Codebase Exploration

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

Quick Start

bash

/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:

python

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 8 phases, all parallel agents
Code structure only: Skip phases 4-6 (health, dependencies, product)
Data flow: Focus phase 3 agents on data tracing
Architecture patterns: Focus on backend-system-architect agent
Quick search: Skip to phase 1-2 only, return file list

STEP 0b: Select Orchestration Mode

Choose Agent Teams (mesh — explorers share discoveries) or Task tool (star — all report to lead):

```
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1
```
→ Agent Teams mode
Agent Teams unavailable → Task tool mode (default)
Otherwise: Full exploration with 4+ agents → recommend Agent Teams; Quick search or single-focus → Task tool

Aspect	Task Tool	Agent Teams
Discovery sharing	Lead synthesizes after all complete	Explorers share discoveries as they go
Cross-referencing	Lead connects dots	Data flow explorer alerts architecture explorer
Cost	~150K tokens	~400K tokens
Best for	Quick/focused searches	Deep full-codebase exploration

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

⚠️ CRITICAL: Task Management is MANDATORY (CC 2.1.16)

BEFORE doing ANYTHING else, create tasks to show progress:

python

# 1. Create main exploration task IMMEDIATELY
TaskCreate(
  subject="Explore: {topic}",
  description="Deep codebase exploration for {topic}",
  activeForm="Exploring {topic}"
)

# 2. Create subtasks for phases (8-phase process)
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")

# 3. Update status as you progress
TaskUpdate(taskId="2", status="in_progress")  # When starting
TaskUpdate(taskId="2", status="completed")    # When done

Workflow Overview

Phase	Activities	Output
1. Initial Search	Grep, Glob for matches	File locations
2. Memory Check	Search knowledge graph	Prior context
3. Deep Exploration	4 parallel explorers	Multi-angle analysis
4. Code Health Assessment	Rate found code 0-10	Quality scores
5. Dependency Hotspot Map	Identify coupling	Hotspot visualization
6. Product Perspective	Business context	Findability suggestions
7. Report Generation	Compile findings	Actionable report

Phase 1: Initial Search

python

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

Phase 2: Memory Check

python

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

Phase 3: Parallel Deep Exploration (4 Agents)

Launch 4 specialized explorers in ONE message with

run_in_background: true

python

# 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:

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

Phase 3 — Agent Teams Alternative

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

python

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:

python

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 Phase 3 Task spawns above.

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 (NEW)

Goal: Rate found code quality 0-10 with specific dimensions.

python

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
)

Phase 6: Dependency Hotspot Map (NEW)

Goal: Identify highly-coupled code and dependency bottlenecks.

python

# Analyze imports and dependencies
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
)

Phase 7: Product Perspective Agent (NEW)

Goal: Add business context and findability suggestions.

python

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
)

Phase 8: Generate Report

markdown

# 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]

Common Exploration Queries

"How does authentication work?"
"Where are API endpoints defined?"
"Find all usages of EventBroadcaster"
"What's the workflow for content analysis?"

Related Skills

implement: Implement after exploration

Version: 2.1.0 (February 2026)

explore

NPX Install

Tags

SKILL.md Content

Codebase Exploration

Quick Start

STEP 0: Verify User Intent with AskUserQuestion

STEP 0b: Select Orchestration Mode

⚠️ CRITICAL: Task Management is MANDATORY (CC 2.1.16)

Workflow Overview

Phase 1: Initial Search

Phase 2: Memory Check

Phase 3: Parallel Deep Exploration (4 Agents)

Phase 3 — Agent Teams Alternative

Phase 4: AI System Exploration (If Applicable)

Phase 5: Code Health Assessment (NEW)

Phase 6: Dependency Hotspot Map (NEW)

Phase 7: Product Perspective Agent (NEW)

Phase 8: Generate Report

Common Exploration Queries

Related Skills