/explore

name: explore
license: MIT
compatibility: "Claude Code 2.1.277+. Requires memory MCP server."
description: "Multi-angle codebase exploration by parallel agents: structure, data flow, architecture, health. Use to map an unfamiliar repo. NOT for task research files (use create-research)."
argument-hint: "[topic-or-feature] [--render=markdown|json-render|both] [--effort=low|medium|high]"
context: fork
# user-typed commands stay interactive; CC >= 2.1.218 backgrounds forks by default (#3093)
background: false
user-invocable: true
allowed-tools: "AskUserQuestion Read Write Grep Glob Agent TaskCreate TaskUpdate TaskStop mcp__memory__search_nodes Bash ToolSearch"
skills: [glyph, architecture-decision-record, memory, architecture-patterns, chain-patterns]
effort: high
model: sonnet
metadata:
  category: workflow-automation
  mcp-server: memory
  version: "2.6.0"
  author: "OrchestKit"
  complexity: "high"
  tags: "exploration, code-search, architecture, codebase, health-assessment"
triggers:
  keywords: [explore, "how does", "walk me through", "big picture", architecture, "trace the", "where is", "find all files", codebase]
  examples:
    - "how does the auth system work in this codebase"
    - "what is the architecture of this repo"
    - "i just joined this project, walk me through the structure"
  anti-triggers: [fix, implement, build, commit, review, assess]
paths: ["src/**", "package.json", "CLAUDE.md"]

Codebase Exploration

Host-neutral workflow. Invoke by skill name (`explore`). Claude Code slash routing, YAML hook loaders, and `.claude/chain` live in `references/claude-code.md`.

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

🎯 Quick Start

explore authentication

> **Opus 5.5**: 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 5) | 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. `doctor` warns only when `xhigh` is configured on a model in its `XHIGH_UNSUPPORTED_PREFIXES` table.

---

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": "Quick scan", "description": "Find relevant files + structure, skip deep analysis"},
      {"label": "Data flow", "description": "Trace how data moves through the system"},
      {"label": "Architecture patterns", "description": "Identify design patterns and integrations"}
    ],
    "multiSelect": false
  }]
)

**Based on answer, adjust workflow:**

• **Full exploration**: All phases, all parallel agents
• **Quick scan**: Files + structure only (phases 1-2), skip health/deps/product — no deep agents
• **Data flow**: Focus phase 3 agents on data tracing
• **Architecture patterns**: Focus on backend-system-architect agent

---

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 **Agent tool** (star):

1. Agent Teams mode (GA since CC 2.1.33) → **recommended for 4+ agents** 2. Agent tool mode → **for quick/single-focus exploration** 3. `ORCHESTKIT_FORCE_TASK_TOOL=1` → **Agent tool** (override)

| Aspect | Agent 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 Agent tool for remaining exploration.

> **Model pin:** Set `model="haiku"` on the built-in Explore calls in Agent tool mode. This trial covers that path only. Agent Teams calls keep their existing model selection. A fork uses the parent model and shared context.

---

🚨 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
TaskCr