Claude Skill

api-error-report

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 err

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Download hoangsonww-claude-code-agent-monitor-plugins_ccam-quality_skills_api-error-report-83d4df5.zip · 1 KB
Part of hoangsonww/claude-code-agent-monitor — 86 skills

Install

skills CLI npx skills add https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-quality/skills/api-error-report
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install hoangsonww-claude-code-agent-monitor@llmmart
Git git clone https://github.com/hoangsonww/Claude-Code-Agent-Monitor.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole hoangsonww/claude-code-agent-monitor collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

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

  • 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, and summary values — 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 curl cannot reach http://localhost:4820, tell the user to start the dashboard with npm start from the repo root.
Files (claude-code-agent-monitor)
  • agents
    • openai.yaml 268 B
      interface:
        display_name: "Api Error Report"
        short_description: "Produce a detailed report on APIError events from Agent..."
        default_prompt: "Use $api-error-report to inspect CCAM data and complete this workflow safely."
      policy:
        allow_implicit_invocation: true
      
  • SKILL.md 3 KB
    ---
    name: api-error-report
    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.
    ---
    
    # 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
    
    - 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`, and `summary` values — 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 `curl` cannot reach `http://localhost:4820`, tell the user to start the dashboard with `npm start` from the repo root.
    

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