「python」の検索結果

Choose supported scope and compatibility boundaries for SDK behavior changes; revisit when feedback changes the design.

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。

Assess a GitHub issue or PR for demonstrated need, supported alternatives, correctness, and maintainer action. Desk review only.

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。

Plan controlled runtime probes when explicitly invoked; execute only after the required probe approval.

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。

Carry implementation through an isolated worktree and local handoff. Use only when this skill is explicitly invoked.

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。

Fix the tiny credit-note formatting bug and rerun the exact targeted test command.

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 or update English SDK documentation against the requested implementation scope.

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 the task requires capturing or automating a real browser from the terminal.

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。

Review completed implementation changes before final verification. Use when repository policy requires independent review or the user explicitly requests it.

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 a concise prior authorization packet from local case files and payer policy docs.

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。

Run the required final formatting, lint, type, and test checks after eligible SDK changes pass 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。

Analyze CSV files in /mnt/data and return concise numeric summaries.

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。

Prepare the required local PR title, description, and branch suggestion after eligible implementation work is complete.

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 publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.

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 effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.

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。

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production 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。

Authoring playbook for building agents that write, edit, review, or refactor code. Use this when the user asks for an agent that writes scripts, generates code, reviews pull requests, refactors a codebase, fixes bugs, implements features, writes tests, or works with programming languages such as Python, TypeScript, JavaScript, Go, Rust, SQL, or shell.

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。

Directory and router for the marketing skills library. Use when you need to find the right marketing skill for a task, see what marketing capabilities exist, or get oriented in this plugin. 44 specialist skills across 8 pods (content, SEO + AEO, CRO, channels, growth, intelligence, sales enablement, ops), 59 stdlib Python tools. Routes to one skill — it does not execute marketing work itself.

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。

Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation, backlog grooming, story points, burndown charts, blocker resolution, or agile team health. Runs Python scripts to analyse sprint JSON exports from Jira or similar tools: velocity_analyzer.py for Monte Carlo sprint forecasting, sprint_health_scorer.py for multi-dimension health scoring, and retrospective_analyzer.py for action-item and theme tracking. Produces confidence-interval forecasts, health grade reports, and improvement-velocity trends for high-performing Scrum teams.

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Use when building a Kubernetes Operator — custom controllers that reconcile CRD state. Triggers on "build an operator", "CRD design", "reconcile loop", "controller-runtime", "kubebuilder", "operator-sdk", "metacontroller", "KOPF", "operator capability levels", or "custom resource". Ships CRD validator, reconcile-loop linter, and OperatorHub capability auditor (all stdlib Python), 4 references on the operator pattern + CRD design + reconcile patterns + tooling landscape, and a /operator-audit slash command. NOT a generic k8s skill — specifically the Operator pattern.

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。

Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production.

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。

Design and ship production-ready MCP (Model Context Protocol) servers from OpenAPI contracts instead of hand-written tool wrappers. Python and TypeScript support, schema validation, safe evolution. Use when exposing an existing API as an MCP server, building tool integrations for Claude or Codex or Cursor, or scaffolding an MCP project from scratch.

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。

Validate, test, and score the quality of skills within the claude-skills ecosystem. Comprehensive meta-skill: structure validation, Python script testing (syntax + imports + runtime + output format), multi-dimensional quality scoring with letter grades and tier classification (BASIC/STANDARD/POWERFUL). Use when authoring a new skill, auditing existing skills for tier promotion, setting up pre-commit hooks for skill quality, or integrating skill QA into CI.

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。

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

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。

Security audit and vulnerability scanner for AI agent skills before installation. Use when: (1) evaluating a skill from an untrusted source, (2) auditing a skill directory or git repo URL for malicious code, (3) pre-install security gate for Claude Code plugins, OpenClaw skills, or Codex skills, (4) scanning Python scripts for dangerous patterns like os.system, eval, subprocess, network exfiltration, (5) detecting prompt injection in SKILL.md files, (6) checking dependency supply chain risks, (7) verifying file system access stays within skill boundaries. Triggers: "audit this skill", "is this skill safe", "scan skill for security", "check skill before install", "skill security check", "skill vulnerability scan".

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。