/workflow-optimizer

name: workflow-optimizer
description: >
  Analyze workflow patterns using the Agent Monitor's workflow intelligence
  API — orchestration DAGs, tool flow transitions, subagent effectiveness,
  model delegation patterns, error propagation by depth, concurrency lanes,
  compaction impact, and agent co-occurrence. Produces prioritized optimization
  recommendations with quantified impact.

Workflow Optimizer

Analyze Claude Code workflows using the Agent Monitor's workflow intelligence engine.

Input

The user provides: **$ARGUMENTS**

Options: "analyze", a session ID for single-session analysis, or a focus: "tools", "subagents", "cost", "errors".

Data Sources

| Endpoint | Returns | |----------|---------| | `GET /api/sessions?limit=100` | Session list with metadata | | `GET /api/workflows/{sessionId}` | **11 workflow datasets** (see below) | | `GET /api/analytics` | Tool usage top 20, event types, agent types | | `GET /api/pricing` | Model pricing rules for cost comparison |

Workflow Intelligence API (`GET /api/workflows/{sessionId}`)

Returns these 11 datasets per session:

| Dataset | Content | |---------|---------| | `stats` | Aggregate session stats: tool count, agent depth, event count | | `orchestration` | **DAG**: agent nodes with parent/child edges, depths, types | | `toolFlow` | **Transition matrix**: tool A → tool B with counts (common sequences) | | `effectiveness` | **Subagent success**: per-type completion rates, avg duration, task success | | `patterns` | **Recurring sequences**: detected workflow patterns with frequency | | `modelDelegation` | **Model choices**: which models are delegated which tasks | | `errorPropagation` | **Error flow by depth**: where in the agent tree errors originate and propagate | | `concurrency` | **Concurrency lanes**: overlapping agent execution timelines | | `complexity` | **Complexity score**: numerical score based on depth, breadth, tool diversity | | `compaction` | **Compaction impact**: token savings, frequency, context health | | `cooccurrence` | **Agent pairs**: which agents frequently run together |

Optimization Analyses

1. Tool Flow Optimization

From `toolFlow` transition data:

• Identify the most common tool sequences (e.g., Read → Edit → Bash)
• Find redundant transitions (same tool called repeatedly = retries)
• Detect anti-patterns: high-frequency failure loops
• Recommend tool chain shortcuts

2. Subagent Strategy

From `effectiveness` + `orchestration`:

• Which subagent types (task, explore, code-review) have highest completion rates
• Average duration per subagent type — are subagents taking too long?
• Underutilized types: tasks that could benefit from delegation
• Over-spawning: too many subagents for simple tasks

3. Model Delegation Analysis

From `modelDelegation`:

• Which models handle which task types
• Cost-per-task comparison across models
• Opportunities to delegate simple tasks to cheaper models (Haiku/Sonnet instead of Opus)
• Calculate estimated savings from model rebalancing

4. Error Prevention

From `errorPropagation`:

• Where errors originate (agent depth level)
• How errors cascade to parent agents
• Error types (APIError, tool failure) by frequency
• Defensive strategies: which patterns lead to fewer errors

5. Concurrency Optimization

From `concurrency`:

• Which agents run in parallel vs sequential
• Bottlenecks: sequential agents that could be parallelized
• Resource contention: overlapping heavy tasks

6. Context Health

From `compaction`:

• How often compaction occurs per session
• Token recovery from compaction baselines
• Sessions that hit context limits — suggest breaking into smaller tasks

Output

Prioritized recommendations table:

| # | Recommendation | Source Data | Impact | Effort | Est. Savings | |---|---------------|-------------|--------|--------|-------------|

Top 5 recommendations with detailed explanation, supporting data from the workflow API, and implementation steps.