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pattern-detect

Detect recurring patterns using the Agent Monitor's workflow intelligence — toolFlow transitions (tool A → B frequency matrices), recurring workflow patterns, agent co-occurrence pairs, model delegation habits, error propagation paths by agent depth, and compaction triggers. Use to discover habitual usage patterns and anti-patterns.

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  • SKILL.md3.5 KB
  • agents/openai.yaml269 B

SKILL.md(원문)

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Pattern Detect

Identify recurring patterns using the Agent Monitor's workflow intelligence engine.

Input

The user provides: $ARGUMENTS

Options: "all", "tools", "errors", "workflows", "last N sessions".

Data Sources

EndpointReturns
GET /api/sessions?limit=200Session list with status, model, cwd, metadata
GET /api/analyticstool_usage top 20, event_types, agent_types
GET /api/workflows/{sessionId}11 datasets per session (see below)

Workflow datasets used for pattern detection

DatasetPattern insight
toolFlowTool transition matrix: tool A → tool B with counts — reveals sequential habits
patternsDetected workflow patterns: recurring sequences with frequency scores
cooccurrenceAgent co-occurrence: which agents frequently run together
modelDelegationModel habits: which models are chosen for which task types
errorPropagationError patterns: where errors start and how they cascade by agent depth
effectivenessSubagent patterns: which types succeed most, avg duration per type
compactionCompaction triggers: what causes context overflow
complexityComplexity patterns: session complexity scores over time

Pattern Categories

1. Tool Chain Patterns (from toolFlow)

  • Most common sequences: Top 10 tool transitions (e.g., Read → Edit: 145 times)
  • Starter tools: First tool used in sessions (indicates task type)
  • Finisher tools: Last tool before Stop event
  • Anti-patterns: Tool → same Tool repeated (retries/failures)
  • Co-occurrence: Tools that always appear together in sessions

2. Workflow Patterns (from patterns)

  • Named patterns: Workflow sequences the API has detected with frequency
  • Session archetypes: Common session shapes (short edit, long debug, subagent-heavy)
  • Project-specific: Patterns that appear in specific working directories

3. Error Patterns (from errorPropagation + event_types)

  • Error origins: Which agent depth level produces most errors
  • Cascade patterns: Errors that trigger chains of follow-up errors
  • APIError frequency: quota hits, rate_limit, overloaded — by time of day
  • Recovery patterns: How errors are typically resolved (tool retry vs agent switch)

4. Agent Patterns (from cooccurrence + effectiveness)

  • Agent pairs: Which agents are spawned together frequently
  • Delegation patterns: Main agent → subagent task delegation habits
  • Success by type: Which subagent types (task/explore/code-review) work best for which tasks

5. Temporal Patterns (from session timestamps + daily_sessions)

  • Peak hours: When sessions cluster
  • Duration patterns: Short vs long session distribution
  • Day-of-week trends: Productive days vs quiet days

Output

Pattern Report with top 10 patterns ranked by frequency × impact:

  • Pattern name and description
  • Frequency (occurrences across analyzed sessions)
  • Impact: positive (reinforce), negative (eliminate), or neutral (observe)
  • Actionable recommendation for each

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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benchmark

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

hoangsonww/Claude-Code-Agent-Monitor1.1천2026년 10월 21일 업데이트

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