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claude-code

Claude-Code-Agent-Monitor workflow-optimizer

install
source · Clone the upstream repo
git clone https://github.com/hoangsonww/Claude-Code-Agent-Monitor
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/hoangsonww/Claude-Code-Agent-Monitor "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/ccam-productivity/skills/workflow-optimizer" ~/.claude/skills/hoangsonww-claude-code-agent-monitor-workflow-optimizer && rm -rf "$T"
manifest: plugins/ccam-productivity/skills/workflow-optimizer/SKILL.md
source content

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

EndpointReturns
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:

DatasetContent
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:

#RecommendationSource DataImpactEffortEst. Savings

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