2.8万 件 ・ 関連度順
概要と使いどころ
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 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 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 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 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 a reference list needs APA 7 cleanup, when DOIs must be confirmed real against Crossref or PubMed, when the user asks whether AI-suggested citations actually exist, or when a manuscript needs a citation hygiene pass before submission or defense.
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 for the search-planning stage of a literature review, when database lanes and language layers must be chosen, when DOI coverage policy is unclear, or when inclusion and exclusion criteria need drafting before any source is read; not for running the screening and PRISMA pipeline of a formal review, which is prisma-scoping-review-pipeline.
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 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 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 building slides, a poster, or a lightning talk from a manuscript, when a presentation must exist in Turkish and English, or when conference visuals need a traceability check so every number on a slide matches the analysis 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 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。
Use when sources must stay traceable across sessions and years, when each reference needs a passport recording where it was found, how it was verified, and where it is cited, or when a draft needs a cited-but-unverified sweep 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 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 reviewer comments arrive and each one needs a tracked decision, when a revise and resubmit letter must map every comment to a manuscript change, or when contradictory reviewer demands need evidence-based, editor-facing framing.
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。
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。
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。
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。
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。
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。
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.
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。
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。