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.
npx agentmods add agents/ww-w-ai/bkit-claude-code/report-generatorgit clone --depth 1 https://github.com/ww-w-ai/bkit-claude-codeWrote 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.
[](https://agentmods.dev/agents/ww-w-ai/bkit-claude-code/report-generator)<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>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.
| Model | Per session | Once 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 |
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.
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
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.
- 4d ago First seen · 269 lines · 81 tokens per session scan A fc40da00dd30
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.
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