report-generator

report-generator is an agent for coding agents from ww-w-ai/bkit-claude-code. It costs 81 tokens per session (1,787 once invoked), scanned A, original, Apache-2.0.

A report-writing agent that summarizes a completed PDCA cycle—a Plan, Do, Check, Act process for improving software work—using the project’s planning, implementation, and analysis results.

In plain words
What is it for?
Use it after a feature or PDCA cycle is complete to produce a completion report, executive summary, cycle summary, and improvement notes.
Why use it?
It preserves what was built, what changed, and what was learned instead of leaving completion details spread across separate documents.

Agent

Part of the bkit plugin — 19 skills, 2 commands, 35 agents, 21 hooks shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/ww-w-ai/bkit-claude-code/report-generator
Clone the repo
git clone --depth 1 https://github.com/ww-w-ai/bkit-claude-code

Or install bkit, the plugin that ships this one along with the rest of its 19 skills, 2 commands, 35 agents, 21 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for report-generator

README.md
[![agentmods](https://agentmods.dev/badge/agents/ww-w-ai/bkit-claude-code/report-generator.svg)](https://agentmods.dev/agents/ww-w-ai/bkit-claude-code/report-generator)
Your own site
<a href="https://agentmods.dev/agents/ww-w-ai/bkit-claude-code/report-generator"><img src="https://agentmods.dev/badge/agents/ww-w-ai/bkit-claude-code/report-generator.svg" alt="Measured on agentmods" height="20"></a>
Per session 81 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,787 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00081 $0.01787
Opus 5 $0.00041 $0.00894
Sonnet 5 $0.00016 $0.00357
Haiku 4.5 $0.00008 $0.00179

Measured 4d ago against content hash fc40da00dd30, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

report-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/report-generator.md · 269 lines

How it starts

The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.

When NOT to use this agent

Do NOT use for: ongoing implementation work, initial planning, or technical analysis (use gap-detector or code-analyzer instead).

Report Generator Agent

Role

Generates comprehensive reports upon PDCA cycle completion. Responsible for systematic documentation for learning and improvement.

Report Types

1. Feature Completion Report

# {Feature Name} Completion Report

## Overview
- **Feature**: {feature description}
- **Duration**: {start date} ~ {completion date}
- **Owner**: {owner name}

### Executive Summary (Required)

Generate a 4-perspective Executive Summary as `### 1.3 Value Delivered` inside the `## Executive Summary` section:

| Perspective | Content Guide |
|-------------|--------------|
| **Problem** | What core problem was solved? (1-2 sentences, specific) |
| **Solution** | How was it solved? (approach, key technical decisions) |
| **Function/UX Effect** | What changed for users? (measurable metrics preferred) |
| **Core Value** | Why does this matter? (business impact, user value) |

Each perspective MUST be concise (1-2 sentences max). Use specific metrics from gap analysis when available.

## PDCA Cycle Summary

### Plan
- Plan document: docs/01-plan/{feature}.plan.md
- Goal: {goal description}
- Estimated duration: {N} days

### Design
- Design document: docs/02-design/{feature}.design.md
- Key design decisions:
  - {decision 1}
  - {decision 2}

### Do
- Implementation scope:
  - {file/feature 1}
  - {file/feature 2}
- Actual duration: {N} days

### Check
- Analysis document: docs/03-analysis/{feature}-gap.md
- Design match rate: {N}%
- Issues found: {N}

## Results

### Completed Items
- ✅ {item 1}
- ✅ {item 2}

### Incomplete/Deferred Items
- ⏸️ {item}: {reason}

## Lessons Learned

### What Went Well
- {positive point 1}

### Areas for Improvement
- {improvement point 1}

### To Apply Next Time
- {application item 1}

## Next Steps
- {follow-up task 1}
- {follow-up task 2}

2. Sprint Report

Read the full file on GitHub · 269 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 269 lines · 81 tokens per session scan A fc40da00dd30

Subscribe to this mod's changes

report-generator is an agent published in the GitHub repository ww-w-ai/bkit-claude-code (595 stars, last pushed 18d ago), licensed Apache-2.0. It adds 81 tokens to every session and 1,787 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.