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概要と使いどころ
Post a "what's changed since the last release" recap to the release Slack channel for final QA. Groups merged PRs by author and classifies each as feature / bug fix / task. Use when the user wants a release recap, a changelog since the last tag, or to prep QA before cutting a release from main.
日本語の概要は準備中です。原文の説明を表示しています。
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.
日本語の概要は準備中です。原文の説明を表示しています。
Operate any Obsidian vault as a living, self-rewriting second brain (an evolution of Karpathy's LLM Wiki pattern: sources rewrite existing pages, contradictions reconcile automatically, scheduled agents maintain the vault while you sleep). Use this skill whenever the user asks Claude to read, write, update, search, or manage their Obsidian vault - including saving notes from conversation, creating daily entries, updating kanban boards, logging dev work, managing people notes, capturing decisions, tracking deals, or maintaining any vault structure. Also triggers when the user wants to bootstrap a new vault from scratch, run a vault health check, or drop a _CLAUDE.md into their vault so all Claude surfaces share the same operating rules. Includes a research toolkit (7 commands: /x-read, /x-pulse, /research, /research-deep, /notebooklm, /youtube, /podcast) for AI-powered research via Grok, Perplexity, NotebookLM, YouTube, and podcast feeds - findings save to the vault automatically following the AI-first vault rule. Use proactively whenever the conversation produces information worth preserving (decisions, people met, projects started, tasks completed, lessons learned, research findings).
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when the user asks to "attack Active Directory", "exploit AD", "Kerberoasting", "DCSync", "pass-the-hash", "BloodHound enumeration", "Golden Ticket", "Silver Ticket", "AS-REP roasting", "NTLM relay", or needs guidance on Windows domain penetration testing.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze codebases and automatically generate architecture diagrams, flowcharts, and org charts. Uses AST parsing to map import dependencies for Python, JS/TS, Go, and Java, outputting Mermaid or SVG files. Triggered when users ask to visualize code architecture, understand dependencies, draw a flowchart, or create a module diagram from source code.
日本語の概要は準備中です。原文の説明を表示しています。
Query, display, and resolve reconciliation gaps from the TRES Finance MCP connector. Trigger this skill ONLY when the user explicitly requests to view or close reconciliation gaps — for example: "show me the reconciliation gaps", "close the recon gaps", "view gaps for today", "plug the reconciliation gaps for ETH", "fix the gaps for USDC and BTC". Do NOT trigger for general balance queries, transaction history, or ledger browsing. Covers the full workflow: confirming the target date, optionally filtering by asset, fetching gap data, enriching with fiat and on-chain balances, rendering an HTML dashboard where each gap row has copy-prompt buttons (Plug / Auto-fill) — clicking copies a ready-to-paste Claude prompt for that gap. The user pastes it in the chat and Claude executes the action. Ends with a data collect once the user is done.
日本語の概要は準備中です。原文の説明を表示しています。
Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.
日本語の概要は準備中です。原文の説明を表示しています。
Chinese LaTeX thesis assistant for existing .tex degree thesis projects (XeLaTeX/LuaLaTeX/latexmk). Use this skill whenever a user works on a Chinese master's or doctoral thesis needing compilation, GB/T 7714 bibliography checks, chapter structure mapping, template detection (thuthesis, pkuthss), terminology consistency, logic coherence review, heading lead-in checks, title optimization, de-AI editing, experiment chapter review, three-line table validation, or abstract structure diagnosis. Trigger even for single issues like "帮我编译论文", "检查国标格式", "看看绪论逻辑", "毕业论文", "学位论文", "硕士/博士论文", "三线表", "检查摘要", or "摘要结构".
日本語の概要は準備中です。原文の説明を表示しています。
Econometrics skill for OLS regression and linear models. Activates when the user asks about: "run OLS", "linear regression", "ordinary least squares", "interpret regression results", "heteroskedasticity", "multicollinearity", "regression assumptions", "robust standard errors", "GLS", "WLS", "fit a regression model", "check regression diagnostics", "OLS假设", "最小二乘法", "线性回归", "回归系数", "残差检验", "异方差", "多重共线性", "普通最小二乘", "稳健标准误", "回归诊断"
日本語の概要は準備中です。原文の説明を表示しています。
Runs your conversion data through different attribution models side by side — last click, first click, linear, time decay, position based, and data-driven. Shows you how credit shifts between campaigns depending on the model so you can make better budget decisions instead of over-investing in last-touch campaigns. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive Google Ads account health analysis detecting wasted spend, search term leaks, negative keyword gaps, bid strategy issues, and Quality Score problems. Use when analyzing campaign data, pasting Google Ads exports, reviewing account performance, or requesting a full diagnostic of advertising spend efficiency. Platform: Google.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill to cut an APM release from the current worktree: assess whether the cycle since the last tag warrants a patch or minor bump (semver discipline against the merged-since-last-tag diff), sanitize the [Unreleased] CHANGELOG block into a dated version block with one concise "so what" entry per merged PR (drop internal-only churn, consolidate duplicates), bump pyproject.toml + uv.lock, run the CI-mirror lint chain, and open the release PR. Activate on "ship a release", "cut v0.x", "release prep", "bump and PR", "open release PR", "what kind of release do we need", or any phrasing that ends in opening a release PR -- even when the user does not say "skill". Stops BEFORE tagging; tagging stays a human gate that triggers the release workflow. Refuses to bump to a major (>= 1.0.0) version without explicit operator confirmation.
日本語の概要は準備中です。原文の説明を表示しています。
Organize menus and visit notes with grounded tasting analysis and comparisons to earlier experiences. Use when the user shares a restaurant menu, wants a tasting write-up, or asks to merge a visit into Notes/Gourmet.md without overwriting earlier visits.
日本語の概要は準備中です。原文の説明を表示しています。
Locate and read a Codex thread by a codex thread link, thread id, or rollout path across all local CODEX_HOME directories (~/.codex, ~/.codex-gpt, ...). Use when the user pastes a codex:// link, asks to understand the 前因后果 of a Codex session, wants a timeline of what a long-running main thread did, or needs to search / export a past Codex conversation.
日本語の概要は準備中です。原文の説明を表示しています。
Fast codebase search via WarpGrep (20x faster than grep)
日本語の概要は準備中です。原文の説明を表示しています。
Reference guide for Agentica multi-agent infrastructure APIs
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. Use for waste, negatives, budgets, bid changes, poor CPA or ROAS, underpacing, overspend, or scaling decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Investigate fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool call/result correlation, and common failure patterns.
日本語の概要は準備中です。原文の説明を表示しています。
Searches and rewrites code by AST shape across 25 languages. Use when the target is a syntax pattern (every call/class/import shaped like X, a codemod, a YAML rule) rather than literal text; for plain strings, comments, or filenames, use rg.
日本語の概要は準備中です。原文の説明を表示しています。
Web+AI 安全测试知识库。融合 WooYun 88,636 案例 + 先知 L1-L4 方法论 + GAARM 150 风险 + OWASP Top 10 (LLM/ASI/WSTG)。 TRIGGER when 任务是实战安全测试：渗透测试、漏洞挖掘/利用、红队攻防、安全审计 (SAST/DAST)、 CTF、AI/LLM 安全测试 (Prompt 注入/越狱/MCP/Agent/沙箱逃逸)。用户明确给出测试目标 (URL/代码/模型/Agent 架构) 且意图是"测试/审计/挖漏洞/利用"。 DO NOT trigger: - 安全概念讨论（"什么是 XSS"、"SQL 注入原理是什么"）→ 普通问答 - 非安全性质的 code review / debug / 性能优化 → code-audit-skill 或其他 - 修复语法错误 / 业务逻辑 bug → 普通编程协助 - 纯 Web 白盒代码审计（完整项目目录 / Source-Sink 污点分析）→ code-audit-skill - 仅引用 CVE 编号查文档 → WebSearch 边界细则: CTF 短代码片段 + 利用思路 → 本 Skill；完整项目目录 + 系统白盒审计 → code-audit-skill
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
日本語の概要は準備中です。原文の説明を表示しています。
Property-based testing with fast-check (TypeScript/JavaScript) and Hypothesis (Python). Generate test cases automatically, find edge cases, and test mathematical properties. Use when user mentions property-based testing, fast-check, Hypothesis, generating test data, QuickCheck-style testing, or finding edge cases automatically.
日本語の概要は準備中です。原文の説明を表示しています。
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, and 7 programming languages (Python, R, Julia, JavaScript, C++, Java, Go) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
日本語の概要は準備中です。原文の説明を表示しています。