从 Agent Monitor 数据生成 APIError 报告,包括错误随时间的变化、受影响的会话和模型,以及限流、过载或上下文窗口压力等可能原因。适用于 API 错误激增或请求失败需要解释时。
资源
1安装
npx skillscat add hoangsonww/claude-code-agent-monitor/api-error-report 通过 SkillsCat registry 安装。
从 Agent Monitor 数据生成 APIError 报告,包括错误随时间的变化、受影响的会话和模型,以及限流、过载或上下文窗口压力等可能原因。适用于 API 错误激增或请求失败需要解释时。
API Error Report
Drill into APIError events: how many, when, where, and most likely why.
Input
The user provides: $ARGUMENTS
This may be:
- empty or "all" — report on every APIError in the recent window (default)
- a session ID — report APIErrors for that one session only
- a window like "today" or "last 7d" — restrict the time range
- a cause filter: "rate-limit", "overload", or "context"
Data Sources
| Endpoint | Returns |
|---|---|
GET /api/analytics |
event_types (total APIError count), daily_events (365d) — APIError volume and trend over time |
GET /api/events?session_id=X |
Per-session event stream — each APIError carries summary, data, and timestamp used to classify the cause |
GET /api/sessions?limit=N |
Sessions with id, model, started_at — attribute each error to a model and place it on the timeline |
Report Sections
1. Volume & Trend
From GET /api/analytics: total APIError count and its share of total_events. Use daily_events to chart APIErrors over the requested window and flag any day that spikes above the window mean.
2. Affected Sessions & Models
For each session in scope, pull GET /api/events?session_id=X and collect APIError events. Group by session_id and, via GET /api/sessions, by model. Report the top affected sessions and which model accounts for the most errors.
3. Likely Cause Classification
Inspect each error's summary/data and bucket it:
- Rate limit — mentions 429, "rate limit", "quota", or retry-after.
- Overload — mentions 529, "overloaded", or capacity.
- Context — mentions context length, token limit, or "too long" (correlate with nearby
Compactionevents). - Other — anything else; quote the
summary.
Report the count and percentage in each bucket.
4. Timeline
List the most recent APIErrors with timestamp, session_id, model, classified cause, and a one-line summary excerpt.
Output
- A Markdown table per section (volume, by model, by cause).
- Rates as percentages to 2 decimals; any currency in USD to 4 decimals.
- Cite exact
session_id,model,timestamp, andsummaryvalues — never invent a cause not supported by the payload; bucket as "Other" when unclear. - End with the dominant cause and a concrete mitigation (e.g., back off and retry on 529, reduce context to cut context errors, slow request rate on 429).
- Read-only: only report what the API returns. If
curlcannot reachhttp://localhost:4820, tell the user to start the dashboard withnpm startfrom the repo root.