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概要と使いどころ
Fully autonomous bug hunting pipeline — discover bugs in a scoped area using parallel subagents, independently triage each finding, fix confirmed issues with subagents, then audit all fixes against repo constraints and target platforms. Runs end-to-end without user interaction.
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。
Firmware development for the 40-pin ESP32-S3-WROOM-1 camera board — the Freenove ESP32-S3-WROOM (FNK0085) and the AliExpress "ESP32-S3 CAM" / "ESP32-S3-WROOM N16R8 CAM" clones that copy its header: 20 pins a side, a 24-pin DVP camera FPC connector for an OV2640/OV3660, an on-board microSD slot on the SDMMC bus, a WS2812, two USB-C sockets (CH343 bridge and native USB), ESP32-S3-WROOM-1-N8R8 or N16R8 with 8 MB octal PSRAM. Use when working on this board or any ESP32-S3 DVP camera board: project setup, platformio.ini, the qio_opi PSRAM memory type, camera and SD_MMC pin maps, esp_camera_init failures, PSRAM not found, frame buffer allocation, SD card mount failures, which GPIOs are left once the camera is wired, strapping pins, ADC1/ADC2, flashing over the two USB ports, BOOT/RESET recovery, brownouts, or debugging why the camera, the card or the board does not 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。
Firmware development for the 30-pin NodeMCU ESP8266 devkit — NodeMCU DevKit V1.0 / Amica (CP2102) and LoLin NodeMCU V3 (CH340G), built on the Ai-Thinker ESP-12E or ESP-12F module (ESP8266EX, 4 MB flash), with PlatformIO and the Arduino ESP8266 core (`board = nodemcuv2`). Use when working on a NodeMCU, ESP-12E, ESP-12F or ESP8266 devkit: project setup, platformio.ini, the D0-D10 vs GPIO numbering, which pins are safe and which break the boot, GPIO15/GPIO0/GPIO2 strapping, the A0 divider and 1 V ADC, software I2C and software PWM, analogWrite range, LittleFS and flash layouts, OTA, deep sleep and the missing GPIO16-to-RST link, Wi-Fi, heap exhaustion, watchdog and Soft WDT resets, exception decoding, flashing over CP2102/CH340, or debugging why the board does not boot, prints garbage, or reboots by itself.
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。
Firmware development for the LGT8F328P-LQFP32 MiniEVB — the Nano-style 30-pin "purple nano" board whose silkscreen reads LGTBF32BP (also sold as BTE21-15A, WAVGAT, LGT8F328P Nano V3.0), built on the Logic Green / Prodesign LGT8F328P, an AVR-instruction-compatible LGT8XM part with a 32 MHz internal RC and no crystal, a 12-bit ADC, an 8-bit DAC on D4, four timers, 80 mA drive pins, SWD instead of ICSP, and EEPROM emulated in program flash. Use when working on this board, any LGT8F328P/LGT8F328D/LGT8FX8P board, or a sketch being ported from an Arduino Nano or ATmega328P: PlatformIO setup with the lgt8fx core, platformio.ini, f_cpu/f_osc/clock_source and why timings come out 2x wrong, pin mapping and the LQFP32 bonded pads, analogRead resolution and reference errors, analogWrite hitting the DAC, EEPROM vanishing after upload, 80 mA outputs, upload failures over CH340/CH9340/HT42B534, SWD recovery, or debugging why something on the board does not 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。
Firmware development for the WeAct Studio "Black Pill" (STM32F411CEU6) and its clones such as the DFRobot DFR0864 — the 25 MHz HSE and 32.768 kHz LSE crystals, PC13 LED, K1 button, USB-C wired straight to USB_OTG_FS, ROM DFU flashing, the SPI-flash footprint, and the STM32Cube HAL + PlatformIO setup around them. Use when working on this board or any STM32F411CE/F401CC Black Pill: project setup, platformio.ini, clock and PLL configuration for 96 MHz plus 48 MHz USB, pin mapping on the 48-pin package, USB CDC console, RTC on the LSE crystal, the internal temperature sensor, ADC accuracy, DFU flashing and recovery, or debugging why something on the board does not 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。
Firmware development for the Beetle — the CJMCU / DFRobot "Mini Arduino Leonardo" USB board (ATmega32U4 @ 16 MHz, 5 V, 21×28 mm, castellated pads) — its native USB (CDC serial + HID keyboard/mouse/joystick), Caterina bootloader and 1200 bps touch upload, ten exposed I/O pads, four PWM pins across Timer0/Timer1, five ADC channels, I2C on D2/D3, SPI on the back-side ICSP pads, 28 KB usable flash and 2.5 KB SRAM, and the PlatformIO + Arduino AVR core (`board = leonardo`) setup around them. Use when working on this board or any ATmega32U4 Leonardo/Micro/Pro-Micro-class clone: project setup, platformio.ini, why the board shows up as two different serial ports, uploads that fail or hang, recovering a bricked or HID-spamming board by shorting the ICSP reset pads, pin mapping and which pads are missing, PWM/timer conflicts, Servo and tone, USB HID keyboard projects, the SRAM budget, powering from the + pad, or debugging why something on the board does not 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。
Understand what SKMTC is, how its engine works, and the architectural invariants — for agents building or extending infrastructure *around* SKMTC rather than authoring generators or running the CLI. Covers the three-phase pipeline, the host/Worker boundary, cross-generator coordination, the manifest, the attribution / gen-maps (provenance) subsystem, the package graph, the dependency substrate, and the design decisions that make SKMTC behave unlike a typical codegen tool. Use this skill when the user asks "what is SKMTC", "how does the SKMTC engine work", "explain the SKMTC architecture", or is building platform infrastructure around SKMTC — a hosted generate API, a schema or generator registry, tracing or provenance tooling, a web app or SaaS that wraps the engine, or platform-level CI integration. This skill is the system mental model. It does NOT cover authoring generators (→ skmtc-generator), running CLI commands (→ skmtc-cli), or diagnosing broken runs (→ skmtc-debug).
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。
PC and console game development principles. Engine selection, platform features, optimization 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。
Mobile-first design thinking and decision-making for iOS and Android apps. Touch interaction, performance patterns, platform conventions. Teaches principles, not fixed values. Use when building React Native, Flutter, or native mobile apps.
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。
Game development orchestrator. Routes to platform-specific skills based on project needs.
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。
Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
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。
Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA standards. Use this skill to generate semantic ARIA for Web and accessibility traits for Web and Native platforms (iOS/Android).
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。
Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.
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。
Complete community building, engagement, and monetization system. From zero to thriving community — launch strategy, member engagement loops, content programming, moderation frameworks, growth tactics, monetization models, and health metrics. Works for Discord, Slack, Telegram, Circle, forums, or any platform.
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。
Paid acquisition strategy, budget allocation, and avoiding common advertising mistakes across platforms
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。
AI-powered affiliate marketing. Product selection, content optimization, link management, conversion tracking. Multi-platform support.
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 marketing ad images using Nano Banana Pro (Gemini 3 Pro Image). Accepts campaign-planner creative briefs, reads brand bible for visual style, constructs marketing-optimized prompts, and produces platform-ready images at correct aspect ratios. Supports 1:1, 9:16, 16:9, 4:5 formats. Includes self-review loop to catch hallucinated logos, wrong text, and quality issues. Draft-first workflow (1K fast iteration, 4K final). Outputs to /tmp/marketing/assets/images/.
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。
The academic research platform for AI agents. Submit papers, review research, build consensus, and push toward AGI — together.
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。
Connect your AI agent to 37Soul social platform for authentic interactions, posting tweets, replying to messages, and developing genuine social personality
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。
Research current job markets and hiring requirements, analyze recent job descriptions (JDs), compare them with a user's resume, projects, skills, and constraints, and turn evidence gaps into prioritized career actions. Use when students, job seekers, or career changers ask about current role or industry outlook (including “现在行情怎么样”), whether a role or JD fits them (including “这个JD适合我吗”), cross-platform JD sampling, resume evidence gaps or truthful tailoring, what career skill to learn next, target-role course or certificate decisions, application readiness, or a capacity-bounded 30/60/90-day job-search plan. Do not use for unrelated personal or education advice, simple factual lookups, automatic job applications, or unsupported resume rewriting.
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。
Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols. Implements suicide ideation detection, automated escalation, and crisis resource integration. Use for mental health apps, recovery platforms, support communities. Activate on "crisis detection", "suicide prevention", "mental health NLP", "intervention protocol". NOT for general sentiment analysis, medical diagnosis, or replacing professional help.
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。
Verify one approved SwiftUI feature slice against its behavioral parity contract with deterministic and conditional Apple-platform checks.
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。
Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.
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。
Run 150+ AI applications in the cloud via the inference.sh platform. Triggers on "generate image with FLUX", "create video", "use Veo/Seedance", "run inference.sh", "infsh CLI", "single key for image+video+LLM". One API key covers image generation (FLUX, Reve, Seedream, Grok, Gemini), video (Veo, Wan, Seedance, OmniHuman, HunyuanVideo), LLMs (Claude, Gemini, Kimi, GLM-4), search (Tavily, Exa), 3D (Rodin), social (Twitter/X), and audio (TTS, voice cloning).
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。