Claude-Code-Agent-Monitor session-report

install

source · Clone the upstream repo

git clone https://github.com/hoangsonww/Claude-Code-Agent-Monitor

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/hoangsonww/Claude-Code-Agent-Monitor "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/ccam-analytics/skills/session-report" ~/.claude/skills/hoangsonww-claude-code-agent-monitor-session-report && rm -rf "$T"

manifest: plugins/ccam-analytics/skills/session-report/SKILL.md

source content

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)

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.