2.5万 件 ・ 関連度順
概要と使いどころ
Creates Architecture Decision Records documenting key technical decisions with context, alternatives considered, tradeoffs, consequences, and decision owners. Use when documenting "architecture decisions", "technical choices", "design decisions", or "ADRs".
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 when a Spark change needs its authoritative owner, affected boundaries, risks, acceptance criteria, and smallest verifiable slice established before implementation or review.
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 when a Spark implementation needs an evidence-based review for correctness, ownership, compatibility, failure handling, maintainability, and unnecessary complexity.
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 when a Spark branch or pull request needs final diff hygiene, validation selection, commit scope, remote topology, and readiness checked before publication.
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 when designing a Spark feature or writing or materially revising a design proposal that needs repository-first research, user input on unresolved key choices, and an owner-aligned implementation plan before code changes.
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 when an ethics committee or IRB application involves AI anywhere in the pipeline, when KVKK, GDPR, or EU AI Act duties must be mapped to a study, or when consent and disclosure language about AI use needs drafting before submission.
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 when coding interviews, focus groups, or open-ended responses with AI assistance, when a codebook needs building or auditing, when intercoder reliability must be computed, or when qualitative quotes need integrity checks before publication.
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 when automating research session rituals in Claude Code with hooks, such as session-start context injection, daily research logging, session-end persistence, or pre-commit guards that block secrets and unverified citations.
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 before a tag, GitHub release, or Zenodo archive, when README, CATALOG, CHANGELOG, CITATION.cff, the disclosure aggregate, and skill discovery must agree, or when a go or no-go release verdict needs recorded evidence behind it.
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 when a systematic, scoping, or rapid review needs a reproducible pipeline: protocol framing (PICO/SPIDER/PCC), a logged search string, title/abstract and full-text screening with recorded exclusion reasons, and PRISMA flow counts, with the human as the deciding screener.
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 when AI contribution metadata must match the actual workflow, when model alias and dated identifiers need auditing, when verified and fabricated citation counts feed a release decision, or when a venue's AI disclosure statement needs evidence behind it.
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 when paired tr.md and en.md files need a parity verdict, when titles, frontmatter, headings, or DOI sets may have drifted between languages, or when a bilingual document is approaching release and its translation coverage needs an audit.
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 when a study needs a preregistration or a frozen analysis plan, when confirmatory and exploratory analyses must be separated, or when every deviation from the registered plan must be logged before the result is reported.
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 when a research proposal needs work packages, milestones, deliverables, a timeline, a risk register, and budget-justification scaffolding, while keeping scientific claims and feasibility honest and human-owned.
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 when choosing a journal for a finished or nearly finished manuscript, when index status in Scopus, SSCI, ESCI, or TR Dizin must be confirmed, when a venue looks predatory, or when a cover letter needs drafting from the manuscript's actual claims.
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 when authorship order, CRediT contribution roles, or AI-assistance attribution must be recorded transparently for a manuscript, when an author dispute needs an evidence trail, or before a contributorship statement is required at submission; this skill maintains the human-attribution ledger of who contributed what and in which role (authorship order, CRediT taxonomy, revision responsibilities), which is distinct from the disclosure-metadata audit owned by ai-disclosure-auditor.
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。
LoRaWAN and sub-GHz (433 / 868 / 915 MHz) attack methodology — LoRaWAN ABP/OTAA join attack, network/session key reuse, frame counter replay, downlink injection on TTN/Helium-style networks, sub-GHz protocol replay (KeeLoq garage doors, fixed-code remotes, TPMS spoofing, smart plug telemetry), HackRF / RTL-SDR / Flipper Zero workflows, signal analysis with Inspectrum / Universal Radio Hacker, and reconstruction of proprietary packet formats. Use for LoRaWAN deployments (smart cities, asset tracking, industrial telemetry), or any wireless device using the unlicensed 433/868/915 MHz bands (garage openers, doorbells, IoT sensors, RC equipment).
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。
Evil Twin / KARMA / Mana access point methodology — rogue AP construction with hostapd-mana / wifiphisher / airgeddon, KARMA universal probe response, Mana selective probe response, captive portal phishing, deauth-driven client coercion to attacker AP, MAC randomization defeat via PNL leak analysis, post-association MITM (DNS, ARP, transparent proxy), credential capture for portal/web/SMB, and detection-evasion tactics. Use to coerce client devices onto an attacker-controlled AP, intercept their traffic, harvest credentials, or deliver payloads via captive portal.
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。
WPA/WPA2-PSK attack methodology — four-way handshake capture via targeted deauthentication, PMKID attacks (no client required), hcxdumptool / hcxpcapngtool conversion to hashcat hc22000 format, GPU-accelerated cracking with dictionary, mask, and rule-based attacks, vendor default-PSK generators (UPC, Sky, BT, etc.), 802.11r FT key cracking, opportunistic key cache analysis, and signal-level optimization. Use when the in-scope network is WPA/WPA2 Personal — the most common consumer/SMB encryption mode.
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。
Wi-Fi reconnaissance methodology — adapter selection, monitor mode and packet injection setup, regulatory domain handling, multi-band airspace mapping, hidden SSID discovery, BSSID/ESSID/channel/PMF/encryption fingerprinting, client probe analysis, vendor OUI lookup, war-driving with Kismet/airodump-ng/Wigle, and structured airspace data capture for downstream attacks. Use at the start of any wireless engagement to build the target map before active attacks; covers 2.4 GHz, 5 GHz, and 6 GHz (Wi-Fi 6E) bands and adapter compatibility for each.
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。
Hunt HTML Injection — user-supplied input is rendered as raw HTML in the response without sanitisation, allowing an attacker to inject arbitrary HTML tags (but not necessarily JavaScript). Lower severity than XSS but enables phishing, UI manipulation, and credential harvesting via injected forms. Use when testing text-display surfaces (search results, profile fields, comments, error messages, feedback forms). For markup that executes JavaScript, escalate to hunt-xss.
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。
Discover a single-page-app's hidden backend API from its public JS bundle, then test that API for broken access control / missing authentication. One of the highest-yield web plays in modern recon — SPAs ship their entire backend route map to the browser, and the API behind them is frequently missing the auth middleware the login page implies. Built from an authorized engagement where this play found an unauthenticated financial API that an ASM scan reporting hundreds of 'Criticals' completely missed. Use whenever a target serves a JS-heavy SPA (React/Vue/Angular/Next), an 'app'/'console'/'dashboard'/'portal' subdomain, or any `*api*` host shows up in recon. Leaked build artifacts (source maps / .env / .git / asset-manifest) are owned by hunt-source-leak; API version-inventory and behavioral diffing by hunt-shadow-api; this skill owns mapping a live SPA's backend routes from its JS bundle and testing them for broken access control / missing auth.
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。
Cross-Site Scripting testing checklist: stored/reflected/DOM/blind XSS discovery, polyglot payloads, CSP bypass, XSS filter bypass, event handler injection, DOM clobbering, mutation XSS, and impact escalation (session hijack, phishing, keylogging). Use for web app XSS testing and bug bounty.
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。
Hunting skill for sqli vulnerabilities. Built from 12 public bug bounty reports including modern NoSQL injection (Rocket.Chat CVE-2021-22911 MongoDB $regex, Mongoose ORM CVE-2024-53900 $where bypass), modern ORM raw-fragment SQLi (Django CVE-2024-42005, Sequelize GHSA-wrh9-cjv3-2hpw), second-order SOQL injection (HackerOne Salesforce), time-based blind SQLi in GraphQL resolvers, and SQLi on OIDC-proxy backends. Use when hunting SQLi on any target. Dedicated NoSQL operator injection (MongoDB/CouchDB $where/$regex/$ne) is owned by hunt-nosqli — NoSQL appears here only as adjacent ORM/WAF context.
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。