hoangsonww

session-report

This skill generates a detailed session report from the Claude Code Agent Monitor, including per-model token usage, cost breakdown, tool invocations, agent hierarchy, compaction events, API errors, turn durations, and thinking block counts. It solves the problem of aggregating and presenting complex session data into a structured report. An agent or developer should use it when reviewing a specific session or summarizing activity over a date range, using a session ID, "latest", or a date range as input.

hoangsonww 1,106 257 Updated 2mo ago

Resources

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GitHub

Install

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

Install via the SkillsCat registry.

About this skill

This skill generates a detailed session report from the Claude Code Agent Monitor, including per-model token usage, cost breakdown, tool invocations, agent hierarchy, compaction events, API errors, turn durations, and thinking block counts. It solves the problem of aggregating and presenting complex session data into a structured report. An agent or developer should use it when reviewing a specific session or summarizing activity over a date range, using a session ID, "latest", or a date range as input.

SKILL.md

Session Report

Generate a detailed session report from the Claude Code Agent Monitor.

Input

The user provides: $ARGUMENTS

This may be a session ID, "latest", or a date range like "last 24 hours".

Data Sources

All data comes from the Agent Monitor API at http://localhost:4820:

Endpoint What it returns
GET /api/sessions/{id} Session with nested .agents[] and .events[]
GET /api/sessions?limit=50 Session list with agent_count, last_activity, and inline cost per session (bulk pricing applied server-side)
GET /api/pricing/cost/{sessionId} { total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] }
GET /api/events?session_id={id} Event stream: each has event_type, tool_name, summary, data (JSON), created_at

Key data points available per session

  • Status: active / completed / error / abandoned
  • Model: primary model (e.g. claude-sonnet-4-20250514)
  • Metadata (JSON): thinking_blocks count, turn_count, total_turn_duration_ms, usage_extras (service_tier, speed, inference_geo)
  • Token usage per model: Pricing breakdown reports input_tokens, output_tokens, cache_read_tokens, cache_write_tokens per model (baselines are pre-summed into these totals at the DB level)
  • Cost formula: (tokens / 1,000,000) × rate_per_mtok for each of 4 token types, using longest-match pricing rule
  • Agent hierarchy: recursive parent_agent_id tree, subagent_type (e.g. "task", "explore", "code-review", "compaction")
  • Event types: PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, Notification, Compaction, APIError, TurnDuration

Report Sections

1. Session Overview

  • ID (first 16 chars), name, status, model, working directory
  • Start → end time, total duration
  • Turn count and avg turn duration (from metadata)

2. Token Usage (per model)

Include these columns: Model, Input, Output, Cache Read, Cache Write, and Total.
Show effective totals (current + baseline) since baselines preserve tokens lost during compaction. Calculate cache hit rate: cache_read / (cache_read + input) × 100.

3. Cost Breakdown

From /api/pricing/cost/{id} — show each model's cost with the matched pricing rule. Note rates are per million tokens.

4. Agent Hierarchy

Render the agent tree (main → subagents, with nested children). For each agent: name, type, subagent_type, status, task (first 60 chars), duration.

5. Tool Activity

Count PreToolUse events by tool_name. Flag tools that appear in error events. Note subagent spawns (tool_name = "Agent").

6. Compaction & Context Health

  • Count of Compaction events (each = context was compressed)
  • Baseline tokens recovered (sum of baseline_* columns)
  • Thinking block count from metadata

7. API Errors

List any APIError events with type (quota, rate_limit, overloaded) and message.

8. Timeline

Key lifecycle events: SessionStart → first tool → compactions → errors → Stop → SessionEnd. Include TurnDuration events.

Output Format

Clean Markdown: executive summary line, structured tables, agent tree, numbered timeline. Bold key metrics.