agentsclimarketplace

Error scan

Skill hoangsonww/Claude-Code-Agent-Monitor/plugins/ccam-quality/skills/error-scan

🚀 A real-time monitoring dashboard for Claude Code, built with SQLite3, Node.js, Express, React, Vite, TailwindCSS, and WebSockets. It tracks sessions, agent activity, tool usage, and subagent orchestration, providing live analytics, a Kanban status board, status notifications, a cute buddy, and an interactive web UI/MacOS/Windows native app.

Install
npx -y skills add hoangsonww/Claude-Code-Agent-Monitor --skill error-scan

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Scan recent Claude Code activity for errors and failure signals across all sessions using Agent Monitor data — APIError events and PreToolUse→PostToolUse gaps (tools that started but never completed) — then group failures by tool and model and rank them by frequency. Use when checking for errors or asking "what's failing right now".

SKILL.md

3.0 KB, 672 tokens by cl100k_base, as published. Nobody here has run it

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

EndpointReturns
GET /api/analyticsevent_types (counts per type incl. PreToolUse, PostToolUse, APIError), tool_usage (top 20), daily_events (365d) — fleet-wide failure baseline
GET /api/events?session_id=XPer-session event stream: event_type, tool_name, summary, data, timestamp — locate APIError and unmatched PreToolUse
GET /api/sessions?limit=NSessions 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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.