Session Report
SkillMonitoring & opsGenerate a comprehensive session report with per-model token usage (input, output, cache_read, cache_write including compaction baselines), cost breakdown via the pricing engine, tool invocations, agent hierarchy, compaction events, API errors, turn durations, and thinking block counts. Use when reviewing a specific session or summarizing activity over a date range.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Session Report skill
What this skill tells your AI
The instructions your AI receives, as published by hoangsonww/claude-code-agent-monitor in plugins/ccam-analytics/skills/session-report/SKILL.md and read by ahel’s review.
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_blockscount,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_tokensper model (baselines are pre-summed into these totals at the DB level) - Cost formula:
(tokens / 1,000,000) × rate_per_mtokfor 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
Compactionevents (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.
Signals
- GitHub stars
- 989
- Forks
- 233
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
session-report-hoangsonww- Source
- github.com/hoangsonww/claude-code-agent-monitor