Japanese Enterprise Document Formatter

Format documents for Japanese enterprise approval workflows including ringi (稟議), purchase requests (購入申請), and internal proposals. Handles bilingual requirements, proper keigo levels, required approval sections, and corporate template compliance.

No API Required

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Formats 5 enterprise document types (稟議書 / 購入申請書 / 提案書 / 報告書 / 依頼書) with 4 keigo levels (最上級 / 上級 / 標準 / 基本). Includes section validation with completeness scoring, bilingual output with English summaries, and approval-section generation appropriate to each document type.

2. Prerequisites

  • Python 3.9+
  • No API keys required

3. Quick Start 

# Install the skill locally
make install SKILL=japanese-enterprise-doc-formatter

# Or fetch the .skill package
curl -L -o japanese-enterprise-doc-formatter.skill https://github.com/takusaotome/claude-skills-library/raw/main/skill-packages/japanese-enterprise-doc-formatter.skill

Then trigger the skill in Claude Code by describing what you want — see the Usage Examples section below for trigger phrases.

4. How It Works

The skill follows the workflow documented in its SKILL.md. Key stages:

  1. Input parsing — interprets the user request and any provided source files.
  2. Core processing — applies the skill’s domain logic (see Reference section).
  3. Output generation — produces structured artifacts (markdown / JSON / templates) ready for downstream use.

For the authoritative step-by-step procedure, open skills/japanese-enterprise-doc-formatter/SKILL.md.

5. Usage Examples

  • You’re drafting 稟議書 / 購入申請書 / 提案書 for Japanese approval flows
  • You need automatic keigo-level normalization across document types
  • You want section validation against required structures (e.g. 背景/目的/効果/費用/承認)
  • You produce bilingual versions for cross-regional approval chains

6. Understanding the Output

The skill produces structured output following the conventions in its templates and reference docs (see Section 10). Outputs are:

  • Reproducible — identical input + same templates → same output structure.
  • Reviewable — each section is labeled and ordered consistently.
  • Composable — outputs of this skill can feed adjacent skills (see Section 8).

7. Tips & Best Practices

  • Start with a small, realistic input to validate the workflow before scaling.
  • Keep skills/japanese-enterprise-doc-formatter/SKILL.md open alongside this guide; it remains the authoritative source.
  • Read the most relevant reference file first (see Section 10) instead of trying to absorb all of them.
  • Run scripts on test data before applying to production-bound inputs.
  • Preserve intermediate outputs so you can explain assumptions and trace decisions.

8. Combining with Other Skills

  • Pair with adjacent skills in the same category to cover the planning → execution → review arc.
  • Browse the Operations & Docs category for neighboring workflows: category index.
  • See the full English skill catalog: skill catalog.

9. Troubleshooting

  • Re-check prerequisites first; missing runtime dependencies are the most common failure mode.
  • Run helper scripts on a minimal input before applying them to a full dataset.
  • Compare your input shape against the reference files to confirm expected fields, sections, or metadata.
  • Confirm Python version (3.9+) and required packages are installed in the active environment.
  • When output looks incomplete, re-read the relevant reference file to verify the input contract.

10. Reference

References:

  • skills/japanese-enterprise-doc-formatter/references/document_types.md
  • skills/japanese-enterprise-doc-formatter/references/keigo_guide.md
  • skills/japanese-enterprise-doc-formatter/references/section_templates.md

Scripts:

  • skills/japanese-enterprise-doc-formatter/scripts/format_document.py
  • skills/japanese-enterprise-doc-formatter/scripts/transform_keigo.py
  • skills/japanese-enterprise-doc-formatter/scripts/validate_sections.py

Assets:

(none)