レシート・領収書・ふるさと納税受領証明書の画像を読み取り、構造化データとして返すスキル。
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionreading-receiptExecute the skills CLI command in your project's root directory to begin installation:
Fetches reading-receipt from kazukinagata/shinkoku and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate reading-receipt. Access via /reading-receipt in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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レシート・領収書・ふるさと納税受領証明書の画像を読み取り、構造化データとして返すスキル。
ファイルが PDF（.pdf）の場合、画像 OCR の前にテキスト抽出を試みる。
shinkoku pdf extract-text --file-path <path> を実行するshinkoku pdf to-image --file-path <path> --output-dir <dir> で PNG に変換し、以下の画像読み取りフローに進む精度を高めるため、同じ画像を2つの独立したコンテキストで並列に読み取り、結果を照合する。
2つの独立した読み取りを実行する: サブエージェントが使える環境では、2つのサブエージェントを並列で起動し、それぞれ独立に画像を読み取る。 各サブエージェントには以下の「基本ルール」と「出力フォーマット」をプロンプトとして渡し、画像ファイルパスを指定する。
結果照合: 両方の読み取り結果から主要フィールド（金額等）を比較する。
一致の場合: そのまま採用。「2つの独立した読み取りで結果が一致しました」と報告する。
不一致の場合: ユーザーに元画像パスと両方の結果を提示し、正しい方を選択してもらう:
サブエージェントが利用できない環境では、以下の手順で読み取る:
⚠ デュアル検証が利用できないため、必ずユーザーに目視確認を依頼してください。
画像を読み取り、以下の形式で返す:
---RECEIPT_DATA---
date: YYYY-MM-DD
vendor: 店舗名
total_amount: 金額（int）
tax_included: true/false
items:
- name: 品目名
amount: 金額（int）
quantity: 数量（int）
---END---
画像を読み取り、以下の形式で返す:
---FURUSATO_RECEIPT_DATA---
municipality_name: 自治体名（市区町村名）
prefecture: 都道府県名
amount: 寄附金額（int）
date: YYYY-MM-DD
receipt_number: 受領証明書番号（記載がなければ UNKNOWN）
---END---
複数のファイルパスが指示された場合、または Glob パターンでファイル一覧を取得した場合:
## file1.jpg
---RECEIPT_DATA---
...
---END---
## file2.jpg
---RECEIPT_DATA---
...
---END---
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Keeps context tight: reading-receipt is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: reading-receipt is focused, and the summary matches what you get after install.
reading-receipt is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
reading-receipt reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for reading-receipt matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in reading-receipt — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend reading-receipt for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
reading-receipt has been reliable in day-to-day use. Documentation quality is above average for community skills.
reading-receipt fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
reading-receipt is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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