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OrchestKit v6.7.1 — 67 skills, 38 agents, 77 hooks with Opus 4.6 support
OrchestKit
Skills

Explore

explore — Deep codebase exploration with parallel agents. Use when exploring a repo, discovering architecture, finding files, or analyzing design patterns.

Command high

Codebase Exploration

Multi-angle codebase exploration using 3-5 parallel agents.

Quick Start

/ork:explore authentication

Opus 4.6: Exploration agents use native adaptive thinking for deeper pattern recognition across large codebases.


STEP 0: Verify User Intent with AskUserQuestion

BEFORE creating tasks, clarify what the user wants to explore:

AskUserQuestion(
  questions=[{
    "question": "What aspect do you want to explore?",
    "header": "Focus",
    "options": [
      {"label": "Full exploration (Recommended)", "description": "Code structure + data flow + architecture + health assessment"},
      {"label": "Code structure only", "description": "Find files, classes, functions related to topic"},
      {"label": "Data flow", "description": "Trace how data moves through the system"},
      {"label": "Architecture patterns", "description": "Identify design patterns and integrations"},
      {"label": "Quick search", "description": "Just find relevant files, skip deep analysis"}
    ],
    "multiSelect": false
  }]
)

Based on answer, adjust workflow:

  • Full exploration: All phases, all parallel agents
  • Code structure only: Skip phases 5-7 (health, dependencies, product)
  • Data flow: Focus phase 3 agents on data tracing
  • Architecture patterns: Focus on backend-system-architect agent
  • Quick search: Skip to phases 1-2 only, return file list

STEP 0b: Select Orchestration Mode

Choose Agent Teams (mesh) or Task tool (star):

  1. CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1Agent Teams mode
  2. Agent Teams unavailable → Task tool mode (default)
  3. Full exploration with 4+ agents → recommend Agent Teams; Quick/single-focus → Task tool
AspectTask ToolAgent Teams
Discovery sharingLead synthesizes after all completeExplorers share discoveries as they go
Cross-referencingLead connects dotsData flow explorer alerts architecture explorer
Cost~150K tokens~400K tokens
Best forQuick/focused searchesDeep full-codebase exploration

Fallback: If Agent Teams encounters issues, fall back to Task tool for remaining exploration.


Task Management (MANDATORY)

BEFORE doing ANYTHING else, create tasks to show progress:

TaskCreate(subject="Explore: {topic}", description="Deep codebase exploration for {topic}", activeForm="Exploring {topic}")
TaskCreate(subject="Initial file search", activeForm="Searching files")
TaskCreate(subject="Check knowledge graph", activeForm="Checking memory")
TaskCreate(subject="Launch exploration agents", activeForm="Dispatching explorers")
TaskCreate(subject="Assess code health (0-10)", activeForm="Assessing code health")
TaskCreate(subject="Map dependency hotspots", activeForm="Mapping dependencies")
TaskCreate(subject="Add product perspective", activeForm="Adding product context")
TaskCreate(subject="Generate exploration report", activeForm="Generating report")

Workflow Overview

PhaseActivitiesOutput
1. Initial SearchGrep, Glob for matchesFile locations
2. Memory CheckSearch knowledge graphPrior context
3. Deep Exploration4 parallel explorersMulti-angle analysis
4. AI System (if applicable)LangGraph, prompts, RAGAI-specific findings
5. Code HealthRate code 0-10Quality scores
6. Dependency HotspotsIdentify couplingHotspot visualization
7. Product PerspectiveBusiness contextFindability suggestions
8. Report GenerationCompile findingsActionable report

# PARALLEL - Quick searches
Grep(pattern="$ARGUMENTS", output_mode="files_with_matches")
Glob(pattern="**/*$ARGUMENTS*")

Phase 2: Memory Check

mcp__memory__search_nodes(query="$ARGUMENTS")
mcp__memory__search_nodes(query="architecture")

Phase 3: Parallel Deep Exploration (4 Agents)

See Exploration Agents for Task tool mode prompts.

See Agent Teams Mode for Agent Teams alternative.

Phase 4: AI System Exploration (If Applicable)

For AI/ML topics, add exploration of: LangGraph workflows, prompt templates, RAG pipeline, caching strategies.

Phase 5: Code Health Assessment

See Code Health Assessment for agent prompt. See Code Health Rubric for scoring criteria.

Phase 6: Dependency Hotspot Map

See Dependency Hotspot Analysis for agent prompt. See Dependency Analysis for metrics.

Phase 7: Product Perspective

See Product Perspective for agent prompt. See Findability Patterns for best practices.

Phase 8: Generate Report

See Exploration Report Template.

Common Exploration Queries

  • "How does authentication work?"
  • "Where are API endpoints defined?"
  • "Find all usages of EventBroadcaster"
  • "What's the workflow for content analysis?"
  • ork:implement: Implement after exploration

Version: 2.1.0 (February 2026)


Rules (5)

Coordinate multi-agent exploration teams with real-time discovery sharing — HIGH

Agent Teams Mode

In Agent Teams mode, form an exploration team where explorers share discoveries in real-time:

TeamCreate(team_name="explore-{topic}", description="Explore {topic}")

Task(subagent_type="Explore", name="structure-explorer",
     team_name="explore-{topic}",
     prompt="""Find all files, classes, and functions related to: {topic}
     When you discover key entry points, message data-flow-explorer so they
     can trace data paths from those points.
     When you find backend patterns, message backend-explorer.
     When you find frontend components, message frontend-explorer.""")

Task(subagent_type="Explore", name="data-flow-explorer",
     team_name="explore-{topic}",
     prompt="""Trace entry points, processing, and storage for: {topic}
     When structure-explorer shares entry points, start tracing from those.
     When you discover cross-boundary data flows (frontend→backend or vice versa),
     message both backend-explorer and frontend-explorer.""")

Task(subagent_type="backend-system-architect", name="backend-explorer",
     team_name="explore-{topic}",
     prompt="""Analyze backend architecture patterns for: {topic}
     When structure-explorer or data-flow-explorer share backend findings,
     investigate deeper — API design, database schema, service patterns.
     Share integration points with frontend-explorer for consistency.""")

Task(subagent_type="frontend-ui-developer", name="frontend-explorer",
     team_name="explore-{topic}",
     prompt="""Analyze frontend components, state, and routes for: {topic}
     When structure-explorer shares component locations, investigate deeper.
     When backend-explorer shares API patterns, verify frontend alignment.
     Share component hierarchy with data-flow-explorer.""")

Team Teardown

After report generation:

SendMessage(type="shutdown_request", recipient="structure-explorer", content="Exploration complete")
SendMessage(type="shutdown_request", recipient="data-flow-explorer", content="Exploration complete")
SendMessage(type="shutdown_request", recipient="backend-explorer", content="Exploration complete")
SendMessage(type="shutdown_request", recipient="frontend-explorer", content="Exploration complete")
TeamDelete()

Fallback: If team formation fails, use standard Task tool spawns. See exploration-agents.md.

Incorrect — Sequential exploration without coordination:

Task(subagent_type="Explore", prompt="Find auth files")
# Wait for result...
Task(subagent_type="Explore", prompt="Trace auth data flow")
# Sequential, no sharing between agents

Correct — Team mode with real-time discovery sharing:

TeamCreate(team_name="explore-auth")
Task(subagent_type="Explore", name="structure-explorer",
     team_name="explore-auth",
     prompt="Find auth files. Message data-flow-explorer with entry points.")
Task(subagent_type="Explore", name="data-flow-explorer",
     team_name="explore-auth",
     prompt="When structure-explorer shares entry points, trace data flows.")
# Parallel execution, coordinated via messages

Score code health across five quality dimensions with structured assessment criteria — MEDIUM

Code Health Assessment

Rate found code quality 0-10 with specific dimensions. See code-health-rubric.md for scoring criteria.

Task(
  subagent_type="code-quality-reviewer",
  prompt="""CODE HEALTH ASSESSMENT for files related to: $ARGUMENTS

  Rate each dimension 0-10:

  1. READABILITY (0-10)
     - Clear naming conventions?
     - Appropriate comments?
     - Logical organization?

  2. MAINTAINABILITY (0-10)
     - Single responsibility?
     - Low coupling?
     - Easy to modify?

  3. TESTABILITY (0-10)
     - Pure functions where possible?
     - Dependency injection?
     - Existing test coverage?

  4. COMPLEXITY (0-10, inverted: 10=simple, 0=complex)
     - Cyclomatic complexity?
     - Nesting depth?
     - Function length?

  5. DOCUMENTATION (0-10)
     - API docs present?
     - Usage examples?
     - Architecture notes?

  Output:
  {
    "overall_score": N.N,
    "dimensions": {
      "readability": N,
      "maintainability": N,
      "testability": N,
      "complexity": N,
      "documentation": N
    },
    "hotspots": ["file:line - issue"],
    "recommendations": ["improvement suggestion"]
  }

  SUMMARY: End with: "HEALTH: [N.N]/10 - [best dimension] strong, [worst dimension] needs work"
  """,
  run_in_background=True,
  max_turns=25
)

Incorrect — Vague code quality feedback:

Code Review: The code looks okay. Some parts are complex.
Maybe add more tests.

Correct — Structured health assessment with scores:

{
  "overall_score": 6.2,
  "dimensions": {
    "readability": 8,
    "maintainability": 5,
    "testability": 4,
    "complexity": 6,
    "documentation": 8
  },
  "hotspots": [
    "auth.ts:45 - nested if/else 5 levels deep",
    "utils.ts:120 - 200-line function, no SRP"
  ],
  "recommendations": [
    "Extract auth.ts:45-80 to separate validation functions",
    "Add unit tests for utils.ts edge cases"
  ]
}

Identify highly-coupled code and dependency bottlenecks to reduce change risk — MEDIUM

Dependency Hotspot Analysis

Identify highly-coupled code and dependency bottlenecks. See dependency-analysis.md for metrics and formulas.

Task(
  subagent_type="backend-system-architect",
  prompt="""DEPENDENCY HOTSPOT ANALYSIS for: $ARGUMENTS

  Analyze coupling and dependencies:

  1. IMPORT ANALYSIS
     - Which files import this code?
     - What does this code import?
     - Circular dependencies?

  2. COUPLING SCORE (0-10, 10=highly coupled)
     - How many files would break if this changes?
     - Fan-in (incoming dependencies)
     - Fan-out (outgoing dependencies)

  3. CHANGE IMPACT
     - Blast radius of modifications
     - Files that always change together

  4. HOTSPOT VISUALIZATION

[Module A] --depends--> [Target] <--depends-- [Module B] | v [Module C]


Output:
{
"coupling_score": N,
"fan_in": N,
"fan_out": N,
"circular_deps": [],
"change_impact": ["file - reason"],
"hotspot_diagram": "ASCII diagram"
}

SUMMARY: End with: "COUPLING: [N]/10 - [N] incoming, [M] outgoing deps - [key concern]"
""",
run_in_background=True,
max_turns=25
)

Incorrect — Listing imports without analysis:

auth.ts imports:
- utils.ts
- config.ts
- db.ts

Correct — Hotspot analysis with coupling score:

{
  "coupling_score": 8,
  "fan_in": 12,
  "fan_out": 5,
  "circular_deps": ["auth.ts → user.ts → auth.ts"],
  "change_impact": [
    "auth.ts change breaks 12 files",
    "utils.ts and auth.ts always change together"
  ],
  "hotspot_diagram": "
    [12 files] --depend on--> [auth.ts]
                                  |
                              depends on
                                  v
                      [utils, config, db, user, session]
  "
}

Spawn parallel exploration agents using Task tool for concurrent codebase analysis — HIGH

Exploration Agents (Task Tool Mode)

Launch 4 specialized explorers in ONE message with run_in_background: true:

# PARALLEL - All 4 in ONE message
Task(
  subagent_type="Explore",
  prompt="""Code Structure: Find all files, classes, functions related to: $ARGUMENTS

  Scope: ONLY read files directly relevant to the topic. Do NOT explore the entire codebase.

  SUMMARY: End with: "RESULT: [N] files, [M] classes - [key location, e.g., 'src/auth/']"
  """,
  run_in_background=True,
  max_turns=25
)
Task(
  subagent_type="Explore",
  prompt="""Data Flow: Trace entry points, processing, storage for: $ARGUMENTS

  Scope: ONLY read files directly relevant to the topic. Do NOT explore the entire codebase.

  SUMMARY: End with: "RESULT: [entry] → [processing] → [storage] - [N] hop flow"
  """,
  run_in_background=True,
  max_turns=25
)
Task(
  subagent_type="backend-system-architect",
  prompt="""Backend Patterns: Analyze architecture patterns, integrations, dependencies for: $ARGUMENTS

  Scope: ONLY read files directly relevant to the topic. Do NOT explore the entire codebase.

  SUMMARY: End with: "RESULT: [pattern name] - [N] integrations, [M] dependencies"
  """,
  run_in_background=True,
  max_turns=25
)
Task(
  subagent_type="frontend-ui-developer",
  prompt="""Frontend Analysis: Find components, state management, routes for: $ARGUMENTS

  Scope: ONLY read files directly relevant to the topic. Do NOT explore the entire codebase.

  SUMMARY: End with: "RESULT: [N] components, [state lib] - [key route]"
  """,
  run_in_background=True,
  max_turns=25
)

Explorer Roles

  1. Code Structure Explorer - Files, classes, functions
  2. Data Flow Explorer - Entry points, processing, storage
  3. Backend Architect - Patterns, integration, dependencies
  4. Frontend Developer - Components, state, routes

Incorrect — Sequential exploration:

Task(subagent_type="Explore", prompt="Find auth files")
# Wait...
Task(subagent_type="Explore", prompt="Trace auth flow")
# Wait...
Task(subagent_type="backend-system-architect", prompt="Analyze patterns")
# Slow, sequential

Correct — Parallel exploration in one message:

# All 4 in ONE message with run_in_background: true
Task(subagent_type="Explore", prompt="Code Structure: Find all files related to auth",
     run_in_background=True, max_turns=25)
Task(subagent_type="Explore", prompt="Data Flow: Trace auth entry→storage",
     run_in_background=True, max_turns=25)
Task(subagent_type="backend-system-architect", prompt="Backend Patterns: Analyze auth architecture",
     run_in_background=True, max_turns=25)
Task(subagent_type="frontend-ui-developer", prompt="Frontend: Find auth components",
     run_in_background=True, max_turns=25)
# Parallel execution

Add business context and findability analysis to technical codebase exploration — MEDIUM

Product Perspective

Add business context and findability suggestions. See findability-patterns.md for discoverability best practices.

Task(
  subagent_type="product-strategist",
  prompt="""PRODUCT PERSPECTIVE for: $ARGUMENTS

  Analyze from a product/business viewpoint:

  1. BUSINESS CONTEXT
     - What user problem does this code solve?
     - What feature/capability does it enable?
     - Who are the users of this code?

  2. FINDABILITY SUGGESTIONS
     - Better naming for discoverability?
     - Missing documentation entry points?
     - Where should someone look first?

  3. KNOWLEDGE GAPS
     - What context is missing for new developers?
     - What tribal knowledge exists?
     - What should be documented?

  4. SEARCH OPTIMIZATION
     - Keywords someone might use to find this
     - Alternative terms for the same concept
     - Related concepts to cross-reference

  Output:
  {
    "business_purpose": "description",
    "primary_users": ["user type"],
    "findability_issues": ["issue - suggestion"],
    "recommended_entry_points": ["file - why start here"],
    "search_keywords": ["keyword"],
    "documentation_gaps": ["gap"]
  }

  SUMMARY: End with: "FINDABILITY: [N] issues - start at [recommended entry point]"
  """,
  run_in_background=True,
  max_turns=25)

Incorrect — Technical analysis without business context:

Found auth.ts, user.ts, session.ts
Uses JWT tokens, bcrypt hashing
Database: PostgreSQL users table

Correct — Product perspective with findability:

{
  "business_purpose": "Secure user authentication and session management",
  "primary_users": ["End users logging in", "Developers integrating auth"],
  "findability_issues": [
    "auth.ts - generic name, try auth/core.ts",
    "Missing README in auth/ - devs don't know where to start"
  ],
  "recommended_entry_points": [
    "auth/README.md (missing - create this!)",
    "auth/core.ts - main authentication flow"
  ],
  "search_keywords": ["login", "authentication", "session", "JWT", "security"],
  "documentation_gaps": [
    "No auth flow diagram",
    "Token refresh logic undocumented"
  ]
}

References (4)

Code Health Rubric

Code Health Rubric

Standardized 0-10 scoring criteria for assessing code quality across five dimensions.

Scoring Scale

ScoreRatingDescription
9-10ExcellentProduction-ready, exemplary code
7-8GoodMinor improvements possible
5-6AdequateFunctional but needs attention
3-4PoorSignificant issues, refactor recommended
0-2CriticalMajor problems, immediate action required

1. Readability (0-10)

ScoreCriteria
10Self-documenting, intuitive naming, perfect structure
7-8Clear names, logical flow, minimal cognitive load
5-6Understandable with effort, some unclear sections
3-4Confusing logic, poor naming, requires context
0-2Incomprehensible, magic numbers, no conventions

2. Maintainability (0-10)

ScoreCriteria
10SRP adherence, loose coupling, DRY, easy to modify
7-8Good separation, minor duplication, clear boundaries
5-6Some coupling, moderate duplication, changes ripple
3-4High coupling, significant duplication, fragile
0-2Spaghetti code, any change breaks multiple areas

3. Testability (0-10)

ScoreCriteria
10Pure functions, DI, 90%+ coverage, mocks easy
7-8Most logic testable, some DI, 70%+ coverage
5-6Testable with effort, some hidden dependencies
3-4Hard to isolate, global state, 30% coverage
0-2Untestable, tightly coupled, no test infrastructure

4. Complexity (0-10, inverted: 10=simple)

ScoreCriteria
10Cyclomatic <5, max 2 nesting, <20 line functions
7-8Cyclomatic 5-10, 3 nesting, <40 line functions
5-6Cyclomatic 10-15, 4 nesting, some long functions
3-4Cyclomatic 15-25, deep nesting, 100+ line functions
0-2Cyclomatic >25, 6+ nesting, god functions

5. Documentation (0-10)

ScoreCriteria
10Complete API docs, examples, architecture notes
7-8Public API documented, inline comments where needed
5-6Some docstrings, missing edge cases
3-4Sparse comments, outdated documentation
0-2No documentation, misleading comments

Overall Score Calculation

overall = (readability + maintainability + testability + complexity + documentation) / 5

Score Interpretation:

  • 8.0+: Ship it
  • 6.0-7.9: Acceptable, plan improvements
  • 4.0-5.9: Technical debt, prioritize refactoring
  • <4.0: Stop and fix before proceeding

Dependency Analysis

Dependency Analysis

Identify coupling hotspots and dependency patterns in codebases.

Fan-In / Fan-Out Metrics

MetricDefinitionImplication
Fan-InFiles that import this moduleHigh = many dependents, changes risky
Fan-OutModules this file importsHigh = many dependencies, fragile
InstabilityFan-Out / (Fan-In + Fan-Out)0 = stable, 1 = unstable

Ideal Patterns:

  • Core utilities: High fan-in, low fan-out (stable)
  • Feature modules: Low fan-in, moderate fan-out
  • Entry points: Low fan-in, high fan-out

Hotspot Identification

High-Risk Indicators

PatternRiskAction
Fan-in > 10Blast radius largeAdd interface/abstraction
Fan-out > 8Too many dependenciesExtract facades
Instability = 1, Fan-in > 5Unstable coreStabilize or decouple

Coupling Score Formula

coupling_score = min(10, (fan_in + fan_out) / 3)
  • 0-3: Low coupling (healthy)
  • 4-6: Moderate coupling (monitor)
  • 7-10: High coupling (refactor)

Circular Dependency Detection

Signs of Circular Dependencies:

  1. Import errors at runtime
  2. Mysterious None values
  3. Files that always change together
  4. Cannot extract to separate package

Detection Approach:

A imports B
B imports C
C imports A  <- CIRCULAR

Resolution Strategies:

  1. Extract shared interface
  2. Dependency inversion (depend on abstractions)
  3. Merge tightly coupled modules
  4. Event-driven decoupling

Change Impact Analysis

Questions to Answer:

  1. If I modify this file, what breaks?
  2. Which files always change together?
  3. What is the blast radius of a refactor?

Measuring Impact:

  • Direct Impact: Files importing the changed module
  • Transitive Impact: Files importing those files
  • Co-Change Frequency: Git history of files changed together

High Impact Indicators:

  • 5 direct dependents

  • 20 transitive dependents

  • 80% co-change frequency with another file

Exploration Report Template

Exploration Report Template

Use this template for Phase 8 report generation.

# Exploration Report: $ARGUMENTS

## Quick Answer
[1-2 sentence summary]

## File Locations
| File | Purpose | Health Score |
|------|---------|--------------|
| `path/to/file.py` | [description] | [N.N/10] |

## Code Health Summary
| Dimension | Score | Notes |
|-----------|-------|-------|
| Readability | [N/10] | [note] |
| Maintainability | [N/10] | [note] |
| Testability | [N/10] | [note] |
| Complexity | [N/10] | [note] |
| Documentation | [N/10] | [note] |
| **Overall** | **[N.N/10]** | |

## Architecture Overview
[ASCII diagram]

## Dependency Hotspot Map

[Incoming deps] → [TARGET] → [Outgoing deps]

- **Coupling Score:** [N/10]
- **Fan-in:** [N] files depend on this
- **Fan-out:** [M] dependencies
- **Circular Dependencies:** [list or "None"]

## Data Flow
1. [Entry] → 2. [Processing] → 3. [Storage]

## Findability & Entry Points
| Entry Point | Why Start Here |
|-------------|----------------|
| `path/to/file.py` | [reason] |

**Search Keywords:** [keyword1], [keyword2], [keyword3]

## Product Context
- **Business Purpose:** [what problem this solves]
- **Primary Users:** [who uses this]
- **Documentation Gaps:** [what's missing]

## How to Modify
1. [Step 1]
2. [Step 2]

## Recommendations
1. [Health improvement]
2. [Findability improvement]
3. [Documentation improvement]

Findability Patterns

Findability Patterns

Improve code discoverability for developers exploring the codebase.

Naming Conventions for Searchability

PatternExampleSearchability
Domain prefixauth_login(), auth_logout()Grep "auth_" finds all
Feature suffixUserService, UserRepositoryGrep "User" finds related
Action verbscreate_user, delete_orderGrep "create_" finds patterns
Consistent pluralizationusers/, orders/Predictable directory names

Anti-Patterns:

  • Abbreviations: usr, mgr, svc (hard to search)
  • Generic names: utils.py, helpers.js (too broad)
  • Inconsistent casing: getUserData, get_user_data

Documentation Placement

LocationPurposeFindability
README.md in directoryModule overviewFirst thing developers see
Inline docstringsFunction behaviorIDE tooltips, grep
docs/architecture/System designHigh-level understanding
CLAUDE.md / CONTRIBUTING.mdDevelopment guideOnboarding entry

Entry Point Strategy:

  1. Every directory should have a README or index
  2. Complex modules need architecture diagrams
  3. Public APIs need usage examples
  4. Workflows need sequence diagrams

Module Organization

Vertical Slice Architecture

features/
  auth/
    api.py          # Entry point
    service.py      # Business logic
    repository.py   # Data access
    models.py       # Domain models
    tests/          # Co-located tests

Benefits:

  • Related code together
  • Easy to find all auth-related files
  • Clear boundaries

Horizontal Layer Architecture

api/
  auth.py
  users.py
services/
  auth.py
  users.py

Benefits:

  • Technical cohesion
  • Easier cross-cutting concerns

Improving Discoverability

Quick Wins

  1. Add index files: Export public API from __init__.py or index.ts
  2. Use consistent prefixes: handle_, on_, create_, get_
  3. Create README per directory: Brief purpose + key files
  4. Tag with keywords: Add searchable comments for concepts

Search Optimization

# Keywords: authentication, login, JWT, OAuth, session
# See also: user_service.py, token_handler.py

Metadata in Files:

  • Related files cross-reference
  • Alternative terms for the concept
  • Links to documentation
Edit on GitHub

Last updated on

On this page

Related SkillsCodebase ExplorationQuick StartSTEP 0: Verify User Intent with AskUserQuestionSTEP 0b: Select Orchestration ModeTask Management (MANDATORY)Workflow OverviewPhase 1: Initial SearchPhase 2: Memory CheckPhase 3: Parallel Deep Exploration (4 Agents)Phase 4: AI System Exploration (If Applicable)Phase 5: Code Health AssessmentPhase 6: Dependency Hotspot MapPhase 7: Product PerspectivePhase 8: Generate ReportCommon Exploration QueriesRelated SkillsRules (5)Coordinate multi-agent exploration teams with real-time discovery sharing — HIGHAgent Teams ModeTeam TeardownScore code health across five quality dimensions with structured assessment criteria — MEDIUMCode Health AssessmentIdentify highly-coupled code and dependency bottlenecks to reduce change risk — MEDIUMDependency Hotspot AnalysisSpawn parallel exploration agents using Task tool for concurrent codebase analysis — HIGHExploration Agents (Task Tool Mode)Explorer RolesAdd business context and findability analysis to technical codebase exploration — MEDIUMProduct PerspectiveReferences (4)Code Health RubricCode Health RubricScoring Scale1. Readability (0-10)2. Maintainability (0-10)3. Testability (0-10)4. Complexity (0-10, inverted: 10=simple)5. Documentation (0-10)Overall Score CalculationDependency AnalysisDependency AnalysisFan-In / Fan-Out MetricsHotspot IdentificationHigh-Risk IndicatorsCoupling Score FormulaCircular Dependency DetectionChange Impact AnalysisExploration Report TemplateExploration Report TemplateFindability PatternsFindability PatternsNaming Conventions for SearchabilityDocumentation PlacementModule OrganizationVertical Slice ArchitectureHorizontal Layer ArchitectureImproving DiscoverabilityQuick WinsSearch Optimization