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Use when authoring a new manuscript, thesis chapter, or abstract from scratch in Turkish and English in parallel — building the claim skeleton first, then drafting Turkish and re-authoring English — not for auditing parity between existing versions.

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.

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 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 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.

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 selecting a statistical test with AI consultation, when assumptions need checking against the actual data, when an analysis plan or decision log must be written before running anything, or when results need APA-style reporting with effect sizes.

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 or configuring MCP servers for research work, when a literature, reference manager, or scholarly search server needs a trust check before installation, or when an existing research tool stack needs a data flow and permission 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 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 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。

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.

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 Apache JServ Protocol (AJP)

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。

Wireless / 802.11 attack methodology for red team engagements and wireless security assessments. Covers monitor-mode setup, WPA/WPA2-PSK handshake capture and PMKID attacks, WPA3 SAE downgrade and Dragonblood, WPA-Enterprise (EAP) attacks (MSCHAPv2 cracking, EAP-TLS cert theft, evil-twin RADIUS), Karma / Known Beacons / Mana evil twin attacks, captive-portal phishing, KRACK and FragAttacks, WPS Pixie Dust, deauthentication and disassociation attacks, rogue AP construction (hostapd-mana), 802.1X bypass, MAC randomization defeat, BLE/Zigbee/IEEE 802.15.4 sidebands, and Wi-Fi 6/6E/7 considerations. Use when scoping wireless pentest, war-driving an estate, or testing corporate wireless segmentation.

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。

You are helping a penetration tester exploit JWT (JSON Web Token)

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

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 bypass file upload restrictions to achieve

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 bypass two-factor authentication. The

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。

SSO bugs frequently pay High–Critical. XML parsers are notoriously inconsistent.

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。

Server-Side Request Forgery testing checklist: SSRF discovery, blind SSRF with out-of-band, cloud metadata endpoints (AWS/GCP/Azure), SSRF filter bypass techniques (IP encoding, DNS rebinding, redirect chains), and SSRF to RCE escalation. Use for web app SSRF 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。

Hunt NoSQL Injection — MongoDB operator injection ($where, $regex, $gt, $ne), CouchDB, Redis command injection, auth bypass via NoSQLi, data dump. Use when target uses MongoDB/Mongoose, CouchDB, Redis, or shows NoSQL error messages.

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。

Offensive methodology for attacking GraphQL APIs during penetration tests and bug bounty engagements. Covers the full attack lifecycle: endpoint discovery, introspection abuse and blind schema reconstruction when introspection is disabled, authentication and authorization bypass through Relay node IDs and nested object traversal, injection via variables and directives, query batching for brute force and OTP bypass, denial of service through depth bombs and alias amplification, WebSocket subscription hijacking, information disclosure through verbose errors and field suggestion oracles, and file upload abuse via the multipart GraphQL specification. Includes tool-specific guidance for InQL, graphql-cop, CrackQL, BatchQL, Altair, GraphQL Voyager, and clairvoyance. Trigger on: GraphQL, graphql, introspection query, batching attack, query depth, GraphQL injection, GraphQL IDOR, field suggestion, GraphQL auth bypass, GraphQL DoS, GraphQL security, graphql-cop, InQL, CrackQL, BatchQL, Relay node, alias amplificat...

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。