1.8万 件 ・ 関連度順
概要と使いどころ
Generates semantic commit messages following the Conventional Commits specification with proper types, scopes, breaking changes, and footers. Use when users request "write commit message", "conventional commit", "semantic commit", or "format commit".
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Generates Bruno collection files (.bru) from Express, Next.js, Fastify, or other API routes. Creates organized collections with environments, authentication, and folder structure for the open-source Bruno API client. Use when users request "generate bruno collection", "bruno api testing", "create bru files", or "bruno import".
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。
Orchestrates multi-agent AI systems with task delegation, agent communication, shared memory, and workflow coordination. Use when users request "multi-agent system", "agent orchestration", "AI agents", "agent coordination", or "autonomous agents".
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Generates API documentation using OpenAPI/Swagger specifications with interactive documentation, code examples, and SDK generation. Use when users request "API documentation", "OpenAPI spec", "Swagger docs", "document API endpoints", or "generate API reference".
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。
Hardens API security with rate limiting, input validation, authentication, and protection against common attacks. Use when users request "API security", "secure API", "rate limiting", "input validation", or "API protection".
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Use when a running Spark or DSH web service needs real-browser verification — page load, client-plugin boot errors, console errors, failed requests, or onboarding/dialog rendering — with the repository's own Playwright chromium.
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 working Spark implementation should be simplified without changing its observed behavior, ownership, compatibility, or public contract.
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 Spark engineering knowledge must be placed, deduplicated, migrated, or validated across AGENTS, Notes, Roles, Skills, and Workflows.
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 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.
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Use when adapting academic findings for a public audience (op-ed, social post, podcast) and the simplification must stay evidence-faithful, avoid overclaiming, respect embargoes, and disclose AI assistance.
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 a high-stakes analysis, citation set, or decision should be checked by a second independent AI agent that verifies rather than decides, or when disagreement between agents must be surfaced for the human to adjudicate.
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 sensitive, clinical, identifying, or raw interview data is placed in front of an AI tool, when transcripts or student materials need de-identification, or when a data-minimization gate must run before any analysis or drafting.
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.
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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.
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Use when a researcher asks where to start, which project skill fits the current stage of a study, or in what order literature scoping, source access, vault upkeep, drafting, citation verification, disclosure, and review response should run.
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Transform brain dumps into polished blog posts in Nick Nisi's voice. Use when the user wants to write a blog post with scattered ideas, talking points, and conclusions that need organization into a cohesive narrative with Nick's conversational, authentic, and thoughtful tone.
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Z-Wave attack methodology — sniffing with Z-Force / EZ-Wave / RTL-SDR + ZniffMobile, S0 (legacy) network-key derivation flaw and key reuse, S2 (modern) ECDH commissioning analysis, replay/injection on unauthenticated nodes, default-key brute-force on test deployments, and home-automation hub pivots. Use when targeting Z-Wave smart home devices (door locks, sensors, garage controllers) — common in mid-2010s smart home deployments still in production.
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Deauthentication and disassociation attacks against 802.11 networks — targeted single-client deauth for handshake capture, broadcast deauth for DoS (with authorization), action-frame attacks bypassing 802.11w (PMF), beacon flooding, mdk4 / aireplay-ng tooling, and rate-limit / PMF-aware operation. Use to coerce client reconnection (handshake capture, evil-twin roaming), as targeted DoS, or to test PMF posture.
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。
You are helping a penetration tester exploit XXE injection. The target application
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Bluetooth Low Energy (BLE) attack methodology — GATT enumeration, characteristic read/write without auth, pairing downgrade (Just Works forced), LE Secure Connections bypass, MITM via active relay, sniffing with Sniffle (TI CC1352) / Ubertooth / Frontline, encryption key extraction (LE Legacy Pairing crackable, LE Secure Connections strong), proximity authentication abuse (cars, locks), and companion-app trust analysis. Use for IoT BLE devices, smart locks, fitness trackers, medical devices, BLE beacons, or any device pairing over BLE.
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Bluetooth Classic (BR/EDR) attack methodology — device discovery, service enumeration via SDP, LMP/L2CAP layer attacks, legacy PIN cracking (BlueBorne / KNOB), Bluetooth file-transfer abuse (BlueSnarfing legacy), unauthenticated profile abuse (HSP, HFP, OPP), and modern relevance against older industrial / automotive / accessory targets. Use when in-scope devices use Bluetooth Classic (Bluetooth ≤ 4.0 BR/EDR) — common in legacy car kits, industrial sensors, older medical devices, and audio accessories.
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。