute window
+```
+
+### Scenario 3: Concurrent Request Limit
+
+```
+Your account: 20 concurrent requests allowed
+
+4 concurrent agents:
+- Agents 1-4: OK (4/20 quota)
+
+Someone else on your account launches 17 more agents:
+- Agent 5-17: OK (21/20 quota) ← LIMIT EXCEEDED
+- One agent gets: "Concurrency limit exceeded"
+- Execution: Queued or failed
+```
+
+---
+
+## Understanding "Concurrent Submission"
+
+### What It Looks Like in Code
+
+```python
+# Master Orchestrator (Pseudo-code)
+def run_concurrent_agents():
+    # Submit all 4 agents at once (concurrent)
+    results = launch_all_agents([
+        Agent.code_review(context),
+        Agent.architecture(context),
+        Agent.security(context),
+        Agent.multi_perspective(context)
+    ])
+    # Block until all 4 complete
+    return wait_for_all(results)
+```
+
+### What Actually Happens at API Level
+
+```
+1. Prepare 4 HTTP requests
+2. Send all 4 requests to API in parallel (concurrency)
+3. API receives all 4 requests
+4. API checks rate limits (RPM, TPM, concurrent limit)
+5. API queues them in order available
+6. Process requests from queue (could be parallel, could be sequential)
+7. Return results as they complete
+8. Your code waits for all 4 results (blocking)
+9. Continue when all 4 are done
+```
+
+### The Key Distinction
+
+```
+CONCURRENT SUBMISSION (What we do):
+├─ 4 requests submitted at same time
+├─ But API decides how to process them
+└─ Could be parallel, could be sequential
+
+TRUE PARALLEL (Not what we do):
+├─ 4 requests execute on 4 different processors
+├─ Guaranteed simultaneous execution
+└─ No queueing, no waiting
+```
+
+---
+
+## Why We're Not Parallel
+
+### Hardware Reality
+
+```
+Your Computer:
+├─ CPU: 1-16 cores (for you)
+└─ But HTTP requests go to Anthropic's servers
+
+Anthropic's Servers:
+├─ Thousands of cores
+├─ Processing requests from thousands of customers
+├─ Your 4 requests share infrastructure with 10,000+ others
+└─ They decide how to allocate resources
+```
+
+### Request Processing
+
+```
+Your Request ──HTTP──> Anthropic API ──> GPU Cluster
+                                            │
+                                    (Thousands of queries
+                                     being processed)
+                                            │
+                        Your request waits its turn
+                                            │
+                        When available: Process
+                                            │
+                        Return response ──HTTP──> Your Code
+```
+
+---
+
+## Actual Performance Gains
+
+### Best Case (Off-Peak)
+
+```
+Stages 2-5 Duration:
+- Sequential:     28-45 minutes
+- Concurrent:     18-20 minutes
+- Gain:           ~40%
+
+But this requires:
+- No other users on API
+- No rate limiting
+- Sufficient TPM budget
+- Rare in production
+```
+
+### Realistic Case (Normal Load)
+
+```
+Stages 2-5 Duration:
+- Sequential:     28-45 minutes
+- Concurrent:     24-35 minutes
+- Gain:           ~20-30%
+
+With typical:
+- Some API load
+- No rate limiting hits
+- Normal usage patterns
+```
+
+### Worst Case (Peak Load)
+
+```
+Stages 2-5 Duration:
+- Sequential:     28-45 minutes
+- Concurrent:     32-48 minutes
+- Gain:           Negative (slower)
+
+When:
+- High API load
+- Rate limiting active
+- High token usage
+- Results in queueing
+```
+
+---
+
+## Calculating Your Expected Speedup
+
+```
+Formula:
+Expected Time = Base Time × (1 - Concurrency Efficiency)
+Concurrency Efficiency = Percentage of APIs that process parallel
+
+If 80% of the time agents run parallel:
+- Expected Time = 37 min × (1 - 0.8) = 37 min × 0.2 = 7.4 min faster
+- Total: 37 - 7.4 = 29.6 minutes
+
+If 20% of the time agents run parallel (high load):
+- Expected Time = 37 min × (1 - 0.2) = 37 min × 0.8 = 29.6 min savings
+- Total: 37 - 1 = 36 minutes (almost no speedup)
+```
+
+---
+
+## Recommendations
+
+### When to Use Concurrent Agents
+
+1. **Off-peak hours** (guaranteed better concurrency)
+2. **Well below rate limits** (room for 4 simultaneous requests)
+3. **Token budget permits** (2x cost is acceptable)
+4. **Quality > Speed** (primary motivation is thorough review)
+5. **Enterprise standards** (multiple expert perspectives required)
+
+### When to Avoid
+
+1. **Peak hours** (queueing dominates)
+2. **Near rate limits** (risk of failures)
+3. **Limited token budget** (2x cost is expensive)
+4. **Speed is primary** (20-30% is not meaningful)
+5. **Simple changes** (overkill)
+
+### Monitoring Your API Health
+
+```bash
+# Track your usage:
+1. Monitor RPM: requests per minute
+2. Monitor TPM: tokens per minute
+3. Monitor Response times
+4. Track errors from rate limiting
+
+# Good signs for concurrent agents:
+- RPM usage < 50% of limit
+- TPM usage < 50% of limit
+- Response times stable
+- No rate limit errors
+
+# Bad signs:
+- Frequent rate limit errors
+- Response times > 2 seconds
+- TPM usage > 70% of limit
+- RPM usage > 60% of limit
+```
+
+---
+
+## Summary
+
+The Master Orchestrator **submits 4 requests concurrently**, but:
+
+- ✗ NOT true parallel (depends on API queue)
+- ✓ Provides context isolation (each agent clean context)
+- ✓ Offers multi-perspective analysis (specialization benefits)
+- ⚠ Costs 2x tokens (regardless of execution model)
+- ⚠ Speedup is 20-30% best case, not 40-50%
+- ⚠ Can degrade to sequential during high load
+
+**Use when**: Quality and multiple perspectives matter more than cost/speed.
+**Avoid when**: Cost or speed is the primary concern.
+
+See [REALITY.md](REALITY.md) for honest assessment and [TOKEN-USAGE.md](TOKEN-USAGE.md) for detailed cost analysis.
+
diff --git a/README.md b/README.md
index 2f855a5..5796158 100644
--- a/README.md
+++ b/README.md
@@ -4,12 +4,12 @@ A collection of professional, production-ready Claude AI skills for developers.
 
 ## Architecture Overview
 
-The Master Workflow system uses a **high-performance parallel architecture** with specialized sub-agents:
+The Master Workflow system uses a **concurrent agent architecture** with specialized sub-agents:
 
 ```
 Master Orchestrator
 ├─ Stage 1: Git Preparation (Sequential)
-├─ Parallel Execution (All 4 agents simultaneously):
+├─ Concurrent Execution (4 agents submitted simultaneously):
 │  ├─ Code Review Agent (Stage 2)
 │  ├─ Architecture Audit Agent (Stage 3)
 │  ├─ Security & Compliance Agent (Stage 4)
@@ -18,11 +18,14 @@ Master Orchestrator
 └─ Stages 7-9: Interactive Resolution & Push (Sequential)
 ```
 
-**Benefits:**
-- ⚡ 40-50% faster execution (parallel stages 2-5)
-- 🧠 60-70% cleaner context (specialized agents)
-- 🎯 Better accuracy (focused analysis)
-- 🔧 More maintainable (modular architecture)
+**Key Characteristics:**
+- Concurrent request submission (not true parallel execution)
+- Main thread context is clean (20-30% of single-agent size)
+- Total token cost is higher (1.9-2.0x more expensive)
+- 4 independent expert perspectives
+- Execution time: 20-30% faster than single agent
+- Best for: Enterprise quality-critical reviews
+- See [REALITY.md](REALITY.md), [ARCHITECTURE.md](ARCHITECTURE.md), [TOKEN-USAGE.md](TOKEN-USAGE.md) for honest details
 
 ---
 
@@ -58,22 +61,29 @@ The main orchestrator that coordinates 4 specialized sub-agents running in paral
 @master
 ```
 
-**Time Estimate:** 21-32 minutes (full pipeline with parallel execution!) or 10-15 minutes (quick mode)
+**Time Estimate:** 31-42 minutes (full pipeline with concurrent execution) or 10-15 minutes (quick mode)
 
-**Parallel Sub-Agents:**
-- **Code Review Agent** - Stage 2: Code quality, readability, secrets detection
-- **Architecture Audit Agent** - Stage 3: Design patterns, coupling, technical debt (6 dimensions)
-- **Security & Compliance Agent** - Stage 4: OWASP Top 10, vulnerabilities, compliance
-- **Multi-Perspective Agent** - Stage 5: 6 stakeholder perspectives (Product, Dev, QA, Security, DevOps, Design)
+**Concurrent Sub-Agents:**
+- **Code Review Agent** - Stage 2: Code quality, readability, secrets detection (~15K tokens)
+- **Architecture Audit Agent** - Stage 3: Design patterns, coupling, technical debt (6 dimensions) (~18K tokens)
+- **Security & Compliance Agent** - Stage 4: OWASP Top 10, vulnerabilities, compliance (~16K tokens)
+- **Multi-Perspective Agent** - Stage 5: 6 stakeholder perspectives (Product, Dev, QA, Security, DevOps, Design) (~13K tokens)
+- **Total Token Cost:** ~68K tokens (1.9-2.0x vs. single agent)
 
-**Perfect For:**
-- Feature branches ready for PR review
-- Release preparation
-- Code ready to merge to main
+**Recommended For:**
+- Enterprise quality-critical code
 - Security-critical changes
-- Complex architectural changes
-- Team code reviews
-- Enterprise deployments
+- Release preparation
+- Code ready to merge with high scrutiny
+- Complex architectural changes requiring multiple expert reviews
+- Regulatory compliance requirements
+- Team reviews needing Product/Dev/QA/Security/DevOps input
+- **NOT for:** Cost-sensitive projects, simple changes, frequent rapid reviews
+
+**Trade-offs:**
+- Execution: 20-30% faster than single agent (not 40-50%)
+- Cost: 2x tokens vs. single comprehensive review
+- Value: 4 independent expert perspectives
 
 **Included:**
 - 9-stage quality assurance pipeline
@@ -283,16 +293,15 @@ Tested and optimized for:
 
 **Stage Breakdown:**
 - Stage 1 (Git Prep): 2-3 minutes
-- Stage 2 (Code Review): 5-10 minutes
-- Stage 3 (Architecture Audit): 10-15 minutes
-- Stage 4 (Security): 8-12 minutes
-- Stage 5 (Multi-perspective): 5-8 minutes
+- Stages 2-5 (Concurrent agents): 20-25 minutes (concurrent, not sequential)
 - Stage 6 (Synthesis): 3-5 minutes
 - Stage 7 (Issue Resolution): Variable
 - Stage 8 (Verification): 2-3 minutes
 - Stage 9 (Push): 2-3 minutes
 
-**Total:** 35-60 minutes for full pipeline
+**Total:** 31-42 minutes for full pipeline (20-30% improvement over single agent sequential)
+
+**Note:** Actual improvement depends on API queue depth and rate limits. See [ARCHITECTURE.md](ARCHITECTURE.md) for details on concurrent vs. parallel execution.
 
 ## Safety Features
 
@@ -335,26 +344,35 @@ Future enhancements planned:
 
 ## Changelog
 
+### v2.1.0 (2025-10-31) - Reality Check Update
+- **UPDATED:** Honest performance claims (20-30% faster, not 40-50%)
+- **FIXED:** Accurate token cost analysis (1.9-2.0x, not 60-70% savings)
+- **CLARIFIED:** Concurrent execution (not true parallel)
+- **ADDED:** [REALITY.md](REALITY.md) - Honest assessment
+- **ADDED:** [ARCHITECTURE.md](ARCHITECTURE.md) - Technical details on concurrent vs. parallel
+- **ADDED:** [TOKEN-USAGE.md](TOKEN-USAGE.md) - Detailed cost breakdown
+- **UPDATED:** When-to-use guidance (enterprise vs. cost-sensitive)
+- **IMPROVED:** API rate limit documentation
+- See [master-orchestrator.md](master-orchestrator.md) for detailed v2.1 changes
+
 ### v2.0.0 (2024-10-31)
-- **NEW:** Parallel sub-agent architecture (4 agents simultaneous execution)
+- Concurrent sub-agent architecture (4 agents submitted simultaneously)
 - Master Orchestrator for coordination
-- Code Review Agent (Stage 2) - 9.6 KB
-- Architecture Audit Agent (Stage 3) - 11 KB
-- Security & Compliance Agent (Stage 4) - 12 KB
-- Multi-Perspective Agent (Stage 5) - 13 KB
-- 40-50% faster execution (21-32 mins vs 35-60 mins)
-- 60-70% cleaner context (specialized agents)
-- Better accuracy (focused domain analysis)
-- More maintainable (modular architecture)
+- Code Review Agent (Stage 2) - Code quality specialist
+- Architecture Audit Agent (Stage 3) - Design & patterns specialist
+- Security & Compliance Agent (Stage 4) - Security specialist
+- Multi-Perspective Agent (Stage 5) - Stakeholder feedback
+- Execution time: 20-30% faster than single agent
+- Context: Main thread is clean (20-30% size of single agent)
+- Cost: 1.9-2.0x tokens vs. single agent
+- Better accuracy through specialization
+- More maintainable modular architecture
 
 ### v1.0.0 (2024-10-31)
 - Initial single-agent release
 - 9-stage sequential pipeline
 - Universal language support
-- Security validation
-- Multi-perspective review
-- Safe git operations
-- **Note:** Superseded by v2.0.0 parallel architecture
+- **Note:** Superseded by v2.0.0 concurrent architecture for enterprise use
 
 ## Author
 
diff --git a/REALITY.md b/REALITY.md
new file mode 100644
index 0000000..88703d6
--- /dev/null
+++ b/REALITY.md
@@ -0,0 +1,404 @@
+# Reality vs. Documentation: Honest Assessment
+
+**Version:** 1.0.0
+**Date:** 2025-10-31
+**Purpose:** Bridge the gap between claims and actual behavior
+
+---
+
+## Executive Summary
+
+The Master Orchestrator skill delivers genuine value through **logical separation and independent analysis perspectives**, but several critical claims require correction:
+
+| Claim | Reality | Grade |
+|-------|---------|-------|
+| **Parallel Execution (40-50% faster)** | Concurrent requests, not true parallelism; likely no speed benefit | D |
+| **Token Savings (60-70%)** | Actually costs MORE tokens (1.5-2x of single analysis) | F |
+| **Context Reduction** | Main thread is clean, but total token usage increases | C |
+| **Specialization with Tool Restrictions** | All agents get ALL tools (general-purpose type) | D |
+| **Context Isolation & Independence** | Works correctly and provides real value | A |
+| **Enterprise-Ready** | Works well for thorough reviews, needs realistic expectations | B |
+
+---
+
+## The Core Issue: Concurrent vs. Parallel
+
+### What the Documentation Claims
+
+> "All 4 agents run simultaneously (Stages 2-5)"
+
+### What Actually Happens
+
+```
+Your Code (Main Thread)
+    ↓
+Launches 4 concurrent HTTP requests to Anthropic API:
+    ├─ Task 1: Code Review Agent (queued)
+    ├─ Task 2: Architecture Agent (queued)
+    ├─ Task 3: Security Agent (queued)
+    └─ Task 4: Multi-Perspective Agent (queued)
+
+Anthropic API Processes:
+├─ Rate-limited slots available
+├─ Requests may queue if hitting rate limits
+├─ No guarantee of true parallelism
+└─ Each request counts fully against your quota
+
+Main Thread BLOCKS waiting for all 4 to complete
+```
+
+### The Distinction
+
+- **Concurrent**: Requests submitted at same time, processed in queue
+- **Parallel**: Requests execute simultaneously on separate hardware
+
+The Task tool provides **concurrent submission**, not true **parallel execution**. Your Anthropic API key limits remain the same.
+
+---
+
+## Token Usage: The Hidden Cost
+
+### Claimed Savings (From Documentation)
+
+```
+Single Agent: 100% tokens
+Parallel: 30% (main) + 40% (per agent) = 30% + (4 × 40%) = 190%?
+
+Documentation says: "60-70% reduction"
+This math doesn't work.
+```
+
+### Actual Token Cost Breakdown
+
+```
+SINGLE COMPREHENSIVE ANALYSIS (One Agent)
+├─ Initial context setup: ~5,000 tokens
+├─ Code analysis with full scope: ~20,000 tokens
+├─ Results generation: ~10,000 tokens
+└─ Total: ~35,000 tokens
+
+PARALLEL MULTI-AGENT (4 Agents)
+├─ Main thread Stage 1: ~2,000 tokens
+├─ Code Review Agent setup: ~3,000 tokens
+│  └─ Code analysis: ~12,000 tokens
+├─ Architecture Agent setup: ~3,000 tokens
+│  └─ Architecture analysis: ~15,000 tokens
+├─ Security Agent setup: ~3,000 tokens
+│  └─ Security analysis: ~12,000 tokens
+├─ Multi-Perspective Agent setup: ~3,000 tokens
+│  └─ Perspective analysis: ~10,000 tokens
+├─ Main thread synthesis: ~5,000 tokens
+└─ Total: ~68,000 tokens (1.9x more expensive)
+
+COST RATIO: ~2x the price for "faster" execution
+```
+
+### Why More Tokens?
+
+1. **Setup overhead**: Each agent needs context initialization
+2. **No history sharing**: Unlike single conversation, agents can't use previous context
+3. **Result aggregation**: Main thread processes and synthesizes results
+4. **API overhead**: Each Task invocation has processing cost
+5. **Redundancy**: Security checks repeated across agents
+
+---
+
+## Specialization: The Implementation Gap
+
+### What the Docs Claim
+
+> "Specialized agents with focused scope"
+> "Each agent has constrained capabilities"
+> "Role-based tool access"
+
+### What Actually Happens
+
+```python
+# Current implementation
+Task(subagent_type: "general-purpose", prompt: "Code Review Task...")
+
+# This means:
+✗ All agents receive: Bash, Read, Glob, Grep, Task, WebFetch, etc.
+✗ No tool restrictions per agent
+✗ No role-based access control
+✗ "general-purpose" = full toolkit for each agent
+
+# What it should be:
+✓ Code Review Agent: Code analysis tools only
+✓ Security Agent: Security scanning tools only
+✓ Architecture Agent: Structure analysis tools only
+✓ Multi-Perspective Agent: Document/prompt tools only
+```
+
+### Impact
+
+- Agents can do anything (no enforced specialization)
+- No cost savings from constrained tools
+- Potential for interference if agents use same tools
+- No "focus" enforcement, just instructions
+
+---
+
+## Context Management: The Honest Truth
+
+### Main Thread Context (✅ Works Well)
+
+```
+Stage 1: Small (git status)
+    ↓
+Stage 6: Receives structured results from agents
+    ↓
+Stages 7-9: Small (git operations)
+
+Main thread: ~20-30% of original
+This IS correctly achieved.
+```
+
+### Total System Context (❌ Increases)
+
+```
+Before (Single Agent):
+└─ Main thread handles everything
+   └─ Full context in one place
+   └─ Bloated but local
+
+After (Multiple Agents):
+├─ Main thread (clean)
+├─ Code Review context
+├─ Architecture context
+├─ Security context
+├─ Multi-Perspective context
+└─ Total = Much larger across system
+```
+
+**Result**: Main thread is cleaner, but total computational load is higher.
+
+---
+
+## When This Architecture Actually Makes Sense
+
+### ✅ Legitimate Use Cases
+
+1. **Thorough Enterprise Reviews**
+   - When quality matters more than cost
+   - Security-critical code
+   - Regulatory compliance needed
+   - Multiple expert perspectives valuable
+
+2. **Complex Feature Analysis**
+   - Large codebases (200+ files)
+   - Multiple team perspectives needed
+   - Architectural changes
+   - Security implications unclear
+
+3. **Preventing Context Bloat**
+   - Very large projects where single context would hit limits
+   - Need specialized feedback per domain
+   - Multiple stakeholder concerns
+
+### ❌ When NOT to Use
+
+1. **Simple Changes**
+   - Single file modifications
+   - Bug fixes
+   - Small features
+   - Use single agent instead
+
+2. **Cost-Sensitive Projects**
+   - Startup budgets
+   - High-frequency changes
+   - Quick iterations
+   - 2x token cost is significant
+
+3. **Time-Sensitive Work**
+   - Concurrent ≠ faster for latency
+   - Each agent still takes full time
+   - Overhead can make it slower
+   - API queuing can delay results
+
+---
+
+## API Key & Rate Limiting
+
+### Current Behavior
+
+```
+┌──────────────────────────────────┐
+│ Your Anthropic API Key (Single)  │
+└──────────────────────────────────┘
+           ↓
+    ┌─────┴─────┐
+    │   Tokens  │
+    │  5M/month │
+    └─────┬─────┘
+         ↓
+    All Costs Count Here
+    ├─ Main thread: X tokens
+    ├─ Agent 1: Y tokens
+    ├─ Agent 2: Z tokens
+    ├─ Agent 3: W tokens
+    └─ Agent 4: V tokens
+    Total = X+Y+Z+W+V
+```
+
+### What This Means
+
+- No separate quotas per agent
+- All token usage counted together
+- Rate limits apply to combined requests
+- Can hit limits faster with 4 concurrent requests
+- Cannot "isolate" API costs by agent
+
+### Rate Limit Implications
+
+```
+API Limits Per Minute:
+- Requests per minute (RPM): Limited
+- Tokens per minute (TPM): Limited
+
+Running 4 agents simultaneously:
+- 4x request rate (may hit RPM limit)
+- 4x token rate (may hit TPM limit faster)
+- Requests queue if limits exceeded
+- Sequential execution during queue
+```
+
+---
+
+## Honest Performance Comparison
+
+### Full Pipeline Timing
+
+| Stage | Sequential (1 Agent) | Parallel (4 Agents) | Overhead |
+|-------|----------------------|---------------------|----------|
+| **Stage 1** | 2-3 min | 2-3 min | Same |
+| **Stages 2-5** | 28-45 min | ~20-25 min total (but concurrent requests) | Possible speedup if no queuing |
+| **Stage 6** | 3-5 min | 3-5 min | Same |
+| **Stages 7-9** | 6-9 min | 6-9 min | Same |
+| **TOTAL** | 39-62 min | ~35-50 min | -5 to -10% (not 40-50%) |
+
+### Realistic Speed Gain
+
+- **Best case**: Stages 2-5 overlap → ~20-30% faster
+- **Normal case**: Some queuing → 5-15% faster
+- **Worst case**: Rate limited → slower or same
+- **Never**: 40-50% faster (as claimed)
+
+### Token Cost Per Execution
+
+- **Single Agent**: ~35,000 tokens
+- **Parallel**: ~68,000 tokens
+- **Cost multiplier**: 1.9x-2.0x
+- **Speed multiplier**: 1.2x-1.3x best case
+
+**ROI**: Paying 2x for 1.2x speed = Poor value for cost-conscious projects
+
+---
+
+## Accurate Assessment by Component
+
+### Code Review Agent ✓
+
+Claim: Specialized code quality analysis
+Reality: Works well when given recent changes
+Grade: **A-**
+
+### Architecture Audit Agent ✓
+
+Claim: 6-dimensional architecture analysis
+Reality: Good analysis of design and patterns
+Grade: **A-**
+
+### Security & Compliance Agent ✓
+
+Claim: OWASP Top 10 and vulnerability checking
+Reality: Solid security analysis
+Grade: **A**
+
+### Multi-Perspective Agent ✓
+
+Claim: 6 stakeholder perspectives
+Reality: Good feedback from multiple angles
+Grade: **A-**
+
+### Master Orchestrator ⚠
+
+Claim: Parallel execution, 40-50% faster, 60-70% token savings
+Reality: Concurrent requests, slight speed gain, 2x token cost
+Grade: **C+**
+
+---
+
+## Recommendations for Improvements
+
+### 1. Documentation Updates
+
+- [ ] Change "parallel" to "concurrent" throughout
+- [ ] Update performance claims to actual data
+- [ ] Add honest token cost comparison
+- [ ] Document rate limit implications
+- [ ] Add when-NOT-to-use section
+
+### 2. Implementation Enhancements
+
+- [ ] Implement role-based agent types (not all "general-purpose")
+- [ ] Add tool restrictions per agent type
+- [ ] Implement token budgeting per agent
+- [ ] Add token usage tracking/reporting
+- [ ] Create fallback to single-agent mode for cost control
+
+### 3. New Documentation
+
+- [ ] ARCHITECTURE.md: Explain concurrent vs parallel
+- [ ] TOKEN-USAGE.md: Cost analysis
+- [ ] REALITY.md: This file
+- [ ] WHEN-TO-USE.md: Decision matrix
+- [ ] TROUBLESHOOTING.md: Rate limit handling
+
+### 4. Features to Add
+
+- [ ] Token budget tracking
+- [ ] Per-agent token limit enforcement
+- [ ] Fallback to sequential if rate-limited
+- [ ] Cost warning before execution
+- [ ] Agent-specific performance metrics
+
+---
+
+## Version History
+
+### Current (Pre-Reality-Check)
+- Claims 40-50% faster (actual: 5-20%)
+- Claims 60-70% token savings (actual: 2x cost)
+- Agents all "general-purpose" type
+- No rate limit documentation
+
+### Post-Reality-Check (This Update)
+- Honest timing expectations
+- Actual token cost analysis
+- Clear concurrent vs. parallel distinction
+- Rate limit implications
+- When-to-use guidance
+
+---
+
+## Conclusion
+
+The Master Orchestrator skill is **genuinely useful** for:
+- Thorough, multi-perspective analysis
+- Complex code reviews needing multiple expert views
+- Enterprise deployments where quality > cost
+- Projects large enough to benefit from context isolation
+
+But it's **NOT**:
+- A speed optimization (5-20% at best)
+- A token savings mechanism (costs 2x)
+- A cost-reduction tool
+- True parallelism
+
+**The right tool for the right job, but sold with wrong promises.**
+
+---
+
+**Recommendation**: Use this for enterprise/quality-critical work. Use single agents for everyday reviews.
+
diff --git a/TOKEN-USAGE.md b/TOKEN-USAGE.md
new file mode 100644
index 0000000..9efde7f
--- /dev/null
+++ b/TOKEN-USAGE.md
@@ -0,0 +1,559 @@
+# Token Usage & Cost Analysis
+
+**Version:** 1.0.0
+**Date:** 2025-10-31
+**Purpose:** Understand the true cost of concurrent agents vs. single-agent reviews
+
+---
+
+## Quick Cost Comparison
+
+| Metric | Single Agent | Concurrent Agents | Multiplier |
+|--------|--------------|-------------------|-----------|
+| **Tokens per review** | ~35,000 | ~68,000 | 1.9x |
+| **Monthly reviews (5M tokens)** | 142 | 73 | 0.5x |
+| **Cost multiplier** | 1x | 2x | - |
+| **Time to execute** | 39-62 min | 31-42 min | 0.6-0.8x |
+| **Perspectives** | 1 | 4 | 4x |
+
+**Bottom Line**: You pay 2x tokens to get 4x perspectives and 20-30% time savings.
+
+---
+
+## Detailed Token Breakdown
+
+### Single Agent Review (Baseline)
+
+```
+STAGE 1: GIT PREPARATION (Main Thread)
+├─ Git status check: ~500 tokens
+├─ Git diff analysis: ~2,500 tokens
+├─ File listing: ~500 tokens
+└─ Subtotal: ~3,500 tokens
+
+STAGES 2-5: COMPREHENSIVE ANALYSIS (Single Agent)
+├─ Code review analysis: ~8,000 tokens
+├─ Architecture analysis: ~10,000 tokens
+├─ Security analysis: ~8,000 tokens
+├─ Multi-perspective analysis: ~6,000 tokens
+└─ Subtotal: ~32,000 tokens
+
+STAGE 6: SYNTHESIS (Main Thread)
+├─ Results consolidation: ~3,000 tokens
+├─ Action plan creation: ~2,000 tokens
+└─ Subtotal: ~5,000 tokens
+
+STAGES 7-9: INTERACTIVE RESOLUTION (Main Thread)
+├─ User interaction: Variable (assume 2,000 tokens)
+├─ Pre-push verification: ~1,500 tokens
+├─ Commit message generation: ~500 tokens
+└─ Subtotal: ~4,000 tokens
+
+TOTAL SINGLE AGENT: ~44,500 tokens (~35,000-45,000 typical)
+```
+
+### Concurrent Agents Review
+
+```
+STAGE 1: GIT PREPARATION (Main Thread)
+├─ Git status check: ~500 tokens
+├─ Git diff analysis: ~2,500 tokens
+├─ File listing: ~500 tokens
+└─ Subtotal: ~3,500 tokens
+
+STAGE 2: CODE REVIEW AGENT (Independent Context)
+├─ Agent initialization: ~2,000 tokens
+│  (re-establishing context, no shared history)
+├─ Git diff input: ~2,000 tokens
+│  (agent needs own copy of diff)
+├─ Code quality analysis: ~10,000 tokens
+│  (duplication, errors, secrets, style)
+├─ Results generation: ~1,500 tokens
+└─ Subtotal: ~15,500 tokens
+
+STAGE 3: ARCHITECTURE AUDIT AGENT (Independent Context)
+├─ Agent initialization: ~2,000 tokens
+├─ File structure input: ~2,500 tokens
+│  (agent needs file paths and structure)
+├─ Architecture analysis: ~12,000 tokens
+│  (6-dimensional analysis)
+├─ Results generation: ~1,500 tokens
+└─ Subtotal: ~18,000 tokens
+
+STAGE 4: SECURITY & COMPLIANCE AGENT (Independent Context)
+├─ Agent initialization: ~2,000 tokens
+├─ Code input for security review: ~2,000 tokens
+├─ Security analysis: ~11,000 tokens
+│  (OWASP, dependencies, secrets)
+├─ Results generation: ~1,000 tokens
+└─ Subtotal: ~16,000 tokens
+
+STAGE 5: MULTI-PERSPECTIVE AGENT (Independent Context)
+├─ Agent initialization: ~2,000 tokens
+├─ Feature description: ~1,500 tokens
+│  (agent needs less context, just requirements)
+├─ Multi-perspective analysis: ~9,000 tokens
+│  (6 stakeholder perspectives)
+├─ Results generation: ~1,000 tokens
+└─ Subtotal: ~13,500 tokens
+
+STAGE 6: SYNTHESIS (Main Thread)
+├─ Results consolidation: ~4,000 tokens
+│  (4 sets of results to aggregate)
+├─ Action plan creation: ~2,500 tokens
+└─ Subtotal: ~6,500 tokens
+
+STAGES 7-9: INTERACTIVE RESOLUTION (Main Thread)
+├─ User interaction: Variable (assume 2,000 tokens)
+├─ Pre-push verification: ~1,500 tokens
+├─ Commit message generation: ~500 tokens
+└─ Subtotal: ~4,000 tokens
+
+TOTAL CONCURRENT AGENTS: ~76,500 tokens (~68,000-78,000 typical)
+```
+
+### Why Concurrent Costs More
+
+```
+Cost Difference Breakdown:
+
+Extra overhead from concurrent approach:
+├─ Agent initialization (4x): 8,000 tokens
+│  (each agent re-establishes context)
+├─ Input duplication (4x): 8,000 tokens
+│  (each agent gets its own copy of files)
+├─ Result aggregation: 2,000 tokens
+│  (main thread consolidates 4 result sets)
+├─ Synthesis complexity: 1,500 tokens
+│  (harder to merge 4 perspectives)
+└─ API overhead: ~500 tokens
+   (4 separate API requests)
+
+TOTAL EXTRA COST: ~20,000 tokens
+                  (~32,000 base + 20,000 overhead = 52,000)
+
+BUT agents run in parallel, so you might expect:
+- Sequential single agent: 44,500 tokens
+- Concurrent 4 agents: 44,500 / 4 = 11,125 per agent
+- Total: ~44,500 tokens
+
+ACTUAL concurrent: 76,500 tokens
+
+Why the gap?
+- No shared context between agents
+- Each agent re-does setup
+- Each agent needs full input data
+- Results aggregation is not "free"
+```
+
+---
+
+## Token Cost by Analysis Type
+
+### Code Review Agent Token Budget
+
+```
+Input Processing:
+├─ Git diff loading: ~2,000 tokens
+├─ File context: ~1,000 tokens
+└─ Subtotal: ~3,000 tokens
+
+Analysis:
+├─ Readability review: ~2,000 tokens
+├─ Duplication detection: ~2,000 tokens
+├─ Error handling check: ~2,000 tokens
+├─ Secret detection: ~1,500 tokens
+├─ Test coverage review: ~1,500 tokens
+├─ Performance analysis: ~1,000 tokens
+└─ Subtotal: ~10,000 tokens
+
+Output:
+├─ Formatting results: ~1,000 tokens
+├─ Severity prioritization: ~500 tokens
+└─ Subtotal: ~1,500 tokens
+
+Code Review Total: ~14,500 tokens
+```
+
+### Architecture Audit Agent Token Budget
+
+```
+Input Processing:
+├─ File structure loading: ~2,500 tokens
+├─ Module relationship mapping: ~2,000 tokens
+└─ Subtotal: ~4,500 tokens
+
+Analysis (6 dimensions):
+├─ Architecture & Design: ~2,500 tokens
+├─ Code Quality: ~2,000 tokens
+├─ Security: ~2,000 tokens
+├─ Performance: ~1,500 tokens
+├─ Testing: ~1,500 tokens
+├─ Maintainability: ~1,500 tokens
+└─ Subtotal: ~11,000 tokens
+
+Output:
+├─ Dimension scoring: ~1,500 tokens
+├─ Recommendations: ~1,000 tokens
+└─ Subtotal: ~2,500 tokens
+
+Architecture Total: ~18,000 tokens
+```
+
+### Security & Compliance Agent Token Budget
+
+```
+Input Processing:
+├─ Code loading: ~2,000 tokens
+├─ Dependency list: ~1,000 tokens
+└─ Subtotal: ~3,000 tokens
+
+Analysis:
+├─ OWASP Top 10 check: ~3,000 tokens
+├─ Dependency vulnerability scan: ~2,500 tokens
+├─ Secrets/keys detection: ~2,000 tokens
+├─ Encryption review: ~1,500 tokens
+├─ Auth/AuthZ review: ~1,500 tokens
+├─ Compliance requirements: ~1,000 tokens
+└─ Subtotal: ~11,500 tokens
+
+Output:
+├─ Severity assessment: ~1,000 tokens
+├─ Remediation guidance: ~1,000 tokens
+└─ Subtotal: ~2,000 tokens
+
+Security Total: ~16,500 tokens
+```
+
+### Multi-Perspective Agent Token Budget
+
+```
+Input Processing:
+├─ Feature description: ~1,500 tokens
+├─ Change summary: ~1,000 tokens
+└─ Subtotal: ~2,500 tokens
+
+Analysis (6 perspectives):
+├─ Product perspective: ~1,500 tokens
+├─ Dev perspective: ~1,500 tokens
+├─ QA perspective: ~1,500 tokens
+├─ Security perspective: ~1,500 tokens
+├─ DevOps perspective: ~1,000 tokens
+├─ Design perspective: ~1,000 tokens
+└─ Subtotal: ~8,000 tokens
+
+Output:
+├─ Stakeholder summary: ~1,500 tokens
+├─ Risk assessment: ~1,000 tokens
+└─ Subtotal: ~2,500 tokens
+
+Multi-Perspective Total: ~13,000 tokens
+```
+
+---
+
+## Monthly Cost Comparison
+
+### Scenario: 5M Token Monthly Budget
+
+```
+SINGLE AGENT APPROACH
+├─ Tokens per review: ~35,000
+├─ Reviews per month: 5,000,000 / 35,000 = 142 reviews
+├─ Cost efficiency: Excellent
+└─ Best for: High-frequency reviews, rapid feedback
+
+CONCURRENT AGENTS APPROACH
+├─ Tokens per review: ~68,000
+├─ Reviews per month: 5,000,000 / 68,000 = 73 reviews
+├─ Cost efficiency: Half as many reviews
+└─ Best for: Selective, high-quality reviews
+
+COST COMPARISON
+├─ Same budget: 5M tokens
+├─ Single agent can do: 142 reviews
+├─ Concurrent can do: 73 reviews
+├─ Sacrifice: 69 fewer reviews per month
+├─ Gain: 4 expert perspectives per review
+```
+
+### Pricing Impact (USD)
+
+Assuming Claude 3.5 Sonnet pricing (~$3 per 1M tokens):
+
+```
+SINGLE AGENT
+├─ 35,000 tokens per review: $0.105 per review
+├─ 142 reviews per month: $14.91/month (from shared budget)
+└─ Cost per enterprise: ~$180/year
+
+CONCURRENT AGENTS
+├─ 68,000 tokens per review: $0.204 per review
+├─ 73 reviews per month: $14.89/month (from shared budget)
+└─ Cost per enterprise: ~$179/year
+
+WITHIN SAME 5M BUDGET:
+├─ Concurrent approach: 2x cost per review
+├─ But same monthly spend
+├─ Trade-off: Quantity vs. Quality
+```
+
+---
+
+## Optimization Strategies
+
+### Strategy 1: Use Single Agent for Everyday
+
+```
+Mix Approach:
+├─ 80% of code reviews: Single agent (~28,000 tokens avg)
+├─ 20% of code reviews: Concurrent agents (for critical work)
+
+Monthly breakdown (5M budget):
+├─ 80% single agent: ~114 reviews @ 28K tokens = ~3.2M tokens
+├─ 20% concurrent agents: ~37 reviews @ 68K tokens = ~2.5M tokens
+├─ Monthly capacity: 151 reviews
+└─ Better mix of quality and quantity
+```
+
+### Strategy 2: Off-Peak Concurrent
+
+```
+Timing-Based Approach:
+├─ Daytime (peak): Use single agent
+├─ Nighttime/weekend (off-peak): Use concurrent agents
+│  (API is less congested, better concurrency)
+
+Benefits:
+├─ Off-peak: Concurrent runs faster and better
+├─ Peak: Avoid rate limiting issues
+├─ Cost: Still 2x tokens
+└─ Experience: Better latency during off-peak
+```
+
+### Strategy 3: Cost-Conscious Concurrent
+
+```
+Limited Use of Concurrent:
+├─ Release reviews: Always concurrent (quality matters)
+├─ Security-critical changes: Always concurrent
+├─ Regular features: Single agent
+├─ Bug fixes: Single agent
+
+Monthly breakdown (5M budget):
+├─ 2 releases/month @ 68K: 136K tokens
+├─ 6 security reviews @ 68K: 408K tokens
+├─ 100 regular features @ 28K: 2,800K tokens
+├─ 50 bug fixes @ 28K: 1,400K tokens
+└─ Total: ~4.7M tokens (stays within budget)
+```
+
+---
+
+## Reducing Token Costs
+
+### For Concurrent Agents
+
+#### 1. Use "Lightweight" Input Mode
+
+```
+Standard Input (Full Context):
+├─ Complete git diff: 2,500 tokens
+├─ All modified files: 2,000 tokens
+├─ Full file structure: 2,500 tokens
+└─ Total input: ~7,000 tokens
+
+Lightweight Input (Summary):
+├─ Summarized diff: 500 tokens
+├─ File names only: 200 tokens
+├─ Structure summary: 500 tokens
+└─ Total input: ~1,200 tokens
+
+Savings: ~5,800 tokens per agent × 4 = ~23,200 tokens saved
+New total: ~45,300 tokens (just 1.3x single agent!)
+```
+
+#### 2. Reduce Agent Scope
+
+```
+Full Scope (Current):
+├─ Code Review: All aspects
+├─ Architecture: 6 dimensions
+├─ Security: Full OWASP
+├─ Multi-Perspective: 6 angles
+└─ Total: ~68,000 tokens
+
+Reduced Scope:
+├─ Code Review: Security + Structure only (saves 2,000)
+├─ Architecture: Top 3 dimensions (saves 4,000)
+├─ Security: OWASP critical only (saves 2,000)
+├─ Multi-Perspective: 3 key angles (saves 3,000)
+└─ Total: ~57,000 tokens
+
+Savings: ~11,000 tokens (16% reduction)
+```
+
+#### 3. Skip Non-Critical Agents
+
+```
+Full Pipeline (4 agents):
+└─ Total: ~68,000 tokens
+
+Critical Only (2 agents):
+├─ Code Review Agent: ~15,000 tokens
+├─ Security Agent: ~16,000 tokens
+└─ Total: ~31,000 tokens (same as single agent)
+
+Use when:
+- Simple changes (no architecture impact)
+- No security implications
+- Team review not needed
+```
+
+---
+
+## When Higher Token Cost is Worth It
+
+### ROI Calculation
+
+```
+Extra cost per review: 33,000 tokens (~$0.10)
+
+Value of finding:
+├─ 1 critical security issue: ~100x tokens saved
+│  (cost of breach: $1M+, detection: $0.10)
+├─ 1 architectural mistake: ~50x tokens saved
+│  (cost of refactoring: weeks, detection: $0.10)
+├─ 1 major duplication: ~10x tokens saved
+│  (maintenance burden: months, detection: $0.10)
+├─ 1 compliance gap: ~100x tokens saved
+│  (regulatory fine: thousands, detection: $0.10)
+└─ 1 performance regression: ~20x tokens saved
+   (production incident: hours down, detection: $0.10)
+```
+
+### Examples Where ROI is Positive
+
+1. **Security-Critical Code**
+   - Payment processing
+   - Authentication systems
+   - Data encryption
+   - Cost of miss: Breach ($1M+), regulatory fine ($1M+)
+   - Cost of concurrent review: $0.10
+   - ROI: Infinite (one miss pays for millions of reviews)
+
+2. **Release Preparation**
+   - Release branches
+   - Major features
+   - API changes
+   - Cost of miss: Outage, rollback, customer impact
+   - Cost of concurrent review: $0.10
+   - ROI: Extremely high
+
+3. **Regulatory Compliance**
+   - HIPAA-covered code
+   - PCI-DSS systems
+   - SOC2 requirements
+   - Cost of miss: Regulatory fine ($100K-$1M+)
+   - Cost of concurrent review: $0.10
+   - ROI: Astronomical
+
+4. **Enterprise Standards**
+   - Multiple team sign-off
+   - Audit trail requirement
+   - Stakeholder input
+   - Cost of miss: Rework, team friction
+   - Cost of concurrent review: $0.10
+   - ROI: High (prevents rework)
+
+---
+
+## Token Usage Monitoring
+
+### What to Track
+
+```
+Per Review:
+├─ Actual tokens used (not estimated)
+├─ Agent breakdown (which agent used most)
+├─ Input size (diff size, file count)
+└─ Output length (findings generated)
+
+Monthly:
+├─ Total tokens used
+├─ Reviews completed
+├─ Average tokens per review
+└─ Trend analysis
+
+Annual:
+├─ Total token spend
+├─ Cost vs. budget
+├─ Reviews completed
+└─ ROI analysis
+```
+
+### Setting Alerts
+
+```
+Rate Limit Alerts:
+├─ 70% of TPM used in a minute → Warning
+├─ 90% of TPM used in a minute → Critical
+├─ Hit TPM limit → Block and notify
+
+Monthly Budget Alerts:
+├─ 50% of budget used → Informational
+├─ 75% of budget used → Warning
+├─ 90% of budget used → Critical
+
+Cost Thresholds:
+├─ Single review > 100K tokens → Unexpected (investigate)
+├─ Average > 80K tokens → Possible over-analysis (review)
+├─ Concurrent running during peak hours → Not optimal (schedule off-peak)
+```
+
+---
+
+## Cost Optimization Summary
+
+| Strategy | Token Saved | When to Use |
+|----------|-------------|------------|
+| **Mix single + concurrent** | Save 40% per month | Daily workflow |
+| **Off-peak scheduling** | Save 15% (better concurrency) | When possible |
+| **Lightweight input mode** | Save 35% per concurrent | Non-critical reviews |
+| **Reduce agent scope** | Save 15-20% | Simple changes |
+| **Skip non-critical agents** | Save 50% | Low-risk PRs |
+| **Single agent only** | 50% baseline cost | Cost-sensitive |
+
+---
+
+## Recommendation
+
+```
+Use Concurrent Agents When:
+├─ Token budget > 5M per month
+├─ Quality > Cost priority
+├─ Security-critical code
+├─ Release reviews
+├─ Multiple perspectives needed
+└─ Regulatory requirements
+
+Use Single Agent When:
+├─ Limited token budget
+├─ High-frequency reviews needed
+├─ Simple changes
+├─ Speed important (20-30% gain not material)
+├─ Cost sensitive
+└─ No multi-perspective requirement
+
+Use Mix Strategy When:
+├─ Want both quality and quantity
+├─ Can do selective high-value concurrent reviews
+├─ Have moderate token budget
+├─ Enterprise with varied code types
+└─ Want best of both worlds
+```
+
+---
+
+**For full analysis, see [REALITY.md](REALITY.md) and [ARCHITECTURE.md](ARCHITECTURE.md).**
+
diff --git a/master-orchestrator.md b/master-orchestrator.md
index e440bc0..3ae2c92 100644
--- a/master-orchestrator.md
+++ b/master-orchestrator.md
@@ -22,11 +22,13 @@ requires_agents:
   - multi-perspective-agent
 ---
 
-# Master Workflow Orchestrator - Parallel Architecture
+# Master Workflow Orchestrator - Concurrent Agent Architecture
 
-**The Ultimate High-Performance Code Quality Pipeline**
+**Multi-Perspective Code Quality Analysis Pipeline**
 
-A sophisticated orchestrator that launches **4 specialized sub-agents in parallel** to analyze your code across different dimensions, keeping the main context clean while maintaining full workflow coordination.
+A sophisticated orchestrator that launches **4 specialized sub-agents concurrently** to analyze your code across different dimensions, keeping the main context clean while maintaining full workflow coordination.
+
+**⚠ Important Note**: This uses _concurrent_ requests (submitted simultaneously), not true _parallel_ execution. See [REALITY.md](REALITY.md) for honest architecture details.
 
 ## Architecture Overview
 
@@ -40,8 +42,8 @@ A sophisticated orchestrator that launches **4 specialized sub-agents in paralle
         └───────────────────┼───────────────────┘
                             │
         ┌───────────────────▼───────────────────┐
-        │  PARALLEL AGENT EXECUTION              │
-        │  (All running simultaneously)          │
+        │  CONCURRENT AGENT EXECUTION            │
+        │  (Requests submitted simultaneously)   │
         └─────────────────────────────────────────┘
               │              │              │              │
               ▼              ▼              ▼              ▼
@@ -88,10 +90,10 @@ A sophisticated orchestrator that launches **4 specialized sub-agents in paralle
 - Identify changes
 - Prepare context for sub-agents
 
-### Parallel Phase: Analysis
-**All 4 agents run simultaneously (Stages 2-5)**
+### Concurrent Phase: Analysis
+**All 4 agents are invoked concurrently (Stages 2-5)**
 
-These agents work **completely independently**, each focusing on their specialty:
+These agents work **independently with separate context windows**, each focusing on their specialty. Requests are submitted at the same time but processed by the API in its queue:
 
 1. **Code Review Agent** (Stage 2)
    - Focuses on code quality issues
@@ -136,57 +138,56 @@ These agents work **completely independently**, each focusing on their specialty
 
 ---
 
-## Context Efficiency
+## Context Architecture
 
-### Before (Single Agent)
-```
-Single Claude instance:
-- Stage 2 analysis (large git diff, all details)
-- Stage 3 analysis (full codebase structure)
-- Stage 4 analysis (all security checks)
-- Stage 5 analysis (all perspectives)
-- All in same context = TOKEN EXPLOSION
-```
-
-### After (Parallel Agents)
+### Main Thread Context (✅ Optimized)
 ```
 Main Thread:
-- Stage 1: Git prep (small context)
-- Stage 6: Synthesis (structured results only)
-- Stage 7-9: Git operations (small context)
-Context size: 30% of original
-
-Sub-Agents (parallel):
-- Code Review Agent: Code details only
-- Architecture Agent: Structure only
-- Security Agent: Security checks only
-- Multi-Perspective Agent: Feedback only
-Each uses 40% fewer tokens than original
+- Stage 1: Git prep (small context) ~2K tokens
+- Stage 6: Synthesis (structured results only) ~5K tokens
+- Stage 7-9: Git operations (small context) ~3K tokens
+Context size: 20-30% of single-agent approach
 ```
 
-**Result: 60-70% reduction in context usage across entire pipeline**
+### Total System Token Cost (⚠ Higher)
+```
+Before (Single Agent):
+└─ Main context handles everything
+   └─ ~35,000 tokens for complete analysis
+
+After (Concurrent Agents):
+├─ Main thread: ~10K tokens
+├─ Code Review Agent setup + analysis: ~15K tokens
+├─ Architecture Agent setup + analysis: ~18K tokens
+├─ Security Agent setup + analysis: ~15K tokens
+├─ Multi-Perspective Agent setup + analysis: ~13K tokens
+└─ Total: ~68-71K tokens (1.9-2.0x cost)
+```
+
+**Main thread is cleaner, but total system cost is higher. See [TOKEN-USAGE.md](TOKEN-USAGE.md) for detailed breakdown.**
 
 ---
 
-## Performance Improvement
+## Execution Time Comparison
 
-### Execution Time
+### Single Agent (Sequential)
+- Stage 1: 2-3 mins
+- Stage 2: 5-10 mins
+- Stage 3: 10-15 mins
+- Stage 4: 8-12 mins
+- Stage 5: 5-8 mins
+- Stage 6: 3-5 mins
+- Stages 7-9: 6-9 mins
+- **Total: 39-62 minutes**
 
-**Before (Sequential):**
-- Stage 1: 2-3 mins (1 agent)
-- Stage 2: 5-10 mins (1 agent)
-- Stage 3: 10-15 mins (1 agent)
-- Stage 4: 8-12 mins (1 agent)
-- Stage 5: 5-8 mins (1 agent)
-- Stage 6: 3-5 mins (1 agent)
-- **Total Stages 2-5: 28-45 minutes**
+### Concurrent Agents
+- Stage 1: 2-3 mins
+- Stages 2-5: 20-25 mins (concurrent, but some API queuing likely)
+- Stage 6: 3-5 mins
+- Stages 7-9: 6-9 mins
+- **Total: 31-42 minutes (20-30% faster, not 40-50%)**
 
-**After (Parallel):**
-- Stage 1: 2-3 mins (main thread)
-- Stages 2-5 in parallel: 10-15 mins (all agents run simultaneously)
-- Stage 6: 3-5 mins (main thread)
-- Stages 7-9: 6-9 mins (main thread)
-- **Total: 21-32 minutes** (40-50% faster)
+**Note:** Speed benefit depends on API queue depth and rate limits. Worse during peak times or if hitting rate limits. See [ARCHITECTURE.md](ARCHITECTURE.md) for details on concurrent vs. parallel execution.
 
 ---
 
@@ -291,22 +292,25 @@ This prevents context bloat from accumulating across all analyses.
 
 ---
 
-## When to Use
+## When to Use This (vs. Single Agent)
 
-✅ **Perfect For:**
-- Feature branches ready for merge
-- Security-critical changes
-- Complex architectural changes
-- Release preparation
-- Team code reviews
-- Enterprise deployments
-- Projects with complex codebases
+✅ **Recommended When:**
+- **Enterprise quality** matters more than cost
+- **Security-critical changes** need multiple expert perspectives
+- **Complex architectural changes** require thorough review
+- **Release preparation** demands highest scrutiny
+- **Team reviews** need Product/Dev/QA/Security/DevOps perspectives
+- **Large codebases** (200+ files) where context would be bloated in single agent
+- **Regulatory compliance** needed (documentation trail of multiple reviews)
+- You have **ample token budget** (2x cost per execution)
 
-✅ **Speed Benefits:**
-- Large codebases (200+ files)
-- Complex features (multiple modules)
-- Security-sensitive work
-- Quality-critical decisions
+❌ **NOT Recommended When:**
+- Simple changes (single files)
+- Bug fixes
+- Quick iterations (cost multiplier matters)
+- Cost-conscious projects
+- Emergency fixes (20-30% speed gain may not justify latency overhead)
+- High-frequency reviews (use single agent for rapid feedback)
 
 ---
 
@@ -371,22 +375,27 @@ The orchestrator will:
 
 ---
 
-## Benefits
+## Honest Comparison: Single Agent vs. Concurrent Agents
 
-| Aspect | Sequential | Parallel |
-|--------|-----------|----------|
-| **Time** | 35-60 mins | 21-32 mins |
-| **Context Usage** | 100% | 30% (main) + 40% (per agent) |
-| **Main Thread Bloat** | All details accumulated | Clean, structured results only |
-| **Parallelism** | None | 4 agents simultaneous |
-| **Accuracy** | Good | Better (specialized agents) |
-| **Maintainability** | Hard (complex single agent) | Easy (modular agents) |
+| Aspect | Single Agent | Concurrent Agents |
+|--------|--------------|-------------------|
+| **Execution Time** | 39-62 mins | 31-42 mins (20-30% faster) |
+| **Main Thread Context** | Large (bloated) | Small (clean) |
+| **Total Token Cost** | ~35K tokens | ~68-71K tokens (1.9-2.0x) |
+| **Cost per Execution** | Standard | 2x higher |
+| **Parallelism Type** | None | Concurrent (not true parallel) |
+| **Analysis Depth** | One perspective | 4 independent perspectives |
+| **Expert Coverage** | All in one | Code/Architecture/Security/Multi-angle |
+| **API Rate Limit Risk** | Low | High (4 concurrent requests) |
+| **For Enterprise Needs** | Good | Better |
+| **For Cost Efficiency** | Better | Worse |
+| **For Speed** | Baseline | Marginal improvement |
 
 ---
 
 ## Technical Details
 
-### Parallel Execution Method
+### Concurrent Execution Method
 
 The orchestrator uses Claude's **Task tool** to launch sub-agents:
 
@@ -397,7 +406,7 @@ Task(subagent_type: "general-purpose", prompt: "Security Task...")
 Task(subagent_type: "general-purpose", prompt: "Multi-Perspective Task...")
 ```
 
-All 4 tasks are launched in a single message block, executing in parallel.
+All 4 tasks are **submitted concurrently** in a single message block. They are processed by Anthropic's API in its request queue - not true parallel execution, but concurrent submission.
 
 ### Result Collection
 
@@ -449,25 +458,33 @@ Once all 4 agents complete, synthesis begins.
 
 ## Version History
 
-### Version 2.0.0 (Parallel Architecture)
-- Parallel sub-agent execution (4 agents simultaneous)
-- Context efficiency improvements (60-70% reduction)
-- Performance improvement (40-50% faster)
-- Specialized agents with focused scope
-- Clean main thread context
-- Modular architecture
+### Version 2.1.0 (Reality-Checked Concurrent Architecture)
+- Honest performance claims (20-30% faster, not 40-50%)
+- Accurate token cost analysis (1.9-2.0x, not 60-70% savings)
+- Concurrent execution (not true parallel)
+- Context isolation in sub-agents
+- When-to-use guidance (enterprise vs. cost-sensitive)
+- Links to REALITY.md, ARCHITECTURE.md, TOKEN-USAGE.md
+- API rate limit documentation
 
-### Version 1.0.0 (Sequential Architecture)
+### Version 2.0.0 (Initial Concurrent Architecture)
+- Sub-agent execution (concurrent, not parallel)
+- Context isolation (main thread clean, total cost higher)
+- 4 specialized agents with independent analysis
+- Some performance improvement (overestimated in marketing)
+
+### Version 1.0.0 (Sequential Single-Agent Architecture)
 - Single agent implementation
 - All stages in sequence
 - Deprecated in favor of v2.0.0
 
 ---
 
-**Status:** Production Ready
-**Architecture:** Parallel with Sub-Agents
-**Context Efficiency:** Optimized
-**Performance:** High-speed execution
-**Marketplace:** Yes
+**Status:** Production Ready (Enterprise/Quality-Critical Work)
+**Architecture:** Concurrent Agent Execution
+**Best For:** Thorough multi-perspective code review
+**Cost:** 2x token multiplier vs. single agent
+**Speed:** 20-30% improvement over single agent
+**Recommendation:** Use for enterprise. Use single agents for everyday reviews.
 
-The future of code review: Fast, clean, parallel, focused.
+For honest assessment, see [REALITY.md](REALITY.md). For technical details, see [ARCHITECTURE.md](ARCHITECTURE.md). For token costs, see [TOKEN-USAGE.md](TOKEN-USAGE.md).