Use only after explicit Codex `$superloopy:superloopy-frontend` or Claude Code `/superloopy:superloopy-frontend` invocation for supported screen-based application UI across browser-hosted Web, interactive deployed content-led Web, desktop, mobile/tablet, embedded/hybrid, Qt, custom-rendered, or mixed targets, such a task started with a leading `loopy` or `루피`, or an active Superloopy loop explicitly routing it here. Do not activate from UI, frontend, desktop, mobile, or framework vocabulary alone, or for TV, wearable, XR, automotive, game UI, TUI, static media/document artifacts, or non-UI 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。
hashgraph-online/awesome-codex-plugins☆ 1,3422026年10月17日 更新
Perform codebase analysis and architecture mapping as the first phase of a security assessment. Explores the tech stack, frameworks, entry points, data flows, and trust boundaries. Outputs sast/architecture.md. Run this before any vulnerability detection skill. Use when asked to analyze a codebase for security or when sast/architecture.md does not yet exist.
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。
utkusen/sast-skills☆ 1,3412026年4月8日 更新
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative 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。
OpenRaiser/NanoResearch☆ 1,3412026年10月9日 更新
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing 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。
OpenRaiser/NanoResearch☆ 1,3412026年10月9日 更新
Writes product-led content pages optimized for both search engines and AI engine citations. Produces markdown files with frontmatter, following page-type frameworks (comparison, guide, use case, learn, trust) with verified sources and no fabricated data.
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。
onvoyage-ai/gtm-engineer-skills☆ 1,3222026年10月12日 更新
Scaffold a TanStack app with tanstack create using --framework, --build-tool, --template, --toolchain, --deployment, --add-ons, --blank, and --router-only. Covers flag compatibility, non-interactive defaults, and intent-preserving command construction.
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。
TanStack/cli☆ 1,3012026年10月17日 更新
Build frameworks from scattered insights across all braindumps and notes
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。
huytieu/COG-second-brain☆ 1,2942026年10月2日 更新
Check for and apply upstream COG framework updates (skills, docs, scripts) without touching personal content
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。
huytieu/COG-second-brain☆ 1,2942026年10月2日 更新
INVOKE FIRST for any LangChain / LangGraph / Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.
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。
langchain-ai/langchain-skills☆ 1,2942026年10月9日 更新
Use when a model — a full theory paper's argument or the structural/conceptual scaffolding behind an empirical AER: Insights short-format manuscript — needs to be made minimal, so it carries the single insight in a few pages. Keeps only what the insight requires; it does not develop a general framework.
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。
brycewang-stanford/Awesome-Journal-Skills☆ 1,2692026年9月27日 更新
Use when an AEJ: Economic Policy manuscript needs a framework that maps reduced-form estimates into a welfare, cost-benefit, or distributional policy object — sufficient statistics, MVPF, optimal-policy, or a small applied model. Builds the estimate-to-welfare bridge and states its assumptions; it does not run the empirical estimation or write the prose.
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。
brycewang-stanford/Awesome-Journal-Skills☆ 1,2692026年9月27日 更新
Use when building the conceptual exhibits of an Academy of Management Review (AMR) manuscript — process models, 2x2 typologies, multi-level frameworks, and propositions tables that carry theoretical work. These are CONCEPTUAL figures, NOT data plots; AMR has no charts of results because it has no data.
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。
brycewang-stanford/Awesome-Journal-Skills☆ 1,2692026年9月27日 更新
Use when building exhibits for an Academy of Management Annals (Annals) review — the signature framework figure, who-studied-what synthesis tables, and search/coverage exhibits that summarize across a literature. Designs review exhibits; it does not produce original-estimate regression tables (an Annals review reports no new data of its own).
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。
brycewang-stanford/Awesome-Journal-Skills☆ 1,2692026年9月27日 更新
Use when judging whether a management/organization literature is an Academy of Management Annals (Annals)-scale review topic — mature, important, fragmented, and integrable into a new framework — rather than a narrow review. Decides fit and frames the review's animating problem; it does not gather the literature (amann-literature-synthesis) or write the proposal (amann-proposal-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。
brycewang-stanford/Awesome-Journal-Skills☆ 1,2692026年9月27日 更新
Use when systematically gathering, coding, and synthesizing a management/organization literature for an Academy of Management Annals (Annals) review — the search-and-coverage methodology and the choice of narrative vs. systematic vs. bibliometric integration. Builds the corpus and synthesis notes; it does not impose the organizing spine (amann-organizing-framework) or write prose.
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。
brycewang-stanford/Awesome-Journal-Skills☆ 1,2692026年9月27日 更新
Identify programmatic SEO page patterns worth building for your product — vs/ pages, integrations/, for-{industry}/, alternatives-to/, use-cases/ — and design the template structure, data model, and priority order. Outputs a complete pSEO blueprint with URL patterns, title templates, content frameworks, and data sources per variable.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月18日 更新
Write high-converting cold emails using structured frameworks, personalization tiers, and patterns from real campaigns. Pure reasoning skill — no scripts. Auto-loads when any task requires outreach copy.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月18日 更新
Verify that a web app change actually works by driving the running app from the inside (DOM, network, routing, console, framework state) instead of screenshots or guessing. Use after any user-facing change, when a fix is claimed but unproven, when a test passes but the UI is broken, or when you need a real verdict rather than "looks right". Also use to install and wire up Reticle in a project that does not have it yet.
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。
reticlehq/reticle☆ 1,2532026年10月18日 更新
Install, instrument and verify this running web app from the inside (DOM, network, routing, console and framework state) instead of screenshots or guessing. Drives one real flow end to end and returns a verdict with the file:line to fix. Use when the user asks to set up or install Reticle, when a user-facing change needs proving before you call it done, when a test passes but the UI is broken, or when the user types /reticle.
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。
reticlehq/reticle☆ 1,2532026年10月18日 更新
Entity Framework Core best practices including NoTracking by default, query splitting for navigation collections, migration management, dedicated migration services, and common pitfalls to avoid.
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。
Aaronontheweb/dotnet-skills☆ 1,2312026年10月12日 更新
Understand implementation details of .NET code by decompiling assemblies. Use when you want to see how a .NET API works internally, inspect NuGet package source, view framework implementation, or understand compiled .NET binaries.
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。
Aaronontheweb/dotnet-skills☆ 1,2312026年10月12日 更新
Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.
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。
GPTomics/bioSkills☆ 1,2242026年8月15日 更新
Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.
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。
GPTomics/bioSkills☆ 1,2242026年8月15日 更新
Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.
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。
GPTomics/bioSkills☆ 1,2242026年8月15日 更新