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claude-code

Claude-Code-Agent-Monitor pattern-detect

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 "$T/plugins/ccam-insights/skills/pattern-detect" ~/.claude/skills/hoangsonww-claude-code-agent-monitor-pattern-detect && rm -rf "$T"
manifest: plugins/ccam-insights/skills/pattern-detect/SKILL.md
source content

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=200
Session list with status, model, cwd, metadata
GET /api/analytics
tool_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
toolFlow
Tool transition matrix: tool A → tool B with counts — reveals sequential habits
patterns
Detected workflow patterns: recurring sequences with frequency scores
cooccurrence
Agent co-occurrence: which agents frequently run together
modelDelegation
Model habits: which models are chosen for which task types
errorPropagation
Error patterns: where errors start and how they cascade by agent depth
effectiveness
Subagent patterns: which types succeed most, avg duration per type
compaction
Compaction triggers: what causes context overflow
complexity
Complexity 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