nntrivi2001
17d56d0e01
Clean break, no compatibility alias. Renames the product name, the CLI binary (`ccam` -> `agenthub`), the env var prefix (`CCAM_` -> `AGENTHUB_`), the Prometheus metric namespace (`ccam_*` -> `agenthub_*`), the tmux session prefix (`ccam-lane-<id>` -> `agenthub-lane-<id>`), the per-repo runtime profile directory (`.ccam/profile` -> `.agenthub/profile`), the lanes state root (`~/.claude/ccam-lanes` -> `~/.claude/agenthub-lanes`), the bundled plugins (`plugins/ccam*` -> `plugins/agenthub*`) and their slash commands (`/ccam-doctor` -> `/agenthub-doctor`, `/ccam:*` -> `/agenthub:*`). Because there is no alias, an existing install has to be updated by hand: rename its `.ccam/profile` directory and any `CCAM_*` variables, and reinstall the plugin under its new name. Two other name families are deliberately left alone here, since they are not just strings: the `Claude-Code-Monitor` repository/marketplace name, and the `agent-dashboard` npm package plus its `~/.claude/agent-dashboard` runtime directory, which holds the live database and needs a migration rather than a rename. Verified: server 1406/1406, client 459/459, `grep -i ccam` clean across tracked files, `openapi.yaml` regenerated (95 paths, unchanged count).
3.5 KiB
3.5 KiB
description
| description |
|---|
| 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. |
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
| Endpoint | Returns |
|---|---|
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
| Dataset | Pattern 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