Borrowing it
Nothing to install: this file belongs to jugesuke/rirekisho. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jugesuke/rirekisho/main/.claude/skills/rirekisho/SKILL.mdgit clone --depth 1 https://github.com/jugesuke/rirekishoWrote 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/skills/jugesuke/rirekisho/rirekisho)<a href="https://agentmods.dev/skills/jugesuke/rirekisho/rirekisho"><img src="https://agentmods.dev/badge/skills/jugesuke/rirekisho/rirekisho/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jugesuke/rirekisho/rirekisho"><img src="https://agentmods.dev/badge/skills/jugesuke/rirekisho/rirekisho.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00102 | $0.01716 |
| Opus 5 | $0.00051 | $0.00858 |
| Sonnet 5 | $0.00020 | $0.00343 |
| Haiku 4.5 | $0.00010 | $0.00172 |
Grade A, and why
rirekisho 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 10d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
履歴書の記入と PDF 出力
main.tex に記入データを書き、npm run build で out/main.pdf を出すまでを一通り行う。
レイアウトは rirekisho.sty が持つ。このスキルの作業中に rirekisho.sty を変更しない（欄が足りないなど構造的な問題は、直さずユーザーに報告する）。
手順
1. 現状を確認する
main.tex を読む。既に値が入っているなら「新規に書き直す」のか「一部を更新する」のかをユーザーに確認する。
白紙から始める場合のみ、blank.tex の中身をそのまま main.tex に写してから記入する（main.tex を消して作り直さない。blank.tex はビルド対象ではないので編集しない）。
2. 情報を集める
ユーザーの指示・添付・既存の main.tex から埋められるものは埋め、足りないものだけをまとめて 1 回で聞く。推測で経歴・日付・連絡先を作らない（記入例 example.tex の値をそのまま残さない）。
必要な項目:
| コマンド | 内容 |
|---|---|
\sakuseibi{年}{月}{日} |
作成日。算用数字で。例 {2026}{8}{2}。空欄（{}{}{}）ならビルド日が入る。和暦表示は \sakuseibi[令和8年8月2日]{2026}{8}{2} |
\furigana{} / \shimei{} |
ふりがな（ひらがな）と氏名。姓名の間は全角空白 |
\seinengappi{年}{月}{日} |
生年月日。算用数字で。例 {1990}{1}{1}。満年齢は入力しない（作成日を基準に自動計算される）。和暦表示は \seinengappi[平成2年1月1日]{1990}{1}{1} |
\seibetsu{} |
任意。指定がなければ空のまま |
\shashin{} |
画像ファイル名。空なら点線の貼付枠のみ。3:4 でない画像は中央基準で切られる |
\genjusho{ふりがな}{郵便番号}{住所}{電話番号} |
現住所 |
\renrakusaki{ふりがな}{郵便番号}{住所}{電話番号} |
連絡先。現住所と同じなら {}{}{同上}{} |
\gakureki{年}{月}{内容} |
学歴・職歴。書いた順に並ぶ |
\menkyo{年}{月}{名称} |
免許・資格 |
\shibou{} |
志望の動機、特技、アピールポイントなど |
\kibou{} |
本人希望記入欄 |
3. 記入する
blank.tex のコメント（各欄の説明）は消さずに残し、値だけを入れる。
学歴・職歴の書式:
\gakureki{}{}{\hfil 学歴\hfil\null}
\gakureki{2009}{4}{○○県立○○高等学校 入学}
\gakureki{2012}{3}{○○県立○○高等学校 卒業}
\gakureki{}{}{}
\gakureki{}{}{\hfil 職歴\hfil\null}
\gakureki{2012}{4}{株式会社○○ 入社}
\gakureki{}{}{\hfill 以上}
- 「学歴」「職歴」の見出し行、学歴と職歴の間の空行、末尾の「以上」も 1 行として数える。
- 年は西暦 4 桁、月は数字のみ（
\gakureki{2012}{3}{...}）。学校名・会社名と「入学／卒業／入社／退職」の間は全角空白。 - 在職中なら末尾は
\gakureki{}{}{\hfill 現在に至る}の次行に\gakureki{}{}{\hfill 以上}。
行数の上限（超えた分は警告なく捨てられる）:
- 学歴・職歴: 1 ページ目 15 行 ＋ 2 ページ目 7 行 = 合計 22 行（
\rirekiRowsGakurekiA/\rirekiRowsGakurekiB） - 免許・資格: 6 行（
\rirekiRowsMenkyo、次ページ送りなし）
上限を超えるときは勝手に間引かず、どれを削る／まとめるかをユーザーに確認する。
\shibou{} と \kibou{} は欄の高さが固定（志望の動機 47mm、本人希望 30.4mm）なので、はみ出すと欄外に流れる。長い場合はビルド後の PDF で必ず確認する。
4. lint とビルド
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.
- 10d ago First seen · 86 lines · 102 tokens per session scan A ef8be46b337f
rirekisho is a skill published in the GitHub repository jugesuke/rirekisho (47 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 1,716 once invoked, about $0.0005 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.
Other skills, from other repositories
pydicom
Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
skill-doc-delivery
Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.
pdf-extract-create-workflow
Complete PDF lifecycle: download, extract, and generate structured documents with reportlab.
document-direct-python
Use direct Python execution for reliable document creation including spreadsheets, PDFs, and structured reports.
parse-document
Convert a PDF, scan, image of a page, or office file to clean markdown through the connected Superlinked MCP edge, so the source document is not read into model context directly. Use when the user asks to read, parse, OCR, extract from, summarize, or answer questions about a document.