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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 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 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 a complete research workflow — its instructions, skills, data boundaries, and tool connections — must be assessed for portability across AI agents or environments, so each component is explicitly mapped before migration rather than assumed to transfer.

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 construct, scale, or key term crosses Turkish and English and conceptual equivalence must be audited: translation and back-translation, construct drift, and culturally bound meaning, before measurement or cross-cultural 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。

Use when designing a course, assessment, or student-feedback workflow and deciding where AI assistance is acceptable, when grading or feedback must stay within academic-integrity and FERPA/KVKK boundaries, or when AI-use rules for students need stating.

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。

Manage git worktrees using a centralized worktrees folder. Use for parallel development, PR reviews, or isolated work.

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。

Creates and manages Biome GritQL custom lint rules to enforce coding patterns. Use when creating linter rules, enforcing code conventions, preventing anti-patterns, or when the user mentions Biome, GritQL, custom lint rules, or AST-based linting.

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。

Generate clean, minimal technical SVG diagrams in a consistent Cloudflare-inspired style. Use when creating architecture diagrams, flow diagrams, or component diagrams for blog posts and documentation.

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 "improve test coverage", "add unit tests", "TDD", "test this module", "write tests for", "increase coverage", "/ut command", or discusses unit testing strategies. Covers the unit test loop workflow, React Testing Library best practices, query priorities, and coverage improvement strategies.

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。

Zigbee, Thread, and Matter mesh-protocol attack methodology — IEEE 802.15.4 sniffing with TI CC2531 / CC2540 / Sonoff Zigbee Dongle E, KillerBee toolkit, Touchlink commissioning abuse with the well-known transport key, replay/injection attacks, Zigbee Cluster Library command abuse for door locks and bulbs, Thread network credential theft, Matter commissioning chain analysis, and 6LoWPAN/IPv6 routing exploitation. Use when targeting smart-home or commercial mesh deployments, Zigbee-based door locks, lighting, or sensor networks.

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。

You are helping a penetration tester deploy webshells to web server document

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IoT and embedded device security testing methodology. Covers hardware reconnaissance (UART, JTAG, SWD, SPI flash, I2C EEPROM, eMMC chip-off), firmware acquisition (vendor portals, OTA capture, flash dump, binwalk extraction), firmware analysis (filesystem mounting, binary triage, hardcoded secrets, default credential discovery), bootloader attacks (U-Boot console, secure-boot bypass, fault injection), runtime attacks on embedded Linux/RTOS (busybox CVEs, MTD writes, /dev/mem), wireless protocol attacks (Zigbee, BLE, Z-Wave, LoRaWAN, Thread/Matter, sub-GHz), MQTT/CoAP/Modbus/BACnet/OPC-UA exploitation, mobile companion app analysis, cloud-IoT API abuse, and side-channel/glitching basics. Use for IoT pentest, smart-home assessment, ICS/OT testing, or embedded vulnerability research.

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WPA3 / SAE (Simultaneous Authentication of Equals) attack methodology — transition-mode (mixed WPA2/WPA3) downgrade, Dragonblood side-channel attacks (CVE-2019-9494, 9495, 13377, 13456), SAE auth flooding for AP CPU exhaustion, Hash-to-Element (H2E) timing analysis, group downgrade, and 6 GHz / Wi-Fi 6E spec implications (PMF mandatory, no transition mode allowed). Use when target advertises WPA3-SAE or WPA3-Personal/Enterprise, or operates in 6 GHz where WPA3 + PMF are required by spec.

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。

Time-of-Check / Time-of-Use (TOCTOU) race condition exploitation methodology across binary, kernel, filesystem, web, and container layers. Covers symbolic-link races (open/access/stat split), file-descriptor races, fopen/realpath traversal races, /proc and procfs races, FUSE-backed slow-fs races to widen the window, ptrace and signal races, kernel double-fetch / userspace pointer races, container/runc/symlink escape primitives, kubernetes admission/authz TOCTOU, web auth-vs-authz TOCTOU, JWT-claim TOCTOU at gateway vs service, payment/idempotency races, and modern race-amplification techniques (single-packet attack, slow loris, FUSE pause, cgroup freeze, scheduler shaping). Use when you've identified a 'check then act' pattern in code, when fuzzing for race conditions, or when exploiting concurrency bugs in privileged binaries / kernel / orchestrators.

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。

SQL injection testing skill for offensive security assessments and bug bounty hunting. Covers error-based, UNION-based, boolean/time-based blind, out-of-band, second-order, NoSQL, GraphQL, WebSocket, and JSON-operator SQLi. Includes WAF bypass techniques, database-specific exploitation (MySQL, MSSQL, PostgreSQL, Oracle), cloud-native attack paths, ORM CVE tracking, and SQLmap automation. Use when performing web application SQL injection testing, database enumeration, privilege escalation via SQLi, or assessing injection vectors in APIs and modern stacks.

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.

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Hunt Session Management vulnerabilities — session fixation (no regeneration on login), insufficient invalidation on logout / password-change / email-change, predictable or low-entropy session IDs, JWT-as-session with no exp/revocation, refresh-token rotation/reuse-detection gaps, OAuth/SSO session linkage, device-bound-session (DBSC) downgrade, and cookie attribute issues (Secure/HttpOnly/SameSite/__Host-). Validate with TWO real sessions (attacker A + victim B), body-diff every 200, and OOB confirmation for theft chains. Medium to Critical (fixation→admin hijack, no-invalidation→persistent ATO).

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。

OAuth 2.0 attack checklist: authorization code interception, redirect_uri bypass, CSRF on OAuth flow, state parameter abuse, open redirector chaining, token leakage via Referer, PKCE bypass, and scope escalation. Use when testing OAuth implementations in web apps or 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。

Hunt Node.js specific vulnerabilities — Prototype Pollution → RCE chains (lodash/merge/assign), Express trust proxy misconfiguration, child_process/eval injection, template engine SSTI (EJS/Pug/Handlebars), path traversal in file servers, require() injection, environment variable exfil via /proc/self/environ. Use when target runs Node.js/Express/Fastify/NestJS/Koa.

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。

Advanced API exploitation methodology focused on business logic abuse and sophisticated attack patterns that bypass traditional security controls. Covers business logic bypass through API call chaining and workflow manipulation. Addresses GraphQL-specific attacks including batching for credential brute-force, query depth exploitation, and introspection abuse. Includes pagination exploitation for data exfiltration, webhook hijacking for SSRF and data interception, and resource exhaustion through algorithmic complexity attacks. Covers race conditions in API transactions using parallel request techniques. Provides comprehensive JWT manipulation including algorithm confusion, kid injection, jku/x5u abuse, and claim tampering. Details API key leakage detection across source repositories, client-side code, and error messages. Covers undocumented endpoint discovery through predictable naming, debug routes, and source map analysis. Tooling includes Arjun, ParamSpider, jwt_tool, and GraphQL Voyager. Designed for a...

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。