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).
78 lines
3.5 KiB
Markdown
78 lines
3.5 KiB
Markdown
---
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description: >
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Detect recurring patterns using the Agent Monitor's workflow intelligence —
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toolFlow transitions (tool A → B frequency matrices), recurring workflow
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patterns, agent co-occurrence pairs, model delegation habits, error
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propagation paths by agent depth, and compaction triggers. Use to discover
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habitual usage patterns and anti-patterns.
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---
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# Pattern Detect
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Identify recurring patterns using the Agent Monitor's workflow intelligence engine.
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## Input
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The user provides: **$ARGUMENTS**
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Options: "all", "tools", "errors", "workflows", "last N sessions".
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## Data Sources
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| Endpoint | Returns |
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|----------|---------|
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| `GET /api/sessions?limit=200` | Session list with status, model, cwd, metadata |
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| `GET /api/analytics` | tool_usage top 20, event_types, agent_types |
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| `GET /api/workflows/{sessionId}` | 11 datasets per session (see below) |
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### Workflow datasets used for pattern detection
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| Dataset | Pattern insight |
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|---------|----------------|
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| `toolFlow` | **Tool transition matrix**: tool A → tool B with counts — reveals sequential habits |
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| `patterns` | **Detected workflow patterns**: recurring sequences with frequency scores |
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| `cooccurrence` | **Agent co-occurrence**: which agents frequently run together |
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| `modelDelegation` | **Model habits**: which models are chosen for which task types |
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| `errorPropagation` | **Error patterns**: where errors start and how they cascade by agent depth |
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| `effectiveness` | **Subagent patterns**: which types succeed most, avg duration per type |
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| `compaction` | **Compaction triggers**: what causes context overflow |
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| `complexity` | **Complexity patterns**: session complexity scores over time |
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## Pattern Categories
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### 1. Tool Chain Patterns (from `toolFlow`)
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- **Most common sequences**: Top 10 tool transitions (e.g., Read → Edit: 145 times)
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- **Starter tools**: First tool used in sessions (indicates task type)
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- **Finisher tools**: Last tool before Stop event
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- **Anti-patterns**: Tool → same Tool repeated (retries/failures)
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- **Co-occurrence**: Tools that always appear together in sessions
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### 2. Workflow Patterns (from `patterns`)
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- **Named patterns**: Workflow sequences the API has detected with frequency
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- **Session archetypes**: Common session shapes (short edit, long debug, subagent-heavy)
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- **Project-specific**: Patterns that appear in specific working directories
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### 3. Error Patterns (from `errorPropagation` + `event_types`)
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- **Error origins**: Which agent depth level produces most errors
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- **Cascade patterns**: Errors that trigger chains of follow-up errors
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- **APIError frequency**: quota hits, rate_limit, overloaded — by time of day
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- **Recovery patterns**: How errors are typically resolved (tool retry vs agent switch)
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### 4. Agent Patterns (from `cooccurrence` + `effectiveness`)
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- **Agent pairs**: Which agents are spawned together frequently
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- **Delegation patterns**: Main agent → subagent task delegation habits
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- **Success by type**: Which subagent types (task/explore/code-review) work best for which tasks
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### 5. Temporal Patterns (from session timestamps + `daily_sessions`)
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- **Peak hours**: When sessions cluster
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- **Duration patterns**: Short vs long session distribution
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- **Day-of-week trends**: Productive days vs quiet days
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## Output
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**Pattern Report** with top 10 patterns ranked by frequency × impact:
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- Pattern name and description
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- Frequency (occurrences across analyzed sessions)
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- Impact: positive (reinforce), negative (eliminate), or neutral (observe)
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- Actionable recommendation for each
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