hoangsonww

session-debug

Debug a specific session by inspecting its full event chain (PreToolUse, PostToolUse, Stop, SubagentStop, Compaction, APIError, TurnDuration, Notification events), agent hierarchy (recursive parent/child tree with subagent_type and depth), token usage with compaction baselines, workflow intelligence data (orchestration DAG, error propagation by depth), and session metadata (thinking_blocks, turn_count, total_turn_duration_ms).

hoangsonww 956 221 Updated 4w ago

Resources

1
GitHub

Install

npx skillscat add hoangsonww/claude-code-agent-monitor/session-debug

Install via the SkillsCat registry.

About this skill

Debugs a specific Claude Code session by retrieving its events, agents, and token usage data from the Agent Monitor API. It helps diagnose issues such as errors, slow turns, or failed subagents by exposing the full event chain, agent hierarchy, and workflow metrics. Use this skill when investigating why a session failed, stalled, or behaved unexpectedly.

SKILL.md

Session Debug

Debug and inspect a Claude Code session from Agent Monitor data.

Input

The user provides: $ARGUMENTS

This may be:

  • A session ID to debug
  • "latest" or "last" for the most recent session
  • "errors" to find and debug the most recent errored session

Procedure

  1. Identify the target session:

    • If session ID given: GET /api/sessions/{id} from http://localhost:4820
    • If "latest": GET /api/sessions?limit=1 (default sort: most recently updated first)
    • If "errors": GET /api/sessions?limit=10&status=error
  2. Collect full session data:

    • Session metadata: status, model, cwd, timestamps, duration
    • Events: GET /api/events?session_id={session_id} — full event timeline
    • Agents: GET /api/agents?session_id={session_id} — all agents in session
    • Cost: GET /api/pricing/cost/{session_id}
  3. Analyze the session:

    Session Lifecycle

    • Start time → first event → last event → end time
    • Status transitions (active → working → completed/error)
    • Total duration and active-vs-idle time

    Event Chain Analysis

    • Chronological event list with timestamps and durations
    • Identify the critical path (longest chain of dependent events)
    • Flag events that took unusually long
    • Highlight error events with full error context

    Agent Inspection

    • List all agents: type, task, status, duration
    • Subagent tree visualization (parent → children)
    • Agents that failed and their last known state
    • Agent switching patterns (when and why new agents spawned)

    Tool Execution Trace

    • Every tool invocation in order with: tool name, duration, success/failure
    • Failed tool calls with error messages
    • Tool retry patterns (same tool called multiple times)

    Anomaly Detection

    • Events out of expected order
    • Gaps in event timeline (>30s with no events)
    • Duplicate events or agent states
    • Token usage spikes (compaction indicators)
  4. Diagnosis:

    • Root cause hypothesis (if errors present)
    • Contributing factors
    • Remediation suggestions

Output Format

Present as a debug report with:

  • Session summary header (ID, status, model, duration, cost)
  • Color-coded timeline (✅ success, ❌ error, ⚠️ warning, ℹ️ info)
  • Agent tree diagram
  • Diagnosis section with numbered findings