Claude Skill

sprint-summary

Summarize a sprint's worth of Claude Code activity — sessions grouped by project (cwd), per-model cost breakdown, token efficiency (cache hit rate, compaction baselines), subagent effectiveness from workflow API, velocity metrics (turn_count, turn_duration_ms), and tool diversity

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hoangsonww/claude-code-agent-monitor 1014 238 forks MIT Updated 1d ago

Part of hoangsonww/claude-code-agent-monitor — 86 skills

Install

skills CLI

npx skills add https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-productivity/skills/sprint-summary

Claude Code

claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install hoangsonww-claude-code-agent-monitor@llmmart

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

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole hoangsonww/claude-code-agent-monitor collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Sprint Summary

Generate a sprint summary from Claude Code Agent Monitor data.

Input

The user provides: $ARGUMENTS

This may be:

A sprint duration: "last 2 weeks", "last 10 days"
A date range: "2025-03-31 to 2025-04-13"
"current sprint" (default: last 14 days)

Procedure

Fetch sprint data from http://localhost:4820:
- GET /api/sessions?limit=500 — all sessions in range (default sort: most recently updated first)
- GET /api/analytics — aggregated metrics
- GET /api/pricing/cost — total costs
- For high-value sessions: GET /api/events?session_id={id} — event details
Compile sprint summary:

🎯 Sprint Overview
- Sprint period: [start] to [end]
- Total sessions: N (completed: N, errored: N, abandoned: N)
- Total development hours with Claude Code: N
- Total cost: $X.XX
- Overall completion rate: N%
📦 Deliverables

Group sessions by working directory (project):
- Project A (/path/to/project)
  - N sessions, N hours, key activities
- Project B (/path/to/other)
  - N sessions, N hours, key activities
📊 Velocity Metrics

Metric Sprint Previous Sprint Trend

Sessions/day N N ↑/↓

Avg session duration Nm Nm ↑/↓

Cost/session $N $N ↑/↓

Tokens/session N N ↑/↓

Completion rate N% N% ↑/↓

🛠 Technology Breakdown
- Models used with distribution percentages
- Top 15 tools by usage with category grouping
- Subagent utilization rate
⚡ Efficiency Analysis
- Token efficiency: cache hit rate, compaction frequency
- Cost per completed task
- Time-to-first-output (avg across sessions)
- Error recovery rate (sessions that recovered from errors)
🔄 Retrospective Data Points
- What went well: Highest-efficiency sessions, best completion rates
- What could improve: Most expensive sessions, highest error rates
- Action items: Data-driven suggestions for next sprint

Metric	Sprint	Previous Sprint	Trend
Sessions/day	N	N	↑/↓
Avg session duration	Nm	Nm	↑/↓
Cost/session	$N	$N	↑/↓
Tokens/session	N	N	↑/↓
Completion rate	N%	N%	↑/↓

Output Format

Professional sprint report suitable for sharing with team leads or managers:

Executive summary paragraph (5 sentences max)
Structured data tables with trend indicators
Grouped deliverables by project
Numbered action items at the end

Files (claude-code-agent-monitor)

agents

openai.yaml 263 B

interface:
  display_name: "Sprint Summary"
  short_description: "Summarize a sprint's worth of Claude Code activity —..."
  default_prompt: "Use $sprint-summary to inspect CCAM data and complete this workflow safely."
policy:
  allow_implicit_invocation: true

SKILL.md 2.7 KB

---
name: sprint-summary
description: >
  Summarize a sprint's worth of Claude Code activity — sessions grouped by
  project (cwd), per-model cost breakdown, token efficiency (cache hit rate,
  compaction baselines), subagent effectiveness from workflow API, velocity
  metrics (turn_count, turn_duration_ms), and tool diversity across the sprint.
---

# Sprint Summary

Generate a sprint summary from Claude Code Agent Monitor data.

## Input

The user provides: **$ARGUMENTS**

This may be:
- A sprint duration: "last 2 weeks", "last 10 days"
- A date range: "2025-03-31 to 2025-04-13"
- "current sprint" (default: last 14 days)

## Procedure

1. **Fetch sprint data** from `http://localhost:4820`:
   - `GET /api/sessions?limit=500` — all sessions in range (default sort: most recently updated first)
   - `GET /api/analytics` — aggregated metrics
   - `GET /api/pricing/cost` — total costs
   - For high-value sessions: `GET /api/events?session_id={id}` — event details

2. **Compile sprint summary**:

   ### 🎯 Sprint Overview
   - Sprint period: [start] to [end]
   - Total sessions: N (completed: N, errored: N, abandoned: N)
   - Total development hours with Claude Code: N
   - Total cost: $X.XX
   - Overall completion rate: N%

   ### 📦 Deliverables
   Group sessions by working directory (project):
   - **Project A** (`/path/to/project`)
     - N sessions, N hours, key activities
   - **Project B** (`/path/to/other`)
     - N sessions, N hours, key activities

   ### 📊 Velocity Metrics
   | Metric | Sprint | Previous Sprint | Trend |
   |--------|--------|-----------------|-------|
   | Sessions/day | N | N | ↑/↓ |
   | Avg session duration | Nm | Nm | ↑/↓ |
   | Cost/session | $N | $N | ↑/↓ |
   | Tokens/session | N | N | ↑/↓ |
   | Completion rate | N% | N% | ↑/↓ |

   ### 🛠 Technology Breakdown
   - Models used with distribution percentages
   - Top 15 tools by usage with category grouping
   - Subagent utilization rate

   ### ⚡ Efficiency Analysis
   - Token efficiency: cache hit rate, compaction frequency
   - Cost per completed task
   - Time-to-first-output (avg across sessions)
   - Error recovery rate (sessions that recovered from errors)

   ### 🔄 Retrospective Data Points
   - **What went well**: Highest-efficiency sessions, best completion rates
   - **What could improve**: Most expensive sessions, highest error rates
   - **Action items**: Data-driven suggestions for next sprint

## Output Format

Professional sprint report suitable for sharing with team leads or managers:
- Executive summary paragraph (5 sentences max)
- Structured data tables with trend indicators
- Grouped deliverables by project
- Numbered action items at the end