Error Scan Skill for Claude

Error Scan

Sweep recent events across sessions for error and failure signals, then rank them

by how often they occur and which tool or model produced them.

Input

The user provides: $ARGUMENTS

This may be:

• empty or "all" — scan every failure signal (default)
• "api" — APIError events only
• "tools" — tool-failure gaps only
• a number N — limit the scan to the most recent N sessions
• a session ID — scan a single session

Data Sources

| Endpoint | Returns |

|----------|---------|

| GET /api/analytics | event_types (counts per type incl. PreToolUse, PostToolUse, APIError), tool_usage (top 20), daily_events (365d) — fleet-wide failure baseline |

| GET /api/events?session_id=X | Per-session event stream: event_type, tool_name, summary, data, timestamp — locate APIError and unmatched PreToolUse |

| GET /api/sessions?limit=N | Sessions with id, status, model, started_at — pick the recent window and attribute failures to a model |

Report Sections

1. Scope

Resolve $ARGUMENTS to a session set: pull GET /api/sessions?limit=N (default 50, ordered by started_at). Report how many sessions and what time span are covered.

2. Fleet Failure Counts

From GET /api/analytics event_types, report total APIError count and the PreToolUse→PostToolUse gap: gap = PreToolUse − PostToolUse (unmatched tool starts = likely failures). State both as raw counts and as a share of total_events.

3. Group by Tool

For each session in scope, pull GET /api/events?session_id=X. Match each PreToolUse to its following PostToolUse by tool_name; unmatched starts are failures. Aggregate failures and APIError events per tool_name. Rank tools by failure frequency (descending).

4. Group by Model

Join failures to the owning session's model (from GET /api/sessions). Rank models by APIError count and tool-failure count.

5. Top Offenders

List the single most failure-prone tool, the most error-prone model, and the session with the most failures, each with its exact count and one-line summary excerpt from a representative event.

Output

• A ranked Markdown table: tool/model | APIError count | tool-failure (gap) count | total failures | share of events.
• Rates as percentages to 2 decimals.
• Cite exact event_type, tool_name, and session_id values — never fabricate counts.
• End with the one failure pattern most worth investigating and a concrete next step.
• 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.