1万 件 ・ 関連度順
概要と使いどころ
Use when deciding whether a microeconomics project fits the American Economic Journal: Microeconomics (AEJ: Micro) rather than JET, GEB, Theoretical Economics, Econometrica, or AEJ: Applied. Tests scope and broad-interest fit; it does not develop the model (see aejmic-theory-model).
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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.
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Use when the quantitative model is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — DSGE, New Keynesian, heterogeneous-agent (HANK / Aiyagari-Bewley), or structural estimation — and calibration, parameter identification, solution accuracy, or counterfactual validity need discipline. For empirical shock identification see aejmac-identification.
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Use when the headline result of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript must be shown stable across specification, sample, identification, and tuning choices. Builds the robustness program a macro referee will demand; it does not establish the primary identification or model (use aejmac-identification / aejmac-theory-model first).
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Use when the empirical identification of a macro shock or dynamic causal effect is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — SVAR, local projections, narrative, high-frequency/proxy-VAR, or micro-data macro designs. Stress-tests the identification to the AEJ: Macro broad-interest quantitative bar; for model-parameter identification see aejmac-theory-model.
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Use when an American Economic Journal: Applied Economics (AEJ: Applied) manuscript needs a model to interpret, discipline, or structure its empirical estimates — not to lead the paper. Calibrates how much theory belongs in an empirical-first journal and where it goes; it does not design the identification (aeja-identification) or build a standalone structural estimation.
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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.
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Plan, design, author, preview, publish, and manage Power BI reports across requirements, page design, local PBIR/PBIP edits, validation, screenshots, Fabric upload/download, and rebinding. Use for report lifecycle work; use semantic-model-authoring for model or DAX changes and fabriciq for data questions. Triggers: plan Power BI report, design report page, edit PBIR, preview PBIP, publish report, rebind report
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Render pixel-accurate ChatGPT mobile (iOS) screen mockups in light mode from a thread JSON. Supports user text bubbles, user image attachments, assistant markdown prose, citation chips, the OpenAI spiral logo, the Apps-SDK GPT chip in the composer, and three header styles (model-tag, plain title, "Get Plus"). Fixed 9:16 viewport. Outputs HTML + PNG.
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Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), ROUTED THROUGH THE fal-proxy so it bills the Ads agent. image_urls must be public URLs; --image puts a local file on the fal CDN first. The recipe names the model + prompt. Use for keyframes, flat-cover transforms, product hero edits. (For the OpenAI gpt-image family specifically, create-image-gpt-image-fal also exists.)
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Image-to-video (or text-to-video) via any FAL video model (Kling, Seedance, Veo), ROUTED THROUGH THE GooseWorks fal-proxy so the call bills the Ads agent. The template recipe names the model + params; image_url inputs must be public URLs (the orchestrator hosts local frames with the MCP media_upload). Returns the result video URL and downloads it. Use for the generative base clip of any video-ad format.
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Assemble an animated character-explainer video ad (any art style — Pixar-style 3D, claymation, anime, paper-cut, felt) (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anchor/keyframe/Kling-i2v/VO/music calls to their own capabilities. Use for the 3d-character-explainer listicle format.
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Collect and assess quality metrics for Simulink models using the metric.Engine API. Use when evaluating model design quality (maintainability, complexity, architecture) or testing completeness (unit testing, SIL, PIL, coverage, pass rates) after edits or test runs. Covers metric discovery, collection, report generation, and diagnostics. Triggers on: "collect metrics", "quality metrics", "metric.Engine", "testing metrics", "maintainability metrics", "SIL metrics", "PIL metrics", "what metrics are available", "assess quality", "coverage metrics", "generate metric report", "diagnose metric errors".
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Register, add, or update existing custom Simulink block libraries and configure block policy and knowledge index. Use when a user wants to register, set up, configure, or add existing .slx library files for agent-assisted model building. Do NOT use when the user wants to create or author a new library from scratch — that requires building-simulink-models.
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Use this skill when the user wants to curate the SATK library knowledge index for a Simulink project — mark commonly used blocks, correct auto-assigned block categories, or improve block descriptions so the model-building agent picks better blocks from custom libraries. Triggered by phrases like "mark blocks common", "improve block descriptions", "correct block category", "make the agent prefer certain blocks", and from Gate 3 of building-simulink-models.
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Estimate frequency response from Simulink models using frestimate. Use when frequency response should be obtained from simulation rather than model linearization.
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Manages MATLAB projects for Simulink workflows: path management, file registration, labels, source control configuration, and project lifecycle. Use when creating projects, adding models/dictionaries/requirements to projects, configuring labels for automation, fixing broken model references, or setting up source control for Simulink artifacts.
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Configures Simulink simulations non-destructively using SimulationInput objects — parameter overrides without modifying the model, batch sweeps via parsim, custom input signals via Dataset, and simulation data retrieval via logsout. Use when running sim()/parsim() with setVariable, setBlockParameter, setExternalInput, or when performing parameter sweeps and multi-run analysis. Not needed for one-shot simulations without configuration.
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Use when integrating legacy code, custom code, or C/C++ code into Simulink via C Function blocks. Configure Simulink C Function blocks programmatically using the SymbolSpec API. TRIGGER when the user: - Asks about calling or wrapping C/C++ code or libraries in Simulink - Wants to create, configure, or use a C Function block - Mentions integrating custom code, legacy code, or external C/C++ into a model - Asks to integrate a C++ class into a Simulink model or test bench - Mentions inputs/outputs/parameters for a C++ algorithm block
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Use when a Simulink model is incompatible with Simulink Design Verifier (sldvcompat returns false, or an SLDV analysis errors out on an unsupported construct) and needs to be made analyzable. Diagnoses the incompatibility, applies the matching fix to a copy, and confirms the copy is compatible; optionally raises confidence the fix preserved behavior via a back-to-back consistency replay (agreement on the generated tests, not a proof of equivalence). Do NOT use for running test generation, design error detection, or requirement verification themselves — only for making a model compatible so those can proceed.
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Generates draft requirements from Simulink models. Use when drafting or updating requirement artifacts from a model. Prefers Requirements Toolbox (.slreqx) when available; falls back to structured YAML.
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Import data into the Simulation Data Inspector (SDI) from MAT, CSV, or Excel files, from workspace variables, or from a Simulink simulation. Use when the user wants to view a logged file in SDI, load a bench-test log, or verify what a just-completed `Simulink.sdi.createRun` or `sim(model)` produced. Covers the `'file'`, `'vars'`, and `'namevalue'` sources of `createRun`, the auto-populate behavior after `sim()`, and post-import verification. Not for comparing existing runs (use `Simulink.sdi.compareRuns`); not for streaming data live during a running simulation (use `Simulink.sdi.createRunOrAddToStreamedRun` — a separate API); and not for authoring Gherkin or Simulink Test test cases, assertions, or regression tests (use `testing-simulink-models`). Do not activate on test-authoring or verification prompts even when no test-authoring skill is available — say so and stop.
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Write modern, high-performance C# code using records, pattern matching, value objects, async/await, Span<T>/Memory<T>, and best-practice API design patterns. Emphasizes functional-style programming with C# 12+ features.
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Build reactive/event-driven C# with R3 (Cysharp's modern reimplementation of Reactive Extensions). Covers the Observable<T>/Observer<T> model, the OnErrorResume error contract, async dispatch with AwaitOperation, Task/IAsyncEnumerable integration, TimeProvider/FrameProvider scheduling, the concurrency contract, and how R3 differs from System.Reactive (Rx.NET).
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。