「source」の検索結果

Use MCP servers on-demand via the mcp CLI tool - discover tools, resources, and prompts without polluting context with pre-loaded MCP integrations

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。

Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources.

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。

Apply professional-grade security standards to any web application task. Use this skill when writing code that handles authentication, authorization, user input, APIs, file uploads, sessions, secrets, payments, or any data from external sources. Also use when the user asks to review, audit, or assess security of code or a web app. Covers OWASP Top 10 (2025), secure coding, threat modeling, and defensive architecture. Activate even when security is not explicitly mentioned — treat every web feature as a security surface.

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。

Sub-skill: Implement Auth.md for agent registration discovery. Serve /auth.md with OAuth metadata at /.well-known/oauth-protected-resource and authorization server endpoints.

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 only after explicit Codex `$superloopy:superloopy-research` or Claude Code `/superloopy:superloopy-research` invocation, a research task started with a leading `loopy` or `루피` (such as `loopy research`), or an active Superloopy loop explicitly routing a research deliverable here. Evidence-backed Superloopy research orchestration with automatic advisory usage targets, parallel read-only lanes, per-retrieval verdicts, graded and dated sources, empirical verification, a priced claim ledger, cited synthesis, and optional reports. Do not activate from research, investigate, look-up, summarize, deep-dive, report, or similar vocabulary alone, in any language; ordinary questions, debugging, and implementation context-gathering stay with their primary workflows.

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。

Lighthouse audits and Core Web Vitals — LCP, CLS, INP. Use when optimising page load, layout shift, input delay, images, fonts or render-blocking resources.

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 deep research on a topic. Web searches, data collection, source checking, and a structured report. Use when the user needs more than a quick answer.

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。

Download GitHub repos as clean source code (no .git history) to WORKDIR. Use when cloning repos for analysis or modification. Not for monorepos.

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。

Choose bounded implementation scope and existing owning modules before Guardrails runtime, configuration, client, resource, streaming, Agents, or SDK compatibility changes and feedback fixes.

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。

Audit a website against the Forter Agentic Readiness Guide. Loads the 25 weighted rubrics in `audit/`, probes the target site (and optional source code), scores each guideline Pass/Partial/Fail/N/A with sub-check granularity, and produces a prioritized fix report. Use when a user asks "score my site against the agentic readiness guide", "audit https://… for agent readiness", or "what do I need to fix to be agent-ready".

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。

Prepare a meeting-ready alternative-investments brief for a client, household, IRA, trust, or named account before a review — pulls the alts book from iCapital or Addepar (positions, NAV vs. commitment, called and unfunded amounts, contributions/distributions/subscriptions/redemptions, lockups, and the platform's own IRR/TVPI/DPI/RVPI) and sits it beside the liquid book and IPS target from the connected portfolio system (Orion Connect, Addepar, or Envestnet), then flags cash needed for capital calls, alts % vs. target, concentration, unfunded concentration, and stale as-of data. Cites the source platform on every figure and keeps disclosures on the page. Does not recommend funds, forecast NAVs/IRRs, or simulate allocation changes. Triggers on "alts brief", "/alts-brief", "prep the [client]'s alts review", "what's going on with [client]'s PE/private investments", "how's [client] doing on their alts", or any request to review a household's alternative investment positions before a meeting.

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。

Reference guide for connecting popular APIs to Home Assistant via Node-RED, YAML, or custom integrations. Covers authentication, endpoints, and complete working examples for: energy APIs (Tibber, Nordpool), weather (SMHI, OpenWeatherMap, yr.no), transport (SL, Trafikverket, Resrobot), smart home clouds (Shelly, Tuya, Philips Hue, IKEA), and global APIs (OpenAI, Spotify, Google Calendar, Telegram, GitHub). Use this skill whenever the user mentions a specific external service, API, or data source they want to connect to Home Assistant - even if they don't say "API".

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。

Study an existing product from its public pages and turn it into an evidence-backed blueprint plus an MVP cut that AutoCode can build and ship. Use this whenever the user wants something "like X", "similar to X", "my own version of X", "a clone of X", names a competitor they admire, or pastes a product URL and asks to build it — even when they never say "research" or "analyze". Public sources only (official site, docs, pricing, changelog, public repos, app-store listings, public reviews); no logins, captures, decompiling, or copying of code, copy, images or brand.

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。

Godot 4 Buff系统完整实现，支持状态效果管理、持续时间、循环效果、层叠机制等。使用Resource+Component架构。

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。

Orchestrate a fail-gated, source-locked Blender reconstruction of mascots, logos, brand avatars, and stylized flat characters from wireframes, texture packs, and orthographic views. Use when the user requires a 1:1 brand/model match rather than a plausible stylized interpretation.

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。

Validate and repair source-locked Blender models using named landmarks such as leaf tips, shell corners, eyes, smile, rim thickness, aura center/radius, and view-specific depth markers. Use when bbox/IoU is insufficient and the model must align to templates at designed feature points.

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。

Determine whether front/side/back/top reference templates can be satisfied by one rigid 3D Blender model, choose a canonical source policy, and emit constraints before rebuilding. Use when multiview bboxes, silhouettes, part counts, or depth ratios contradict each other.

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。

Calibrate Blender materials, lighting, camera crop, emission/glow, color management, and aura/HUD styling against supplied original/reference images using measurable color, brightness, saturation, bbox, and mask statistics. Use when a product must match the lighting/look of a source image or when audits say the product is too bright, desaturated, wrong hue, wrong glow, or wrong silhouette extent.

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。

Create tasteful circular/orbital HUD and aura animations around a Blender subject using source-derived arcs, dots, dashes, opacity, parallax, and restrained motion. Use when decorative circles, halos, orbit markers, scanner rings, or HUD effects must correlate with a logo/mascot rather than looking like random oversized rings.

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。

Reconstruct Blender models from supplied reference sheets, branding templates, texture atlases, orthographic front/side/back/top views, or mascot/logo art where visual fidelity to the source is more important than a plausible generated object. Use when the user says the model must match a template, wireframe, texture pack, character sheet, mascot sheet, or brand asset exactly; also use after feedback like "does not look like the reference", "fit the texture 1:1", "wrong number of visible parts", or "compare against the template". Requires Blender MCP plus local Python with Pillow/OpenCV/numpy; pairs with blender-uv-texturing, wireframe-to-3d, blender-modeling, blender-materials, and blender-export.

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。

Turn multiview fit reports into a sequential or parallel repair queue for aligning Blender products to source-of-truth templates. Use after validation shows disalignment, when the agent must iteratively fix wireframe, texture, lighting, or projection mismatches, and when skill gaps should trigger self-refinement before another rebuild.

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 texture-atlas regions and map each source part to its own UV rectangle or projected UV surface. Use when a texture pack must fit a Blender model 1:1, when textures look off/stretched, when alpha decals or lightmaps are provided, or before exporting a textured GLB for a brand mascot/logo.

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 and validate texture-state transitions for Blender animations without ugly whole-image crossfades, texture popping, misregistered morphs, or target-engine-incompatible Python-only swaps. Use when animating between multiple source textures, mascot/logo states, style states, lightmaps, decals, or UI skins.

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。

Data-model rules that make a schema importable from day one, so the migration-import-engineer is never blocked on missing columns. Every SMB Product-Builder product must let a customer bring their data from an incumbent (ServiceTitan/Toast/Mindbody/Shopify) — that requires provenance (source_ref) and rollback (import_batch_id) on importable entities, and modelling real-world actors as entities rather than inline fields. Applied by architect when writing the data model in ARCH-{slug}.md, and checked by migration-import-engineer. One cheap rule set prevents the migration↔architecture seam gap from recurring across all 40 products.

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。