Multi-angle codebase exploration spawning 3-5 parallel agents for code structure, data flow, architecture patterns, and health assessment. Generates ASCII visualizations, import graphs, and design pattern detection with cross-session memory storage. Use when exploring a repo, discovering architecture, onboarding to a new codebase, or analyzing design patterns.
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Multi-angle codebase exploration spawning 3-5 parallel agents for code structure, data flow, architecture patterns, and health assessment. Generates ASCII visualizations, import graphs, and design pattern detection with cross-session memory storage. Use when exploring a repo, discovering architecture, onboarding to a new codebase, or analyzing design patterns.
{"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
Multi-angle codebase exploration using 3-5 parallel agents.
🎯 Quick Start
/ork:explore authentication
Opus 5: Exploration agents use native adaptive thinking for deeper pattern recognition across large codebases.
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. /ork:doctor warns when xhigh is requested without Opus 5.
STEP 0: Verify User Intent with AskUserQuestion
BEFORE creating tasks, clarify what the user wants to explore:
"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"
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:
Fallback: If Agent Teams encounters issues, fall back to Task tool for remaining exploration.
Model cost (CC 2.1.198+): the built-in Explore agent inherits the session model capped at Opus — it no longer runs on haiku. From a premium-model session (Opus, Fable), budget Explore fan-outs at Opus rates; there is no knob to pin the built-in Explore back to haiku. ork's own explorer agents can still pin a cheaper model via frontmatter.
🚨 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. 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.
Load Read("${CLAUDE_PLUGIN_ROOT}/skills/explore/rules/code-health-assessment.md") for agent prompt. Load Read("${CLAUDE_PLUGIN_ROOT}/skills/explore/references/code-health-rubric.md") for scoring criteria.
Phase 6: Dependency Hotspot Map
Load Read("${CLAUDE_PLUGIN_ROOT}/skills/explore/rules/dependency-hotspot-analysis.md") for agent prompt. Load Read("${CLAUDE_PLUGIN_ROOT}/skills/explore/references/dependency-analysis.md") for metrics.
Phase 7: Product Perspective
Load Read("${CLAUDE_PLUGIN_ROOT}/skills/explore/rules/product-perspective.md") for agent prompt. Load Read("${CLAUDE_PLUGIN_ROOT}/skills/explore/references/findability-patterns.md") for best practices.
Emit spec and markdown. Default — gives the human a report and downstream skills a structured handoff.
When emitting a spec:
Load the format and catalog: Read("${CLAUDE_PLUGIN_ROOT}/skills/explore/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.json with compact JSON (no indentation) — minimizes token cost for downstream consumers.
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.
After the session synthesis lands, optionally invoke scripts/post_explore_summary.py <session-dir> to auto-emit a notebook-backed summary of the exploration. Self-skips on every non-happy-path so it never breaks the run:
Auto-skip conditions (all exit 0, all WARN-logged):
Skip reason
Trigger
signal absent
len(dirs_scanned) < 3 (or field missing on explore-output.json)
yg-mcp-core not importable
yg-mcp-core>=0.3.0 not installed (orchestkit is public; yg-mcp-core lives on private pypi.yonyon.ai — HQ-only)
hq-content MCP unreachable
MCP server down OR .mcp.json doesn't define hq-content
Session dir must contain explore-output.json (with dirs_scanned: list[str], optional synthesis: str, required notebook_id: str). Handoff JSON at <session-dir>/explore-summary.json records status (fired / skipped) and summary_path on success.
Mirrors the /ork:brainstorm post-synth podcast pattern from PR #1889. Closes orchestkit#1893.
Notes for long explorations
Oversized reads (CC 2.1.144+): Read returns a [PARTIAL view] truncated first page (not a hard error) when a whole-file read exceeds the token limit. When traversing large files, detect that notice and re-read with explicit offset/limit to page through the rest — never treat the partial as the full file.
When context fills (CC 2.1.141+): Use the rewind menu's "Summarize up to here" to compress earlier turns while keeping recent context, instead of restarting. Reactive compaction (CC 2.1.142+) now sizes the first summarize to the actual overflow, so a second mid-turn pass is rare.
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, or stop after 10 turns
Stops when: codebase architecture diagram is generated and at least 5 design patterns have been classified. Compatible with claude.ai Remote Control runs.
Quality Bar
Done means all of these hold:
Every architectural or data-flow claim cites concrete evidence (file:line or a file path), not prose assertion
Code health is reported as 0-10 scores with a per-dimension breakdown, not a bare number
Dependency hotspots / coupling are named along with the files that drive them
The report includes an architecture or structure visualization for the explored scope
If a json-render spec is emitted, it passes render-spec.mjs --check; on failure fall back to markdown-only and never write a partial spec
