# API Error Report

Produce a detailed report on APIError events from Agent Monitor data, counts over...

- Repository: https://github.com/hoangsonww/Claude-Code-Agent-Monitor (1136 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill api-error-report -g -a claude-code -y`
Codex: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill api-error-report -g -a codex -y`
Cursor: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill api-error-report -g -a cursor -y`
Gemini CLI: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill api-error-report -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add hoangsonww/Claude-Code-Agent-Monitor`

## Description

Produce a detailed report on APIError events from Agent Monitor data, counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 `Compaction` events).
- **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

The rest: https://github.com/hoangsonww/Claude-Code-Agent-Monitor/blob/HEAD/plugins/ccam-quality/skills/api-error-report/SKILL.md