「general mode」の検索結果

Remote-sensing literature search, citation verification, sensor/platform/dataset query strategy, citation file management (.ris/.bib/.enw conversion), and reference management (BibTeX, related articles, DOI/arXiv lookup) across CrossRef, arXiv, Scopus, ScienceDirect, Semantic Scholar, and domain web sources. Use when the user needs coordinated multi-step literature workflows for remote sensing, Earth observation, geospatial AI, GIScience, photogrammetry, SAR, hyperspectral imaging, LiDAR, satellite missions, benchmark datasets, land-cover mapping, change detection, object detection, segmentation, retrieval, or multimodal remote-sensing foundation models. Also trigger on general literature-search needs during academic writing even without the word "Nature", such as searching for papers/literature, doing a literature review, verifying a citation, converting citation files, and Chinese phrasings like 文献检索、查文献、找文献、 文献综述检索、查论文、引文核对、参考文献管理、文献去重.

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。

Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.

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。

Structured 3-phase peer review of a diff or PR — intent, issues by severity, then pointed merge-readiness questions and a verdict. Use for general code review. Not for AI workflow self-fix-and-edit (ai-systems-review) or no-edit devil's-advocate threat models (adversarial-challenge). Phase 3 asks questions only; it is not the adversarial-challenge skill.

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。

Guide for evaluation — inline evaluators, Inspect AI integration, and custom evaluators for measuring training progress. Use when the user asks about evaluation, metrics, benchmarks, or how to measure model quality during training.

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。

Guide for hyperparameter selection — learning rate formulas, LoRA rank, batch size, group size, schedules, and model-specific tuning. Use when the user asks about learning rate, batch size, hyperparameter tuning, or how to configure training parameters.

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。

Guide for using renderers — the bridge between chat-style messages and token sequences. Covers renderer setup, TrainOnWhat, vision inputs, model family matching, and custom renderers. Use when the user asks about renderers, tokenization, message formatting, or vision inputs.

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。

Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).

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。

Entry-point router skill for the 1Password CLI. Use this skill when the user mentions the 1Password CLI (`op`) or 1Password generically, references the secret-reference URI scheme `op://`, asks about `OP_ACCOUNT` or `OP_SERVICE_ACCOUNT_TOKEN`, asks about biometric unlock for a CLI, or is choosing how to load secrets into an app and 1Password is a candidate. Provides general orientation, the auth-mode picker, the full command map, and routing to deeper sub-skills (`op-secrets-injection`, `op-item-management`, `op-provisioning`, `op-ssh-keys`, `op-shell-plugins`, `op-service-accounts`). Defer to those sub-skills for specific commands.

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。

Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has been paged at 3am for a factory class that wrapped one function: question whether the task needs to exist at all, reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full, ultra. The default is full. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests such as general knowledge, prose, translation, summaries, recipes.

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。

Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).

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 or edit images using AI models (FLUX, Gemini). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that isn't a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics skill instead.

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 flat, minimal light/dark-aware SVG diagrams as standalone HTML files, using a unified educational visual language with 9 semantic color ramps, sentence-case typography, and automatic dark mode. Best suited for educational and non-software visuals — physics setups, chemistry mechanisms, math curves, physical objects (aircraft, turbines, smartphones, mechanical watches), anatomy, floor plans, cross-sections, narrative journeys (lifecycle of X, process of Y), hub-spoke system integrations (smart city, IoT), and exploded layer views. If a more specialized skill exists for the subject (dedicated software/cloud architecture, hand-drawn sketches, animated explainers, etc.), prefer that — otherwise this skill can also serve as a general-purpose SVG diagram fallback with a clean educational look. Ships with 15 example diagrams.

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。

Hunting skill for subdomain takeover vulnerabilities. Includes modern provider fingerprints — Microsoft Azure DevOps `cloudapp.azure.com` regional-pool re-issue (1-click OAuth ATO via wildcard `reply_to`, Binary Security), Zendesk help-desk takeover → email interception → password reset chain (0xprial writeup), Vercel `cname.vercel-dns.com` deleted-project takeover, plus general Fastly CDN service re-attach and S3 dangling-bucket cookie-scope techniques. Use when hunting subdomain takeover — emphasis on ATO-chain primitives (OAuth `redirect_uri`, cookie-domain, email DNS).

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。

Rigorous benchmarking of multi-agent coordination systems: experiment design, statistical analysis, human evaluation protocols, and reproducible reporting. NOT FOR ML model evaluation (use llm-evaluation-harness), A/B testing for web products, survey design, or general data science.

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 or fixing a Content Security Policy on a real site, choosing between nonce-based and hash-based CSP, adding strict-dynamic, debugging "Refused to execute inline script" errors, deploying CSP in report-only mode first, configuring report-to / report-uri, or auditing an existing policy for unsafe-inline / unsafe-eval / wildcards. Triggers: "CSP blocks legitimate inline script", strict-dynamic, nonce-{RANDOM}, sha256-{HASH}, object-src none, base-uri none, frame-ancestors, Trusted Types, X-Content-Security-Policy obsolete, report-only vs enforced. NOT for general HTTP security headers (HSTS, COOP/COEP), Trusted Types deep dive, CORS configuration, or building a WAF.

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。

Build, debug, optimize, and deploy ComfyUI workflows for diffusion image, video, and audio generation. Activate on: ComfyUI workflow, ComfyUI custom node, ComfyUI API, ComfyUI Manager, KSampler, FLUX in ComfyUI, Wan 2.2 ComfyUI, Hunyuan video ComfyUI, LTX video ComfyUI, IPAdapter, ControlNet ComfyUI, Kijai wrapper, ComfyDeploy, RunComfy, ComfyUI security, GGUF quantization, TeaCache, Nunchaku, ACE-Step ComfyUI, F5-TTS ComfyUI, subgraphs ComfyUI. NOT for: A1111/Forge/Invoke (different UIs), training pipelines from scratch, non-diffusion ML models, or general image-API integrations (use generative-video-2026 / generative-music-audio / media-gen-deployment).

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。