Files
AgentHub/plugins/agenthub-insights/skills/anomaly-alert/SKILL.md
T
nntrivi2001 17d56d0e01 refactor!: rebrand CCAM to AgentHub
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).
2026-09-07 12:09:15 +07:00

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---
description: >
Identify anomalous sessions using Agent Monitor data — cost outliers from
the pricing engine, token anomalies (cache miss spikes, compaction baseline
surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError
clusters), behavioral deviations from workflow intelligence (complexity
score outliers, error propagation anomalies), and sessions with abnormal
metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
---
# Anomaly Alert
Detect anomalous sessions in AgentHub data.
## Input
The user provides: **$ARGUMENTS**
This may be:
- "all" or empty (default: check all anomaly types)
- "cost" for cost anomalies only
- "duration" for duration anomalies only
- "errors" for error rate anomalies only
- A sensitivity level: "strict" (1σ), "normal" (2σ), "relaxed" (3σ)
## Procedure
1. **Fetch baseline data** from `http://localhost:4820`:
- `GET /api/sessions?limit=500` — historical sessions for baseline
- `GET /api/analytics` — aggregated metrics
- `GET /api/pricing/cost` — cost data per session
2. **Compute baselines** for each metric:
- Mean, median, standard deviation
- P25, P75, P90, P95, P99 percentiles
- Interquartile range (IQR) for robust outlier detection
3. **Detect anomalies** using statistical thresholds:
### Cost Anomalies
- Sessions costing >2σ above mean
- Single sessions exceeding daily average
- Sudden cost spikes (session-over-session increase >200%)
### Duration Anomalies
- Sessions lasting >2σ above mean duration
- Extremely short sessions (<1 minute) that still incur cost
- Sessions with unusual active-vs-idle ratios
### Error Rate Anomalies
- Sessions with error rates >2σ above baseline
- New error types not seen in previous sessions
- Sessions with >3 consecutive tool failures
### Behavioral Anomalies
- Unusual tool combinations not seen before
- Sessions with abnormally high compaction counts
- Model switches mid-session (if unexpected)
- Sessions with no tool usage (pure conversation)
### Token Anomalies
- Input/output token ratio far from historical norm
- Cache miss rate significantly higher than average
- Token usage growing faster than session count
4. **Classify each anomaly**:
- **🔴 Critical**: Likely indicates a real problem requiring attention
- **🟡 Warning**: Unusual but may be expected for certain tasks
- **🔵 Info**: Interesting deviation worth noting
## Output Format
Present as an **Anomaly Report**:
```
═══════════════════════════════════════════════
ANOMALY DETECTION REPORT
Analyzed: N sessions | Baseline: last 30 days
Anomalies found: N (🔴 N critical, 🟡 N warn, 🔵 N info)
═══════════════════════════════════════════════
```
For each anomaly:
- Session ID and timestamp
- Anomaly type and severity
- Observed value vs expected range
- Possible explanation
- Recommended action (if any)