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 -0.5: Effort-Aware Agent Scaling (CC 2.1.120+)
Read ${CLAUDE_EFFORT} to scale exploration depth before any other decision.
# CC 2.1.120+ env var; explicit --effort= overrides
EFFORT = os.environ.get("CLAUDE_EFFORT")
for token in "$ARGUMENTS".split():
if token.startswith("--effort="):
EFFORT = token.split("=", 1)[1]
EFFORT = EFFORT or "high" # default
| Effort | Agent count | Phases | Time |
|--------|-------------|--------|------|
| low | 1 (structure-only) | 1, 2, 8 | ~1 min |
| medium | 2 (structure + data flow) | 1, 2, 3 (subset), 8 | ~3 min |
| high (default) | 4 (full parallel team) | 1–8 | ~6 min |
| xhigh (Opus 4.7 only) | 5 (+ uncertainty pass on health scores) | 1–8 + caveats | ~8 min |
Override gate: if the user passes --effort=high explicitly while ${CLAUDE_EFFORT} is low, the flag wins. /ork:doctor warns when xhigh is requested without Opus 4.7.
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", "markdown": "```\nFull Exploration (8 phases)\n───────────────────────────\n 4 parallel explorer agents:\n ┌──────────┐ ┌──────────┐\n │ Structure│ │ Data │\n │ Explorer │ │ Flow │\n ├──────────┤ ├──────────┤\n │ Pattern │ │ Product │\n │ Analyst │ │ Context │\n └──────────┘ └──────────┘\n ▼\n ┌──────────────────────┐\n │ Code Health N/10 │\n │ Dep Hotspots map │\n │ Architecture diag │\n └──────────────────────┘\n Output: Full exploration report\n```"},
{"label": "Code structure only", "description": "Find files, classes, functions related to topic", "markdown": "```\nCode Structure\n──────────────\n Grep ──▶ Glob ──▶ Map\n\n Output:\n ├── File tree (relevant)\n ├── Key classes/functions\n ├── Import graph\n └── Entry points\n No agents — direct search\n```"},
{"label": "Data flow", "description": "Trace how data moves through the system", "markdown": "```\nData Flow Trace\n───────────────\n Input ──▶ Transform ──▶ Output\n │ │ │\n ▼ ▼ ▼\n [API] [Service] [DB/Cache]\n\n Traces: request lifecycle,\n state mutations, side effects\n Agent: 1 data-flow explorer\n```"},
{"label": "Architecture patterns", "description": "Identify design patterns and integrations", "markdown": "```\nArchitecture Analysis\n─────────────────────\n ┌─────────────────────┐\n │ Detected Patterns │\n │ ├── MVC / Hexagonal │\n │ ├── Event-driven? │\n │ ├── Service layers │\n │ └── External APIs │\n ├─────────────────────┤\n │ Integration Map │\n │ DB ←→ Cache ←→ Queue │\n └─────────────────────┘\n Agent: backend-system-architect\n```"},
{"label": "Quick search", "description": "Just find relevant files, skip deep analysis", "markdown": "```\nQuick Search (~30s)\n───────────────────\n Grep + Glob ──▶ File list\n\n Output:\n ├── Matching files\n ├── Line references\n └── Brief summary\n No agents, no health check,\n no report generation\n```"}
],
"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
MCP Probe
# memory is alwaysLoad in .mcp.json (CC 2.1.121+, #1541) — probe below kept as fallback for older CC:
ToolSearch(query="select:mcp__memory__search_nodes")
Write(".claude/chain/capabilities.json", { memory, timestamp })
if capabilities.memory:
mcp__memory__search_nodes({ query: "architecture decisions for {path}" })
# Enrich exploration with past decisions
Exploration Handoff
After exploration completes, write results for downstream skills:
Write(".claude/chain/exploration.json", JSON.stringify({
"phase": "explore", "skill": "explore",
"timestamp": now(), "status": "completed",
"outputs": {
"architecture_map": { ... },
"patterns_found": ["repository", "service-layer"],
"complexity_hotspots": ["src/auth/", "src/payments/"]
}
}))
Choose Agent Teams (mesh) or Task tool (star):
- Agent Teams mode (GA since CC 2.1.33) → recommended for 4+ agents
- Task tool mode → for quick/single-focus exploration
ORCHESTKIT_FORCE_TASK_TOOL=1→ Task tool (override)
| 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.
Task Management (MANDATORY)
BEFORE doing ANYTHING else, create tasks to show progress:
# 1. Create main task IMMEDIATELY
TaskCreate(subject="Explore: {topic}", description="Deep codebase exploration for {topic}", activeForm="Exploring {topic}")
# 2. Create subtasks for each phase
TaskCreate(subject="Initial file search", activeForm="Searching files") # id=2
TaskCreate(subject="Check knowledge graph", activeForm="Checking memory") # id=3
TaskCreate(subject="Launch exploration agents", activeForm="Dispatching explorers") # id=4
TaskCreate(subject="Assess code health (0-10)", activeForm="Assessing code health") # id=5
TaskCreate(subject="Map dependency hotspots", activeForm="Mapping dependencies") # id=6
TaskCreate(subject="Add product perspective", activeForm="Adding product context") # id=7
TaskCreate(subject="Generate exploration report", activeForm="Generating report") # id=8
# 3. Set dependencies for sequential phases
TaskUpdate(taskId="3", addBlockedBy=["2"]) # Memory check needs file search first
TaskUpdate(taskId="4", addBlockedBy=["3"]) # Agents need memory context
TaskUpdate(taskId="5", addBlockedBy=["4"]) # Health needs exploration done
TaskUpdate(taskId="6", addBlockedBy=["4"]) # Hotspots need exploration done
TaskUpdate(taskId="7", addBlockedBy=["4"]) # Product needs exploration done
TaskUpdate(taskId="8", addBlockedBy=["5", "6", "7"]) # Report needs all analysis done
# 4. Before starting each task, verify it's unblocked
task = TaskGet(taskId="2") # Verify blockedBy is empty
# 5. Update status as you progress
TaskUpdate(taskId="2", status="in_progress") # When starting
TaskUpdate(taskId="2", status="completed") # When done — repeat for each subtask
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. AI System (if applicable) | LangGraph, prompts, RAG | AI-specific findings | | 5. Code Health | Rate code 0-10 | Quality scores | | 6. Dependency Hotspots | Identify coupling | Hotspot visualization | | 7. Product Perspective | Business context | Findability suggestions | | 8. Report Generation | Compile findings | Actionable report |
Progressive Output (CC 2.1.76)
Output findings incrementally as each phase completes — don't batch until the report:
| After Phase | Show User | |-------------|-----------| | 1. Initial Search | File matches, grep results | | 2. Memory Check | Prior decisions and relevant context | | 3. Deep Exploration | Each explorer agent's findings as they return | | 5. Code Health | Health score with dimension breakdown |
For Phase 3 parallel agents, output each agent's findings as soon as it returns — don't wait for all 4 explorers. Early findings from one agent may answer the user's question before remaining agents complete, allowing early termination.
Phase 1: Initial Search
# PARALLEL - Quick searches
Grep(pattern="$ARGUMENTS[0]", output_mode="files_with_matches")
Glob(pattern="**/*$ARGUMENTS[0]*")
Phase 2: Memory Check
mcp__memory__search_nodes(query="$ARGUMENTS[0]")
mcp__memory__search_nodes(query="architecture")
Phase 3: Parallel Deep Exploration (4 Agents)
Load Read("${CLAUDE_SKILL_DIR}/rules/exploration-agents.md") for Task tool mode prompts.
Load Read("${CLAUDE_SKILL_DIR}/rules/agent-teams-mode.md") 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
Load Read("${CLAUDE_SKILL_DIR}/rules/code-health-assessment.md") for agent prompt. Load Read("${CLAUDE_SKILL_DIR}/references/code-health-rubric.md") for scoring criteria.
Phase 6: Dependency Hotspot Map
Load Read("${CLAUDE_SKILL_DIR}/rules/dependency-hotspot-analysis.md") for agent prompt. Load Read("${CLAUDE_SKILL_DIR}/references/dependency-analysis.md") for metrics.
Phase 7: Product Perspective
Load Read("${CLAUDE_SKILL_DIR}/rules/product-perspective.md") for agent prompt. Load Read("${CLAUDE_SKILL_DIR}/references/findability-patterns.md") for best practices.
Phase 8: Generate Report
Load Read("${CLAUDE_SKILL_DIR}/references/exploration-report-template.md").
Phase 8b: Emit Dashboard Spec (json-render)
Parse --render= from $ARGUMENTS. Default is both.
| Mode | Behavior |
|------|----------|
| markdown | Current behavior — markdown report only. No spec emitted. |
| json-render | Emit .claude/chain/explore-dashboard.json only. Skip markdown report. |
| both | Emit spec and markdown. Default — gives the human a report and downstream skills a structured handoff. |
When emitting a spec:
- Load the format and catalog:
Read("${CLAUDE_SKILL_DIR}/references/dashboard-spec.md"). Reference example:references/dashboard-example.json. - Build the spec object using only catalog component types:
Card,StatGrid,DataTable,StatusBadge,BarMeter,Heatmap,Markdown. - Write to
.claude/chain/explore-dashboard.jsonwith compact JSON (no indentation) — minimizes token cost for downstream consumers. - Validate before declaring success:
node "${CLAUDE_SKILL_DIR}/scripts/render-spec.mjs" .claude/chain/explore-dashboard.json --check
If validation fails (exit ≠ 0), do not emit — fall back to markdown-only and surface the error to the user. Never write a partial or invalid spec.
- For
--render=both, render the markdown view from the spec for consistency:
node "${CLAUDE_SKILL_DIR}/scripts/render-spec.mjs" .claude/chain/explore-dashboard.json
Pipe the output into the user-facing markdown report (or use it as-is). This guarantees the JSON spec and markdown report stay in sync — a single source of truth.
Why this matters: Downstream skills (/ork:fix-issue, /ork:implement, /ork:create-pr) parse .claude/chain/explore-dashboard.json directly instead of re-reading 3000-token markdown. Measured: spec ≈ 580 tokens for the same content. Backwards-compatible: old chained workflows that read markdown keep working in both mode.
Common Exploration Queries
- "How does authentication work?"
- "Where are API endpoints defined?"
- "Find all usages of EventBroadcaster"
- "What's the workflow for content analysis?"
Running unattended with /goal
Set a completion condition with /goal (CC 2.1.139+) and this skill will keep working across turns until the condition is met. Works in interactive, -p, and Remote Control. The overlay panel shows live elapsed / turns / tokens.
Example completion condition for this skill:
/goal until report.has_architecture_diagram AND patterns.detected_count >= 5
Stops when: codebase architecture diagram is generated and at least 5 design patterns have been classified. Compatible with claude.ai Remote Control runs.
Related Skills
ork:implement: Implement after exploration
Version: 2.6.0 (April 2026) — ${CLAUDE_EFFORT} env var scales agent count (CC 2.1.120, #1540)