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Manage files and folders on the Unique AI Platform using the unique-cli command-line tool. Use when the user asks to upload, download, delete, rename, list, find, restore versions, list versions, look for, or organize files and folders on Unique, or to read / view / quote the text contents of a known file (optionally by page or page range, e.g. "what's on page 5?", "read pages 10-12"), or when working with scope IDs (scope_*) or content IDs (cont_*). IMPORTANT: When a user says they are "looking for a file" or wants to "find a file", they typically mean locating it within the Unique AI Platform knowledge base — not on the local filesystem. Use this skill to browse and list folders/files on Unique to help them find it. This is also the preferred approach for locating Excel (.xlsx/.xls), CSV (.csv), and image files, as these file types are not full-text indexed and cannot be found via vector/full-text search.

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Search the Unique AI Platform knowledge base using the unique-cli search command, with automatic per-turn citation tracking so cited facts render as clickable reference chips and `<sup>N</sup>` footnotes on the Unique platform. Use whenever the user asks to find, search, or query documents or content on Unique, including filtering by folder or metadata. Also covers `unique-cli read <cont_id>` for reading the full indexed text of a document when its content ID is already known. NOTE: This search uses combined vector + full-text indexing. Excel (.xlsx/.xls), CSV (.csv), and image files are NOT full-text indexed, so they will not appear in search results. To locate these file types, use the unique-cli-file-management skill instead (browse folders with `unique-cli ls` to find them by name).

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係数全開示型のOSS（オープンソース）AVM、「AVM界のLinux」を目指す土地査定エンジン。日本の土地（更地・所有権）の査定価格をMLIT取引事例から統計的に算定する白箱AVMスキル。媒介査定・売却価格の検討・お客様への査定書作成・相場確認・取引事例比較・比準価格算定・公示価格との比較など、土地の価格を客観的かつ説明可能な形で出したい時は、ユーザーが「ヘドニック」「回帰」「白箱」と言わなくても必ずこのSkillを使うこと。トリガー語例：「土地の査定」「土地査定して」「査定書を作って」「査定書がほしい」「いくらで売れる」「売却価格」「査定価格」「相場感」「相場を出して」「マーケット価格」「取引事例から」「事例比較で」「比準価格」「公示価格と比べて」「お客さんに渡す査定書」「顧客提示用の資料」「土地価格査定クン」「土地査定クン」「重調クンの土地版」「説明できる査定」「根拠付きで査定」「AVM」「自動査定」「鑑定書ではなく査定書として」など。MLIT取引価格情報CSV と地価公示GeoJSON（L01）を入力に（基準地価は不可）、ヘドニック係数を都度回帰で算定して個別格差補正を全開示する白箱AVM方式で、業者用シート（係数全開示）と顧客用シート（流推方式準拠）の2シート構成xlsxを出力する。マンション・戸建（建物含む）・借地権・地上権は対象外。

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大学業務で「この資料を AI に入れて大丈夫か」「どのサービスなら安全か」と 判断が求められた時に使う、機密情報取扱の判断フレームワーク。情報分類が 個人感覚に揺れている現場で、3 段分類 (Level 1: 公開 / Level 2: 学内限定 / Level 3: 機密) に基づく一貫した可否判断を提供する。テキスト本文だけでなく ファイルメタデータ / EXIF / OCR リスクや、Custom GPT / Projects / Gem 等の 事前設定機能に登録する情報も同じ軸で扱う。所属大学規程が優先される前提での 汎用的な判定軸であり、法的助言ではない。

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大学業務で AI に入力しようとする情報を Level 1/2/3 に判定し、入力可否と 推奨次アクションを返す。「この資料を AI に入れて大丈夫？」「議事録を 要約させたいが機密度は？」と問われた時、または職員がファイル / テキストを 共有して AI 利用可否判断を求めた時に起動する。判定基準は `confidential-info-guidelines` の 3 段分類に依拠し、所属大学規程が優先される 旨を出力末尾に必ず添える。匿名化 / 統計化による Level 降格の検討も提示する。

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Automatically syncs Gmail and Google Calendar with a Notion database to keep track of the status of job applications the user has submitted. Use this skill whenever the user asks to "sync my job applications," "update my job tracker," "check where my interviews stand," or something similar — even if they don't explicitly mention Notion, Gmail, or Calendar, and even if they don't have a Notion database set up for this yet. Requires (or guides the user through setting up) the Notion, Gmail, and Google Calendar MCP connectors.

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Build, analyze, repair, and audit finance three-statement models and operating forecasts. Use when the user invokes $kelly-finance, asks for 财务三表, 三表模型, income statement, balance sheet, cash flow statement, forecast model, budget model, financial projections, FP&A model, SaaS/unit economics forecast, fundraising model, board finance pack, investor model, model checks, balance-sheet balancing, cash runway, working capital, capex/depreciation schedules, debt schedules, scenario/sensitivity cases, or Excel/Sheets-ready finance outputs.

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Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection. Places strategically named decoy files that mimic high-value targets (financial records, credentials, database exports) in locations ransomware typically enumerates first. Monitors for any read, modify, rename, or delete operations on canary files and triggers immediate alerts via email, Slack webhook, or syslog when interaction is detected, providing early warning before full encryption begins.

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Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.

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Analyzes malicious VBA macros embedded in Microsoft Office documents (Word, Excel, PowerPoint) to identify download cradles, payload execution, persistence mechanisms, and anti-analysis techniques. Uses olevba, oledump, and VBA deobfuscation to extract the attack chain. Activates for requests involving Office macro analysis, VBA malware investigation, maldoc analysis, or document-based threat examination.

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Analyze Chromium-based browser artifacts using Hindsight to extract browsing history, downloads, cookies, cached content, autofill data, saved passwords, and browser extensions from Chrome, Edge, Brave, and Opera for forensic investigation.

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Analyze Windows LNK shortcut files and Jump List artifacts to establish evidence of file access, program execution, and user activity using LECmd, JLECmd, and manual binary parsing of the Shell Link Binary format.

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Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

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Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains `*_executed.ipynb`, run `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square, correlation, power), tooluniverse-image-analysis (microscopy, colony, fluorescence, dose-response, .tif — including ANOVA / Dunnett / power-analysis on image-derived measurements), tooluniverse-epigenomics (DNA methylation, CpG, m6A, MeRIP-seq, bisulfite, ChIP-seq, chromatin), tooluniverse-sequence-analysis (FASTQ, Trimmomatic, BWA, samtools, coverage), tooluniverse-variant-analysis (VCF, VAF, SNP, mutation), tooluniverse-phylogenetics (treeness, PhyKIT, parsimony), tooluniverse-single-cell (scRNA, h5ad, scanpy), tooluniverse-crispr-screen-analysis (MAGeCK, sgRNA), tooluniverse-proteomics-analysis (mass spec, TMT). Use for CSV/Excel/VCF/FASTA/h5ad and any biology/chemistry/medicine analysis question.

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Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

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Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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Generate CSV, Markdown, and XLSX spreadsheets from structured tabular data.

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Generate a formatted XLSX spreadsheet with sales data, currency formatting, and bold headers.

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インタビューガイドの対象者タイプと候補者リストから最適な対象者を選定・評価するスキル。「インタビュー対象者を選定して」「候補者を評価して」「エキスパートリストから対象者を選んで」などのリクエスト時に使用。論点への対応力を軸に適合性を評価し、優先順位付けされた推奨候補リストを作成する。

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最適化モデルの運用設計。自動化・監視・フォールバックを定義する

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