feat: Claude Code Monitor — lanes, pipelines and a merged workspace

Internal SmartGift build of a Claude Code monitoring dashboard. Lanes: a durable unit of parallel agent work, one per working directory, tracked across session restarts. Managed lanes are git worktrees the dashboard provisions and can reset or remove behind a three-check destroy guard and a counted preflight; adopted lanes are directories you already own and are never destroyable. Pipelines: a lane moves through pipeline stages. A stage the agent declares with evidence renders green; a stage inferred from the tool-event stream renders dashed amber and never counts as done. Detection is forward-only within a 30-minute window, and never writes the declared stage. Workspace: one page at /run with a lane grid, the selected lane's pipeline, and a full Claude console behind a disclosure.
2026-07-29 17:07:45 +07:00
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+---
+description: >
+  Detect recurring patterns using the Agent Monitor's workflow intelligence —
+  toolFlow transitions (tool A → B frequency matrices), recurring workflow
+  patterns, agent co-occurrence pairs, model delegation habits, error
+  propagation paths by agent depth, and compaction triggers. Use to discover
+  habitual usage patterns and anti-patterns.
+---
+
+# Pattern Detect
+
+Identify recurring patterns using the Agent Monitor's workflow intelligence engine.
+
+## Input
+
+The user provides: **$ARGUMENTS**
+
+Options: "all", "tools", "errors", "workflows", "last N sessions".
+
+## Data Sources
+
+| Endpoint | Returns |
+|----------|---------|
+| `GET /api/sessions?limit=200` | Session list with status, model, cwd, metadata |
+| `GET /api/analytics` | tool_usage top 20, event_types, agent_types |
+| `GET /api/workflows/{sessionId}` | 11 datasets per session (see below) |
+
+### Workflow datasets used for pattern detection
+
+| Dataset | Pattern insight |
+|---------|----------------|
+| `toolFlow` | **Tool transition matrix**: tool A → tool B with counts — reveals sequential habits |
+| `patterns` | **Detected workflow patterns**: recurring sequences with frequency scores |
+| `cooccurrence` | **Agent co-occurrence**: which agents frequently run together |
+| `modelDelegation` | **Model habits**: which models are chosen for which task types |
+| `errorPropagation` | **Error patterns**: where errors start and how they cascade by agent depth |
+| `effectiveness` | **Subagent patterns**: which types succeed most, avg duration per type |
+| `compaction` | **Compaction triggers**: what causes context overflow |
+| `complexity` | **Complexity patterns**: session complexity scores over time |
+
+## Pattern Categories
+
+### 1. Tool Chain Patterns (from `toolFlow`)
+- **Most common sequences**: Top 10 tool transitions (e.g., Read → Edit: 145 times)
+- **Starter tools**: First tool used in sessions (indicates task type)
+- **Finisher tools**: Last tool before Stop event
+- **Anti-patterns**: Tool → same Tool repeated (retries/failures)
+- **Co-occurrence**: Tools that always appear together in sessions
+
+### 2. Workflow Patterns (from `patterns`)
+- **Named patterns**: Workflow sequences the API has detected with frequency
+- **Session archetypes**: Common session shapes (short edit, long debug, subagent-heavy)
+- **Project-specific**: Patterns that appear in specific working directories
+
+### 3. Error Patterns (from `errorPropagation` + `event_types`)
+- **Error origins**: Which agent depth level produces most errors
+- **Cascade patterns**: Errors that trigger chains of follow-up errors
+- **APIError frequency**: quota hits, rate_limit, overloaded — by time of day
+- **Recovery patterns**: How errors are typically resolved (tool retry vs agent switch)
+
+### 4. Agent Patterns (from `cooccurrence` + `effectiveness`)
+- **Agent pairs**: Which agents are spawned together frequently
+- **Delegation patterns**: Main agent → subagent task delegation habits
+- **Success by type**: Which subagent types (task/explore/code-review) work best for which tasks
+
+### 5. Temporal Patterns (from session timestamps + `daily_sessions`)
+- **Peak hours**: When sessions cluster
+- **Duration patterns**: Short vs long session distribution
+- **Day-of-week trends**: Productive days vs quiet days
+
+## Output
+
+**Pattern Report** with top 10 patterns ranked by frequency × impact:
+- Pattern name and description
+- Frequency (occurrences across analyzed sessions)
+- Impact: positive (reinforce), negative (eliminate), or neutral (observe)
+- Actionable recommendation for each