ctf-ai-ml は何をしますか？
CTFチャレンジ向けのAIおよび機械学習技術を提供します。MLモデルへの攻撃、敵対的サンプルの作成、モデル抽出、プロンプトインジェクション、メンバーシップ推論、トレーニングデータポイズニング、ファインチューニング操作、ニューラルネットワーク分析、LoRAアダプターの悪用、LLM脱獄、またはAI関連パズルの解決に使用します。
CTFチャレンジ向けのAIおよび機械学習技術を提供します。MLモデルへの攻撃、敵対的サンプルの作成、モデル抽出、プロンプトインジェクション、メンバーシップ推論、トレーニングデータポイズニング、ファインチューニング操作、ニューラルネットワーク分析、LoRAアダプターの悪用、LLM脱獄、またはAI関連パズルの解決に使用します。
このスキルは、Capture The Flag（CTF）コンテストにおいて、AIや機械学習に関連する課題を解決するために設計されています。具体的には、MLモデルへの敵対的攻撃（敵対的サンプルの作成、モデル抽出、プロンプトインジェクション、メンバーシップ推論、トレーニングデータポイズニング、ファインチューニング操作）、ニューラルネットワーク解析、LoRAアダプターの悪用、LLM脱獄といった高度な技術を適用します。このスキルは、Claude CodeやCursor、CodexなどのAIエージェントプラットフォームで活用でき、開発者はAI関連のパズルを解いたり、機械学習モデルの脆弱性を調査したりする際に役立ちます。スキルのドキュメント（SKILL.md）には、具体的な使用例や攻撃手法の解説が含まれており、CTF参加者が効率的に問題を解決するための実践的な知識を提供します。
npx skills add https://github.com/ljagiello/ctf-skills/tree/main/skills/ctf-ai-mlInstalled? Explore more 研究＆データ分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
CTFチャレンジ向けのAIおよび機械学習技術を提供します。MLモデルへの攻撃、敵対的サンプルの作成、モデル抽出、プロンプトインジェクション、メンバーシップ推論、トレーニングデータポイズニング、ファインチューニング操作、ニューラルネットワーク分析、LoRAアダプターの悪用、LLM脱獄、またはAI関連パズルの解決に使用します。
This repo contains 10 individual skills — each has its own dedicated page.
Provides cryptography attack techniques for CTF challenges. Use when attacking encryption, hashing, signatures, ZKP, PRNG, or mathematical crypto problems involving RSA, AES, ECC, lattices, LWE, CVP, number theory, Coppersmith, Pollard, Wiener, padding oracle, GCM, key derivation, or stream/block cipher weaknesses.
Provides digital forensics and signal analysis techniques for CTF challenges. Use when analyzing disk images, memory dumps, event logs, network captures, cryptocurrency transactions, steganography, PDF analysis, Windows registry, Volatility, PCAP, Docker images, coredumps, side-channel power traces, DTMF audio spectrograms, packet timing analysis, CD audio disc images, or recovering deleted files and credentials.
Provides malware analysis and network traffic techniques for CTF challenges. Use when analyzing obfuscated scripts, malicious packages, custom crypto protocols, C2 traffic, PE/.NET binaries, RC4/AES encrypted communications, YARA rules, shellcode analysis, memory forensics for malware (Volatility malfind, process injection detection), anti-analysis techniques (VM/sandbox detection, timing evasion, API hashing, process injection, environment checks), or extracting malware configurations and indicators of compromise.
Provides miscellaneous CTF challenge techniques for problems that do not cleanly fit the main categories. Use for encoding puzzles, pyjails, bash jails, RF/SDR, DNS oddities, unicode tricks, esoteric languages, QR or audio puzzles, constraint solving, game theory, unusual sandbox escapes, and hybrid logic puzzles. Prefer a more specific skill first when the challenge is mainly web, pwn, reverse, forensics, malware, OSINT, or crypto. Treat this as the fallback skill for genuine cross-category or edge-case challenges, not the default starting point.
Provides open source intelligence techniques for CTF challenges. Use when gathering information from public sources, social media, geolocation, DNS records, username enumeration, reverse image search, Google dorking, Wayback Machine, Tor relays, FEC filings, or identifying unknown data like hashes and coordinates.
Provides binary exploitation techniques for CTF challenges. Use when you already have a vulnerable native target or service and need to turn memory corruption or low-level primitives into code execution or privilege escalation, such as buffer overflows, format strings, heap bugs, ROP, ret2libc, shellcode, kernel exploitation, seccomp bypass, sandbox escape, or Windows/Linux exploit chains. Do not use it when the main blocker is understanding what the binary does; use reverse engineering first. Do not use it for pure web bugs, disk or packet forensics, or standalone crypto/math challenges.
Provides reverse engineering techniques for CTF challenges. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, game clients, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.
Provides web exploitation techniques for CTF challenges. Use when the target is primarily an HTTP application, API, browser client, template engine, identity flow, or smart-contract frontend/backend surface, including XSS, SQLi, SSTI, SSRF, XXE, JWT, auth bypass, file upload, request smuggling, OAuth/OIDC, SAML, prototype pollution, and similar web bugs. Do not use it for native binary memory corruption, reverse engineering of standalone executables, disk or memory forensics, or pure cryptanalysis unless the web flaw is still the main path to the flag.
Generates a single standardized submission-style CTF writeup for competition handoff and organizer review. Use after solving a CTF challenge to document the solution steps, tools used, and lessons learned in a structured format.
Solves CTF challenges by performing first-pass triage, identifying the dominant category, and routing execution to the right specialized ctf-* skill. Use when the user gives you a challenge bundle, a remote service, a suspicious file, or only a vague challenge description and you must determine where to start. Do not use it when the category is already clear and a specialized skill can be invoked directly; this is the dispatcher and recon entrypoint, not the deepest reference for category-specific techniques.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.