982 件 ・ 関連度順
概要と使いどころ
Use when releasing the Cline VS Code extension — stable (standalone SDK build of main via ext-vscode-publish), nightly (ext-vscode-publish-nightly, manual dispatch), or an emergency legacy-branch hotfix (ext-vscode-publish-legacy). Guides version selection, changelog, workflow dispatch, environment approvals, tagging, post-publish verification, and the remaining retirement of the finished A/B rollout machinery.
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。
Mandatory preflight for web frontend visual design. If available, call read_skill for bundled:taste before the first frontend create or edit tool call whenever creating or visually styling a human-viewed web page, component, app, dashboard, or interactive report. Do not load it for non-visual frontend logic or machine-only HTML. If unavailable, continue without warning. Negative constraints only; user requests and other explicit styles still win.
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。
Set up Muse's built-in Tailscale connector, join a tailnet or Headscale network, check status, and reach private machines through the TCP tunnel proxy. Read for Tailscale, VPN, MagicDNS, network egress, exit-node, or browser routing questions and supported limits.
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 branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, service blueprint, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.
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。
How this machine's Hindsight coding-agent memory works — the plugin behind the 🧠 banner. Use when the user says "store/remember this in hindsight", asks what the memory/knowledge pages are, wants to configure per-repo memory (disable, rename banks, git depth), or something memory-related looks broken.
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。
Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.
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。
Supports machine learning in Python with scikit-learn. Applies when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
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。
Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for open-system dynamics use qutip.
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。
Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.
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。
Supports geospatial research workflows for remote sensing, vector and raster GIS, spatial statistics, terrain and network analysis, and machine learning for Earth observation. Use when processing satellite imagery, aligning coordinate systems and raster grids, accessing STAC catalogs, analyzing geospatial time series, or implementing scientific GIS workflows in Python, R, Julia, JavaScript, C++, Java, Go, or Rust.
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。
Retrieves, versions, and publishes scientific datasets with DataLad and git-annex, and captures computational provenance with datalad run, rerun, and containers-run. Use when cloning or fetching data from OpenNeuro, DANDI, datasets.datalad.org, or any DataLad dataset; when a file in a dataset reads as a broken symlink or a small pointer instead of real data; when an analysis needs a machine-readable record of how each output was produced so it can be re-executed; or when publishing a dataset to siblings such as a GitHub repository plus a storage remote. Also use to decide between DataLad and plain Git for a data-carrying repository.
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。
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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。
Operates Fictiv (app.fictiv.com), the on-demand manufacturing platform, end to end in the user's browser. Covers uploading CAD parts, configuring process, material, finish, threads, tolerances and inspections, getting instant or manual quotes, reading and fixing DFM feedback, choosing lead time and region, checking out and paying (card or PO), tracking orders, reordering, and troubleshooting. Applies when the user mentions Fictiv, wants a part CNC machined, 3D printed, sheet-metal fabricated, urethane cast, injection or compression molded, or die cast through an online service, asks to "get a quote" or "order parts" for a STEP/SLDPRT/STL file, wants to check a Fictiv quote or order status, or has a problem with a Fictiv upload, DFM warning, price or checkout. Also applies when the user wants custom parts manufactured and has a Fictiv account, even if Fictiv is not named.
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。
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
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。
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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 an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
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。
Master Godot 4 GDScript patterns including signals, scenes, state machines, and optimization. Use when building Godot games, implementing game systems, or learning GDScript best practices.
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。
Mistake-proof code so misuse cannot be expressed, rather than warning against it. Use when designing an interface, schema, or state machine and the user wants it hard to get wrong ("make invalid states unrepresentable", "so callers cannot screw it up", "type-safe API", "pit of success"); when auditing existing code for footguns ("what could bite us here", "what is easy to misuse", "poka-yoke this repo", "review this diff for ways to get it wrong"); or when a bug has recurred and the fix must close the class rather than the case ("make sure this never happens again", "this is the third time"). Especially for money, auth, permissions, deletion, migrations, and pipelines where failure is silent. Classifies every finding by what happens when the mistake occurs and how the device notices, which is what keeps it from collapsing into generic code 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。
Audit and improve GitHub Codespaces efficiency. Use this skill when a user wants faster Codespaces startup, lower Codespaces spend, slim devcontainers, right-size machines, tune idle timeout, or scope prebuilds to branches with sustained 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。
Analyze a screen recording of a manual process and produce targeted, working automation scripts. Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation at multiple complexity levels using tools already installed on the user machine.
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 and fix the OpenViking memory plugin on this machine: the plugin install (enablement, hooks, MCP server), the client configuration (ovcli.conf / ov.conf / OPENVIKING_* env) and the connection to the OpenViking server (reachability, 401/403, /mcp). Use whenever memory "isn't working": no <openviking-context> block, empty recall, captures not landing, missing or failing MCP memory tools, 401/403, an offline statusline, right after installing/updating the plugin or switching servers/keys, or when the user asks for the plugin's status. Triggers: "memory not working", "check OpenViking", "plugin status", "记忆没生效", "插件状态", "连不上 OpenViking", "recall 为空", "401".
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。
Find, use, create, install, share, update, and migrate agent skills stored in OpenViking (viking://~/skills and viking://agent/skills). Use it when a search result, or the session's <available-skills> list where the harness injects one, names a skill that fits the task; when a task looks like one a stored skill would cover; when the user asks to write, save, install, or share a skill from text, a Git repository, or a local folder; when a skill should work in every harness and on every machine; or when the user wants to move local skills (~/.claude/skills, ~/.agents/skills, ~/.cursor/skills) into OpenViking — "upload my skills", "save this as a skill", "迁移本地 skill", "把 skill 存到 OpenViking", "新建一个 skill". Covers the add_skill tool, running a skill's helper files, and which local skills must stay local.
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 and fix the OpenViking memory plugin for Codex on this machine: the plugin install (enablement, hooks, MCP server), the client configuration (ovcli.conf / ov.conf / OPENVIKING_* env) and the connection to the OpenViking server (reachability, 401/403, /mcp). Use whenever memory "isn't working": no <openviking-context> block, empty recall, captures not landing, missing or failing MCP memory tools, 401/403, an offline statusline, right after installing/updating the plugin or switching servers/keys, or when the user asks for the plugin's status. Triggers: "memory not working", "check OpenViking", "plugin status", "记忆没生效", "插件状态", "连不上 OpenViking", "recall 为空", "401".
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 a personal Claude Code usage & impact report ("receipts") from this machine's local session transcripts — for justifying Claude Code usage/spend to a manager, self-review, or "what have I been using this for" check-ins. Mines ~/.claude/projects locally (no extra API calls beyond one final write-up), cross-references local git history, and writes a markdown report plus a self-contained HTML receipt to your home directory. Use when the user asks for "receipts", an "impact report", "usage report", wants to "show my Claude Code activity", "prove the value of Claude Code", or runs `/receipts`.
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。