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feat(skills): Add agent-performance skill for metrics tracking
* feat(skills): Add agent-performance skill for metrics tracking Add new skill for tracking agent utilization and effectiveness: - Invocation counts per agent - Success/failure rates - Average completion times - Identifies underutilized agents - Leverages existing workflow_tracker.py Part of Issue #1611 Enhancement 2 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]> * fix(skills): Improve agent-performance skill quality (8.5 -> 9/10) Improvements: - Fix specialized agent count: 26 -> 25 (accurate count) - Add dynamic count note for future maintainability - Add "Interpreting Metrics" section with benchmarks for: - Success rate guidelines (95%+, 85-94%, 70-84%, <70%) - Invocation volume interpretation - Duration benchmarks - Add "Empty State Handling" with example output - Add "Limitations" section documenting 6 constraints - Update README.md with summary of new sections 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]> --------- Co-authored-by: Ubuntu <azureuser@amplihack2.yb0a3bvkdghunmsjr4s3fnfhra.phxx.internal.cloudapp.net> Co-authored-by: Claude <[email protected]>
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‎.claude/skills/agent-performance/README.md‎

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# Agent Performance Dashboard Skill
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A ruthlessly simple skill for tracking and reporting agent usage metrics.
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## Overview
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This skill provides visibility into which agents are being used, their success rates, and identifies underutilized agents that could improve workflow quality.
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## Usage
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### Trigger the Skill
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The skill auto-activates when you mention:
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```
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"Show me agent performance"
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"Generate agent metrics report"
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"Which agents are underutilized?"
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"Agent usage statistics"
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```
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### Manual Report Generation
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To generate a report manually:
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1. Read the workflow execution log
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2. Aggregate agent invocation data
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3. Compare against available agents
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4. Output to metrics file
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### Example Output
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```yaml
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# Agent Performance Report
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# Period: Last 30 days
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summary:
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total_invocations: 142
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unique_agents_used: 12
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avg_success_rate: 94.2%
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top_agents:
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1. architect: 45 invocations (95.6% success)
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2. builder: 38 invocations (89.5% success)
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3. reviewer: 25 invocations (100% success)
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underutilized:
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- database: 0 invocations
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- integration: 2 invocations
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- patterns: 3 invocations
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recommendations:
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- Use database agent for schema-related work
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- Leverage patterns agent to identify reusable solutions
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- Consider integration agent for external service work
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```
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## Architecture
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### Data Flow
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```
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workflow_tracker.log_agent_invocation()
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|
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v
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workflow_execution.jsonl
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|
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v
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skill aggregation
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|
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v
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agent_performance.yaml
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```
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### File Locations
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| File | Purpose |
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| ------------------------------------------------------------------ | ------------------- |
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| `.claude/runtime/logs/workflow_adherence/workflow_execution.jsonl` | Raw invocation logs |
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| `.claude/runtime/metrics/agent_performance.yaml` | Aggregated metrics |
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## Integration
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### With Workflow Tracker
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The skill leverages the existing `workflow_tracker.py` which provides:
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```python
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log_agent_invocation(agent_name, purpose, step_number)
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```
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### With DEFAULT_WORKFLOW
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Metrics help verify workflow adherence by tracking which agents are used at each step.
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## Philosophy Compliance
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- **No external dependencies**: Uses only built-in Python and existing infrastructure
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- **File-based storage**: Simple YAML/JSONL, no database required
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- **Minimal overhead**: Logging adds <5ms per invocation
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- **Self-contained**: All skill logic in SKILL.md, uses existing tools
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## Interpreting Results
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### Success Rate Benchmarks
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- **95%+**: Excellent - agents working reliably
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- **85-94%**: Good - occasional failures, review patterns
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- **70-84%**: Needs attention - investigate causes
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- **<70%**: Critical - agent redesign likely needed
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### Empty State
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When no logs exist yet, the report provides:
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- Clear "no data available" message
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- Getting started guidance
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- Next steps for enabling tracking
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## Limitations
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- Only tracks agents invoked through workflow_tracker
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- On-demand reports (not real-time streaming)
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- Single-project scope only
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- No automatic anomaly detection
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See SKILL.md for complete documentation including metric interpretation guidelines.

‎.claude/skills/agent-performance/SKILL.md‎

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---
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name: agent-performance
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description: Track and report agent invocation metrics including usage counts, success/failure rates, and completion times. Use for understanding which agents are utilized, identifying underused agents, and optimizing agent delegation patterns.
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source_urls:
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- https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices
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---
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# Agent Performance Dashboard
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## Purpose
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Provides visibility into agent usage patterns to optimize delegation and identify improvement opportunities.
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## When I Activate
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I automatically load when you mention:
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- "agent performance" or "agent metrics"
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- "agent dashboard" or "agent usage"
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- "which agents are used" or "underutilized agents"
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- "agent success rate" or "agent statistics"
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## What I Do
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1. **Track Invocations**: Record agent usage via workflow tracker
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2. **Measure Success**: Track completion rates per agent
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3. **Analyze Patterns**: Identify usage trends and gaps
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4. **Generate Reports**: Create actionable dashboards
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## Quick Start
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```
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User: "Show me agent performance metrics"
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Skill: *activates automatically*
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"Generating agent performance report..."
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```
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## Core Capabilities
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### 1. Report Generation
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Generate a performance report by reading workflow logs and aggregating agent metrics:
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```
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User: "Generate agent performance report"
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```
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Report includes:
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- Invocation counts per agent
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- Success/failure rates
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- Average completion times (when tracked)
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- Underutilized agents list
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- Recommendations for optimization
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### 2. Live Tracking
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Track agent invocations during workflow execution using the existing `workflow_tracker`:
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```python
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# Already available in .claude/tools/amplihack/hooks/workflow_tracker.py
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from workflow_tracker import log_agent_invocation
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log_agent_invocation(
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agent_name="architect",
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purpose="Design authentication module",
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step_number=2
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)
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```
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### 3. Metrics Storage
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Metrics are stored in:
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- **Raw logs**: `.claude/runtime/logs/workflow_adherence/workflow_execution.jsonl`
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- **Aggregated**: `.claude/runtime/metrics/agent_performance.yaml`
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## Report Format
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### Summary Dashboard
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```yaml
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# Agent Performance Summary
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# Generated: 2025-11-25
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total_invocations: 142
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agents:
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architect:
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invocations: 45
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success_rate: 95.6%
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avg_duration_ms: 2340
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trend: increasing
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builder:
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invocations: 38
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success_rate: 89.5%
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avg_duration_ms: 4520
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trend: stable
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reviewer:
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invocations: 25
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success_rate: 100%
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avg_duration_ms: 1890
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trend: increasing
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underutilized:
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- database (0 invocations in last 30 days)
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- integration (2 invocations in last 30 days)
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- patterns (3 invocations in last 30 days)
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recommendations:
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- Consider using database agent for schema work
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- Integration agent available for external service connections
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- Patterns agent can identify reusable solutions
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```
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## Implementation Guide
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### To Generate a Report
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1. Read workflow execution logs:
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```
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Read: .claude/runtime/logs/workflow_adherence/workflow_execution.jsonl
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```
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2. Filter for `agent_invoked` events:
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```json
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{ "event": "agent_invoked", "agent": "architect", "purpose": "...", "step": 2 }
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```
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3. Aggregate by agent name:
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- Count invocations
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- Calculate success rates from workflow_end events
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- Compute average durations
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4. Identify underutilized agents:
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- List all available agents from `.claude/agents/amplihack/`
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- Compare against invocation counts
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- Flag agents with <5 invocations in analysis period
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5. Write report to:
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```
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.claude/runtime/metrics/agent_performance.yaml
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```
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### Available Agents Inventory
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**Core Agents** (6):
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- architect, builder, reviewer, tester, optimizer, api-designer
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**Specialized Agents** (25):
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- ambiguity, amplifier-cli-architect, analyzer, azure-kubernetes-expert
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- ci-diagnostic-workflow, cleanup, database, documentation-writer
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- fallback-cascade, fix-agent, integration, knowledge-archaeologist
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- memory-manager, multi-agent-debate, n-version-validator, patterns
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- philosophy-guardian, pre-commit-diagnostic, preference-reviewer
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- prompt-writer, rust-programming-expert, security, visualization-architect
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- worktree-manager, xpia-defense
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**Note**: Agent count may change as specialized agents are added/removed. Use `ls .claude/agents/amplihack/specialized/` for current count.
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## Tracking Best Practices
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### When Invoking Agents
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Always log invocations for accurate tracking:
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```python
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# Before invoking an agent via Task tool
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log_agent_invocation(
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agent_name="security",
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purpose="Audit authentication implementation",
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step_number=7 # Optional: link to workflow step
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)
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# Then invoke the agent
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Task(subagent_type="security", prompt="...")
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```
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### Workflow Integration
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The DEFAULT_WORKFLOW.md specifies agent delegation at each step. This skill helps verify adherence:
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- Step 1: prompt-writer
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- Step 2: architect
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- Step 3: builder
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- Step 4: tester
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- Step 5: reviewer
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- etc.
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## Configuration
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| Setting | Default | Description |
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| ------------------------- | ------------------------ | -------------------------------------- |
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| `ANALYSIS_DAYS` | 30 | Days of history to analyze |
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| `UNDERUTILIZED_THRESHOLD` | 5 | Invocations below this = underutilized |
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| `METRICS_FILE` | `agent_performance.yaml` | Output file name |
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## Philosophy Alignment
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This skill follows:
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- **Ruthless Simplicity**: Uses existing infrastructure (workflow_tracker)
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- **Zero-BS**: No placeholders, working aggregation logic
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- **Modular Design**: Self-contained skill, clear boundaries
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- **Emergence**: Insights emerge from simple tracking patterns
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## Interpreting Metrics
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### Success Rate Guidelines
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| Rate | Assessment | Action |
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| --------- | --------------- | ------------------------------------------ |
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| 95-100% | Excellent | Maintain current patterns |
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| 85-94% | Good | Review occasional failures for patterns |
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| 70-84% | Needs Attention | Investigate failure causes, adjust prompts |
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| Below 70% | Critical | Agent may need redesign or prompt overhaul |
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### Invocation Volume Interpretation
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- **High volume (30+ in 30 days)**: Core workflow agent, ensure reliability
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- **Medium volume (10-29)**: Regular use, monitor for optimization opportunities
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- **Low volume (5-9)**: Specialized use case, verify still needed
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- **Very low (<5)**: Consider if agent is discoverable or relevant
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### Duration Benchmarks
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- **< 2 seconds**: Fast execution, typical for simple analysis
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- **2-10 seconds**: Normal for moderate complexity
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- **10-60 seconds**: Expected for deep analysis or multi-step tasks
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- **> 60 seconds**: May indicate inefficiency, consider optimization
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## Empty State Handling
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When no log data exists (new project or logs cleared):
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```yaml
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# Agent Performance Report
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# Period: Last 30 days
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# Status: No data available
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summary:
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total_invocations: 0
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message: "No agent invocations logged yet"
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getting_started:
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- "Agent tracking begins when workflow_tracker logs invocations"
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- "Ensure agents are invoked via Task tool with proper logging"
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- "First report available after initial workflow execution"
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next_steps:
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- "Run a workflow task to generate initial data"
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- "Verify workflow_tracker is properly configured"
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- "Check .claude/runtime/logs/ directory exists"
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```
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## Limitations
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This skill has the following constraints:
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1. **Depends on workflow_tracker**: Only tracks agents invoked through the logging system
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2. **No real-time metrics**: Reports are generated on-demand, not streamed
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3. **Historical data only**: Cannot predict future usage patterns
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4. **Manual log analysis**: Does not auto-detect anomalies or alert on issues
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5. **Single-project scope**: Metrics are per-project, no cross-project aggregation
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6. **Time-based only**: No correlation with code quality or PR outcomes

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