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).
Resources
1Install
npx skillscat add hoangsonww/claude-code-agent-monitor/session-debug Install via the SkillsCat registry.
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.
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
Identify the target session:
- If session ID given:
GET /api/sessions/{id}fromhttp://localhost:4820 - If "latest":
GET /api/sessions?limit=1(default sort: most recently updated first) - If "errors":
GET /api/sessions?limit=10&status=error
- If session ID given:
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}
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)
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