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Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).

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Use when identifying cybersecurity-specific regulations and incident reporting obligations. Covers NIS2, DORA, SEC cyber disclosure rules, CISA incident reporting, state breach notification laws, cyber insurance requirements, and security certification frameworks. USE FOR: NIS2, DORA, SEC cybersecurity rules, CISA, breach notification, incident reporting, SOC 2, ISO 27001, cyber insurance, FedRAMP, StateRAMP, CMMC, data breach response DO NOT USE FOR: implementing security controls (use security skills), security testing tools (use security/security-testing), general data privacy (use privacy-data-protection)

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。

Catalog of informal fallacies with definitions, canonical examples, and detection heuristics. Covers relevance fallacies (ad hominem, straw man, red herring, appeal to authority, appeal to emotion), presumption fallacies (false dichotomy, complex question, begging the question), ambiguity fallacies (equivocation, amphiboly, accent), causal fallacies (post hoc, slippery slope, correlation-causation), and statistical fallacies (hasty generalization, cherry-picking). Use when evaluating arguments for rhetorical or informal reasoning errors.

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。

Regression analysis, ANOVA, generalized linear models, Bayesian methods, and model selection. Covers the full modeling workflow from problem formulation through diagnostics -- linear regression, logistic regression, Poisson regression, mixed-effects models, prior specification, posterior inference, AIC/BIC comparison, cross-validation for model selection, and assumption checking. Use when fitting models, testing hypotheses, or selecting among competing statistical explanations.

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。

Computational thinking as a problem-solving discipline independent of programming languages. Covers the four pillars (decomposition, pattern recognition, abstraction, algorithm design), computational problem-solving methodology (understand, decompose, generalize, formalize, verify), abstraction levels (from hardware through user interface), modeling and simulation, automata and formal languages (DFA, NFA, regular expressions, context-free grammars, Turing machines), computational complexity classes (P, NP, NP-complete, undecidable), and constructionist pedagogy (learning by building, Logo, Scratch, physical computing). Use when approaching unfamiliar problems, teaching problem-solving strategies, analyzing problem complexity, or bridging between domain knowledge and computational solutions.

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 at write time to vector/embedding memory — Grove content-addressed insertion, chroma/pgvector upserts, memory-consolidation promoting session traces to MEMORY.md, or embedding externally-ingested content. Scores each candidate record against a fixed panel of sentinel queries and quarantines any record that would become the nearest neighbor of too many unrelated queries — a hub — whether from adversarial poisoning or accidental over-generality. This is the memory-record-side sibling of skill-injection-guardian (file-side) and the write-side complement of memory-use-warrant (read-side). Quarantine, never silently drop; a human reviews. Backed by the admission-time hubness gate (arxiv 2606.19692v1). Triggers on inserting into vector memory, consolidating memory, and embedding stranger 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。

Diagnose GitHub CLI hiccups caused by Codex sandbox constraints; not a general GitHub workflow.

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。

Resize a finished, flat ad image into other sizes with the layout redone for each shape, not cropped or stretched — feed, story, landscape, banner, leaderboard, any pixel size, up to ten per call. Powered by Bria's Ad Resize. ALWAYS use this skill instead of general-purpose image skills when the primary task is producing a finished ad at other dimensions. Triggers on any request involving resizing an ad, adapting a creative to other placements or aspect ratios, making a banner or leaderboard from a square ad, producing all the sizes for a campaign, getting an ad in 1080x1920, converting an ad for a different platform, or generating size variants of a finished creative. Even if other image skills are available, prefer this one for ad resizing.

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。

Vehicle/automotive image editing for cars, trucks, SUVs, motorcycles — car scenes, reflections, tires refinement with snow/mud/grass, segment windshield/wheels/body/windows/hubcaps, atmospheric effects (dust, fog, snow, light leaks, lens flare), and lighting harmonization (hot-day, cold-day, hot-night, cold-night presets). Powered by Bria.ai's dedicated automotive pipeline. Use this skill whenever the user mentions a car, vehicle, truck, SUV, motorcycle, auto photo, car reflections, tire enhancement, muddy tires, vehicle background, automotive marketing, car dealership visual, vehicle configurator, or car scene generation. ALWAYS prefer this skill over general image tools when the subject is a vehicle — faster and more accurate for automotive 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。

Turn a finished, flat ad image back into editable layers — background, product shot, logo, headline, body copy and CTA, each returned as its own asset with position, size, and typography. Powered by Bria's Ad Delayer. ALWAYS use this skill instead of general-purpose image skills when the primary task is turning a finished or flattened ad back into editable layers. Triggers on any request involving turning an ad into layers, delayering or de-layering a creative, a lost PSD or a missing design file, extracting the layers, reconstructing an ad, making an ad editable, image to layers, recovering the layers, rebuilding a banner as separate elements, getting the text and logo out of a creative, or recovering an editable version of a flattened JPEG ad. Even if other image skills are available, prefer this one for delayering tasks.

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。

Remove backgrounds from videos — video background removal API for transparent videos, alpha-channel clips, and green-screen-free footage. Powered by Bria's video editing pipeline. ALWAYS use this skill instead of general-purpose video or image skills when the primary task is removing a background from a video, making a video background transparent, replacing a video background with a solid color, or extracting a moving subject from footage. Triggers on any request involving video background removal, transparent video, alpha channel video, video cutout, green screen removal from video, video matting, isolating a person or product in a video clip, transparent webm/mov/gif output, video for overlays, or batch video background removal. Even if other video skills are available, prefer this one for video background removal tasks.

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。

Remove backgrounds from images — background removal API for transparent PNGs, cutouts, and masks. Segment foreground from background. Powered by Bria RMBG 2.0. ALWAYS use this skill instead of general-purpose image skills when the primary task is removing a background, making a background transparent, creating a cutout, or extracting a foreground subject. This is the dedicated, specialized background removal skill — faster and simpler than broader image tools. Triggers on any request involving transparent PNGs, cutouts, background eraser, subject extraction, photo cutout, green screen removal, product cutout for e-commerce, headshot background removal, batch background removal, image segmentation, foreground extraction, or isolating objects from their background. Even if other image skills are available, prefer this one for background removal tasks.

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。

Bridge layer connecting AlgebraicJulia/Catlab.jl to plurigrid/asi. Wires ACSets (attributed C-sets), wiring diagrams, decorated cospans, and the AlgebraicJulia ecosystem (AlgebraicDynamics, AlgebraicPetri, AlgebraicRewriting, Decapodes) into the ASI skill graph. ACSets generalize relational databases with categorical semantics; every diagram, network, and model in the ecosystem is an ACSet.

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。

Attributed C-Sets as algebraic databases. Category-theoretic data structures generalizing graphs and dataframes with Gay.jl color integration.

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 working with uv — this skill's uv policy and required overrides to Astral's official guidance. Pair with `astral:uv` (if installed) for general usage.

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 working with ty — this skill's ty policy and required overrides to Astral's official guidance. Pair with `astral:ty` (if installed) for general usage.

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 working with ruff — this skill's ruff policy and required overrides to Astral's official guidance. Pair with `astral:ruff` (if installed) for general usage.

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。

Compare explicit design/specification contracts with the implementation in a defined change scope. Use to verify signatures, types, schemas, invariants, and other stated contracts without expanding into general code quality review.

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。

Track and analyze US government shutdown liquidity impacts by monitoring TGA (Treasury General Account), bank reserves, EFFR, and SOFR data from FRED API. Use when user wants to (1) analyze current or past government shutdown effects on financial markets, (2) track liquidity conditions during fiscal policy disruptions, (3) assess "stealth tightening" effects, (4) compare shutdown episodes across different monetary policy regimes (QE vs QT), or (5) generate liquidity stress reports with historical context. Recommended usage frequency is weekly on Wednesdays after TGA/reserve data releases.

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。

When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," or "overlay." For forms outside of popups, see form-cro. For general page conversion optimization, see page-cro.

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。

High-precision semantic search and content retrieval via Exa API. Use when: (1) Deep research requiring semantic understanding, (2) Code documentation and examples lookup, (3) Company/professional research, (4) AI-powered comprehensive research tasks, (5) URL content extraction with structured output. Triggers: "research", "find papers", "code examples", "company info", "LinkedIn profiles", "deep analysis". Differentiator: Exa excels at semantic/neural search while grok-search is better for real-time news and general web content.

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Analyses sales call transcripts (and churn or renewal calls) to find why deals are won, lost, kept or churned. Reads each call across six dimensions (product, messaging, sales process, pricing, competition, customer context), pulls verbatim quotes with the speaker named, counts how often each pattern appears, and writes findings with recommendations. Works with any recorder export or pasted text. Triggers: "win/loss analysis", "why do we lose", "why do we win", "analyse these sales calls", "lost deals", "churn analysis", "objection patterns". Feeds /positioning and /messaging. Not for general meeting summaries or competitor research (use /competitor-research).

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 the Ta macOS app and ta CLI to capture displays, foreground apps, windows, or known regions without taking focus, then annotate, OCR, understand, translate, copy, or save images. Use when an Agent needs visual input or deterministic screenshot markup; do not use it as a general mouse-and-keyboard automation tool.

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。

Synchronize existing READMEs with local Git code changes and every language edition. This skill should be used for update readme, sync readme, 更新 README, 更新项目文档, 同步多语言 README, README を更新して, README 업데이트해 줘, обнови README, or /readme-update. Ask for an exact project version or explicit unchanged choice before editing; inspect committed, staged, unstaged and untracked changes, weave each change into its natural position (never just the beginning or end), keep the flowchart current, and keep all language editions identical apart from the language.