refactor!: rebrand CCAM to AgentHub
Clean break, no compatibility alias. Renames the product name, the CLI binary (`ccam` -> `agenthub`), the env var prefix (`CCAM_` -> `AGENTHUB_`), the Prometheus metric namespace (`ccam_*` -> `agenthub_*`), the tmux session prefix (`ccam-lane-<id>` -> `agenthub-lane-<id>`), the per-repo runtime profile directory (`.ccam/profile` -> `.agenthub/profile`), the lanes state root (`~/.claude/ccam-lanes` -> `~/.claude/agenthub-lanes`), the bundled plugins (`plugins/ccam*` -> `plugins/agenthub*`) and their slash commands (`/ccam-doctor` -> `/agenthub-doctor`, `/ccam:*` -> `/agenthub:*`). Because there is no alias, an existing install has to be updated by hand: rename its `.ccam/profile` directory and any `CCAM_*` variables, and reinstall the plugin under its new name. Two other name families are deliberately left alone here, since they are not just strings: the `Claude-Code-Monitor` repository/marketplace name, and the `agent-dashboard` npm package plus its `~/.claude/agent-dashboard` runtime directory, which holds the live database and needs a migration rather than a rename. Verified: server 1406/1406, client 459/459, `grep -i ccam` clean across tracked files, `openapi.yaml` regenerated (95 paths, unchanged count).
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---
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description: >
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Analyze workflow patterns using the Agent Monitor's workflow intelligence
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API — orchestration DAGs, tool flow transitions, subagent effectiveness,
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model delegation patterns, error propagation by depth, concurrency lanes,
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compaction impact, and agent co-occurrence. Produces prioritized optimization
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recommendations with quantified impact.
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---
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# Workflow Optimizer
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Analyze Claude Code workflows using the Agent Monitor's workflow intelligence engine.
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## Input
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The user provides: **$ARGUMENTS**
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Options: "analyze", a session ID for single-session analysis, or a focus: "tools", "subagents", "cost", "errors".
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## Data Sources
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| Endpoint | Returns |
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|----------|---------|
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| `GET /api/sessions?limit=100` | Session list with metadata |
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| `GET /api/workflows/{sessionId}` | **11 workflow datasets** (see below) |
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| `GET /api/analytics` | Tool usage top 20, event types, agent types |
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| `GET /api/pricing` | Model pricing rules for cost comparison |
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### Workflow Intelligence API (`GET /api/workflows/{sessionId}`)
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Returns these 11 datasets per session:
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| Dataset | Content |
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|---------|---------|
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| `stats` | Aggregate session stats: tool count, agent depth, event count |
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| `orchestration` | **DAG**: agent nodes with parent/child edges, depths, types |
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| `toolFlow` | **Transition matrix**: tool A → tool B with counts (common sequences) |
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| `effectiveness` | **Subagent success**: per-type completion rates, avg duration, task success |
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| `patterns` | **Recurring sequences**: detected workflow patterns with frequency |
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| `modelDelegation` | **Model choices**: which models are delegated which tasks |
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| `errorPropagation` | **Error flow by depth**: where in the agent tree errors originate and propagate |
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| `concurrency` | **Concurrency lanes**: overlapping agent execution timelines |
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| `complexity` | **Complexity score**: numerical score based on depth, breadth, tool diversity |
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| `compaction` | **Compaction impact**: token savings, frequency, context health |
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| `cooccurrence` | **Agent pairs**: which agents frequently run together |
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## Optimization Analyses
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### 1. Tool Flow Optimization
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From `toolFlow` transition data:
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- Identify the most common tool sequences (e.g., Read → Edit → Bash)
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- Find redundant transitions (same tool called repeatedly = retries)
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- Detect anti-patterns: high-frequency failure loops
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- Recommend tool chain shortcuts
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### 2. Subagent Strategy
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From `effectiveness` + `orchestration`:
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- Which subagent types (task, explore, code-review) have highest completion rates
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- Average duration per subagent type — are subagents taking too long?
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- Underutilized types: tasks that could benefit from delegation
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- Over-spawning: too many subagents for simple tasks
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### 3. Model Delegation Analysis
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From `modelDelegation`:
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- Which models handle which task types
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- Cost-per-task comparison across models
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- Opportunities to delegate simple tasks to cheaper models (Haiku/Sonnet instead of Opus)
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- Calculate estimated savings from model rebalancing
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### 4. Error Prevention
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From `errorPropagation`:
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- Where errors originate (agent depth level)
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- How errors cascade to parent agents
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- Error types (APIError, tool failure) by frequency
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- Defensive strategies: which patterns lead to fewer errors
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### 5. Concurrency Optimization
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From `concurrency`:
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- Which agents run in parallel vs sequential
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- Bottlenecks: sequential agents that could be parallelized
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- Resource contention: overlapping heavy tasks
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### 6. Context Health
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From `compaction`:
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- How often compaction occurs per session
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- Token recovery from compaction baselines
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- Sessions that hit context limits — suggest breaking into smaller tasks
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## Output
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Prioritized recommendations table:
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| # | Recommendation | Source Data | Impact | Effort | Est. Savings |
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|---|---------------|-------------|--------|--------|-------------|
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Top 5 recommendations with detailed explanation, supporting data from the workflow API, and implementation steps.
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