「deep」の検索結果

Firmware development for the Waveshare ESP32-S3-RLCD-4.2 (SKU 33298 / 33507, and the -EN variant) — an ESP32-S3-WROOM-1-N16R8 AIoT board built around a 4.2" fully reflective 300x400 monochrome LCD on a Sitronix ST7305/ST7306, with no backlight, plus an ES8311 speaker codec, an ES7210 dual-microphone ADC, an SHTC3 temperature/humidity sensor, a PCF85063A RTC, a microSD/TF slot on SDMMC, an 18650 holder with battery sensing, BOOT/KEY/PWR buttons and a 2x8 2.54 mm expansion header. Use when working on this board: project setup, platformio.ini, the qio_opi PSRAM memory type, the pin map and which header pins are traps, U8g2 and the ST7305 constructor, a blank or shifted panel, ST7305 low-power mode HPM/LPM, the shared I2C bus and its four slaves, I2S audio directions and the speaker amplifier enable, mounting the TF card, battery voltage, USB CDC console with no UART bridge, flashing over Type-C, deep sleep, or debugging why something on the board does not work.

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Firmware development for the 36-pin ESP32-WROOM-32 development board — the original DOIT ESP32 DevKit V1 with 18 pins per side, i.e. the 30-pin layout plus the six in-package flash pins (SD0 SD1 SD2 SD3 CMD CLK = GPIO6-11) broken out at the USB end, CH340G or CP2102 bridge, AMS1117 LDO, user LED on GPIO2, GPIO0 not on the header. Use when working on a 36-pin ESP32 devkit (ESP32-D0WDQ6 / ESP32-D0WD-V3, ESP-WROOM-32 module), or when identifying which ESP32 board size you have: project setup, platformio.ini and sdkconfig for ESP-IDF or Arduino, pin mapping and which GPIOs are safe, the flash-pin trap, strapping pins, ADC1 vs ADC2 with Wi-Fi, SPI speed and IO_MUX pins, deep sleep and RTC wake pins, flashing, BOOT/EN recovery, brownouts, or debugging why something on the board does not work.

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Firmware development for the 38-pin ESP32-WROOM-32 development board — Espressif ESP32-DevKitC V4, Ai-Thinker NodeMCU-32S, DOIT ESP32 DevKit V1 38-pin and their CH340/CP2102 clones, 19 pins per side, with GPIO0 and the six in-package flash pins (GPIO6-11, labelled D0 D1 D2 D3 CMD CLK or SD0-SD3) broken out. Use when working on a 38-pin ESP32 devkit (ESP32-D0WDQ6 / ESP32-D0WD-V3, ESP-WROOM-32 or ESP32-WROVER module): project setup, platformio.ini and sdkconfig for ESP-IDF or Arduino, pin mapping and which GPIOs are safe, the flash-pin and IO0 traps, WROVER GPIO16/17 PSRAM conflict, strapping pins, ADC1 vs ADC2 with Wi-Fi, SPI speed and IO_MUX pins, deep sleep and RTC wake pins, flashing, BOOT/EN recovery, brownouts, 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 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.

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Activate a persistent visible-reasoning mode that requires detailed internalcot CLI notes before substantive tools and answers, throughout the conversation. Use only when the user explicitly invokes $internalcot, says "internalcot on", or asks to enable internalcot mode. Keep it active until the user explicitly turns it off. Do not trigger merely because a user asks to think carefully, reason deeply, or show work.

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Web application testing principles. E2E, Playwright, deep audit strategies.

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(builtin) Initialize hierarchical AGENTS.md knowledge base

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Remove AI-generated code smells (slop) from branch changes or an explicit file list. Locks behavior with regression tests FIRST, then runs categorized cleanup via parallel `deep` agents in batches of 5, then verifies with quality gates. Covers 10 slop categories including performance equivalences, excessive complexity (object annotations, if/elif variant chains), and oversized modules (250+ pure LOC with mandatory modular refactoring). MUST USE when the user asks to "remove slop", "clean AI code", "deslop", "clean up AI-generated code", "remove AI slop", or wants to clean up AI-generated patterns from recent changes. Triggers - "remove ai slops", "clean ai code", "deslop", "cleanup AI generated", "remove AI slop", "clean up AI-generated code", "strip slop", "ai-slop cleanup".

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Create an implementation plan for a feature — a dependency-ordered, actionable task list for the project. Has four modes: fast (a quick single-pass plan), full (a richer plan that can also create a git branch), ultra (a multi-file bundle — a manifest plus one deeply specified file per phase — for execution by a smaller model), and add (extend an existing plan with more tasks). Pick the mode from the user's wording: "full plan" runs full; "quick plan" or "fast plan" runs fast; "ultra plan", "ultraplan" or "ultra-plan" runs ultra; a plain "create a plan" with no qualifier defaults to fast; "add to the plan" or "extend the plan" runs add. Use whenever the user wants to plan a feature or task, e.g. "create a plan", "create a full plan", "create a quick plan", "run an ultra plan for the inventory system", "plan this feature", "add this to the plan", "extend the plan", "add a phase to the plan". Ultra runs only when the user names it — never because the feature looks large.

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White-box security audit. Blue-teamer and lead red-teamer run in parallel isolation for an independent first pass — neither sees the other's output during reconnaissance. A synthesis step categorizes findings into four prescriptive buckets (anchoring-suppressed, convergent, blue-flagged-unverified, divergent), producing a unified target list. Focused red-teamers then deep-dive each target. Iterates when exploit chains are discovered. Heavy and thorough by design. Advisory only — produces findings and proposes tickets; does not implement remediation.

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This skill should be used when the user gives a SaaS product's landing page URL, homepage, or name and asks for a full teardown or deep-dive of how the product works across web, desktop, and mobile — including per-platform feature inventories and user journeys, tech stack, pricing/plan mapping, how the platforms integrate and sync (and the business logic behind those choices), hardware integrations, business-scale/revenue signals, and concrete release artifacts per platform — with a structured Markdown report as the deliverable. Trigger on phrases like "research this SaaS", "do a platform teardown of X", "map every feature of this product across web/mobile/desktop", "reverse-engineer how this app works everywhere", or a bare URL plus a request to understand the whole product.

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Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.

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Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

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MUST USE when user wants to research/search/look up/find anything on the internet — e.g. "research this topic", "do a deep dive on X", "search the web for X", "see what people say about X", "look this up". Also MUST USE when user mentions any platform or shares any URL/link: Twitter/X, Reddit, Facebook, Instagram, YouTube, GitHub, Bilibili, XiaoHongShu, Xiaoyuzhou Podcast, LinkedIn/jobs/recruiting, V2EX, Xueqiu (stocks), RSS. 15 platforms, multi-backend routing (OpenCLI / per-platform CLIs / APIs). Zero config for 6 channels. Run `agent-reach doctor --json` to see which backend serves each platform right now. NOT for: writing reports/analysis/translation (this skill only FETCHES internet content); posting/commenting/liking (write operations); platforms that already have a dedicated skill installed (prefer that skill).

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Use when designing or improving a module interface, choosing a seam, reducing shallow modules, making code easier to test, or reviewing architecture for locality, leverage, adapters, and deep-module design.

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Generate investor-ready board decks and reporting packages. Covers monthly board updates, quarterly deep dives, and annual reviews with the metrics that actually matter.

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Deep behavioral security audit for AI agent skills and MCP tools. Performs deterministic static analysis (AST + Semgrep + 15 specialized scanners), cryptographic lockfile generation, and optional LLM-powered intent analysis. Use when installing, reviewing, or approving any skill, tool, plugin, or MCP server — especially before first use. Replaces basic safety summaries with full CWE-mapped, OWASP-tagged, line-referenced security reports.

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Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.

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Use cheap, TEE-verified AI models from the 0G Compute Network as OpenClaw providers. Discover available models and compare pricing vs OpenRouter, verify provider integrity via hardware attestation (Intel TDX), manage your 0G wallet and sub-accounts, and configure models in OpenClaw with one workflow. Supports DeepSeek, GLM-5, Qwen, and other models available on the 0G marketplace.

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Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

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Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.

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Evidence-first multi-step research with bounded parallel collection, contradiction checks, and cited synthesis.

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。

Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

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。

Deep knowledge about deploying applications to Cloudflare (Pages, Workers, Wrangler).

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。