Workflow Optimizer

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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: hoangsonww
• Source: hoangsonww/Claude-Code-Agent-Monitor
• License: MIT
• Homepage: https://hoangsonww.github.io/Claude-Code-Agent-Monitor/

Install and usage instructions live in the source repository linked above.