“code generation”的搜索结果

Use this skill when the user requests to generate, create, or improve documentation for code, APIs, libraries, repositories, or software projects. Supports README generation, API reference documentation, inline code comments, architecture documentation, changelog generation, and developer guides. Trigger on requests like "document this code", "create a README", "generate API docs", "write developer guide", or when analyzing codebases for documentation purposes.

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Coordinate a Claude Code plugin release by updating version numbers across manifests, checking the build, creating a Git tag and GitHub release, and regenerating the changelog. The workflow also publishes to npm and verifies the published version.

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• Choosing a version increment for a plugin release
• Synchronizing versions across plugin manifests
• Creating a GitHub release and regenerating the changelog

Plan reviews of AI-generated code before running or merging it. Define language-specific checks, tests in isolated environments, and evidence requirements, while recording coverage limits and gaps that prevent approval.

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• Planning reviews of AI-generated code before merging
• Defining language-specific security checks
• Recording test evidence and unresolved gaps

Review AI-generated code for problems hidden behind successful runs. Check failure handling, resource cleanup, unusual inputs, nonexistent dependencies, and departures from project conventions to identify missing safeguards before merging or accepting the work as complete.

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• Audit code after an AI generation session
• Review an AI-generated pull request before merging
• Investigate brittle code that appears to work

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

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• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

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• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Helps speed up LLM text generation by comparing methods that predict and verify multiple tokens at once. Covers draft model selection, Medusa prediction heads, Lookahead Decoding settings, and deployment examples for inference services.

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• Reducing chatbot response latency
• Comparing methods for faster code generation
• Selecting a small draft model for a larger LLM

Generate images from text or reference images using multiple image services. Choose aspect ratios, sizes, and quality settings, or generate a collection of images in batches from saved prompt files.

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• Create illustrations from text descriptions
• Change scenes using reference images
• Generate images in specific aspect ratios

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Generate music and sound effects from written descriptions with AudioCraft. Covers MusicGen and AudioGen, using reference melodies or musical styles, producing stereo audio, adjusting generation settings, and saving generated sounds as audio files.

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• Generate music from a written description
• Create sound effects and environmental audio
• Generate music using a reference melody or style

Continue library-dependent Python tasks when sandbox execution fails by switching to a shell script. Covers recognizing import errors, preserving script text, checking execution output, and troubleshooting package availability for tasks such as PDF generation or visualization.

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• Retrying Python code after missing-library errors
• Continuing PDF generation through shell execution
• Running visualization code with installed packages

Recover PDF or DOCX generation when execute_code_sandbox fails with unclear errors. Use run_shell to execute Python directly, check required libraries, report specific failures, and verify that the output file was created.

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• Retry PDF generation after an unclear sandbox error
• Generate DOCX files by running Python through the shell
• Check and install missing document libraries

Recover from Pandoc PDF generation failures by checking installed PDF engines and retrying alternatives. The guide matches engines to Unicode text, simple Latin documents, or HTML-based layouts, and includes an automated fallback script.

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• Retrying failed Pandoc PDF conversions
• Choosing an engine for Japanese or other Unicode text
• Automating PDF engine fallback in document pipelines

Generate DOCX, PDF, and HTML from Markdown through separate, observable conversion steps. The workflow replaces problematic characters for PDF output, captures error messages, and switches PDF engines when needed to troubleshoot document generation failures.

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• Export one Markdown document to DOCX, PDF, and HTML
• Investigate unclear document conversion errors
• Replace special characters before PDF conversion

Guide a bug bounty investigation from scope checks and asset discovery to vulnerability testing, impact validation, and report writing. Covers web applications, source code, and AI features, with an emphasis on reproducible evidence of real harm.

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• Planning an investigation for a new bug bounty target
• Reviewing source code for risky implementation patterns
• Testing web and AI features for security flaws

The art-route layer of the Forge skills. route_media.py is the one command that generates an image or an image-to-video clip - through the paid API of any provider whose key is configured (OpenAI, Google Gemini, xAI, BytePlus ModelArk, fal.ai; the configured key is the owner's consent), else through the user's own signed-in Codex or Grok CLI - and prints one JSON line with the route, artifact, sha256 and cost estimate; with no route it answers no-route (codeart2d is then the last resort). Also the capability check (forge_doctor), API key configuration, the spend ledger, resume and Codex image adoption. Use when a Forge skill sends a generation here, for the capability check, or when the user asks about keys, routes or spend. Not for planning, processing or QA of sprites, video frames or maps (generate2dsprite, video2dsprite, generate2dmap) or for code-drawn art (codeart2d).

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Guidelines for introducing and using nullable reference types (NRT) and System.Diagnostics.CodeAnalysis nullable attributes in C# / .NET codebases. Covers the nullability model, flow analysis, the null-forgiving operator, API design rules, the full attribute catalog (AllowNull, DisallowNull, MaybeNull, NotNull, NotNullWhen, MaybeNullWhen, NotNullIfNotNull, MemberNotNull, MemberNotNullWhen, DoesNotReturn, DoesNotReturnIf), the C# 14 field keyword, incremental migration of legacy codebases, and a code-generation checklist.

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Provides Qwen Coder CLI delegation workflows for coding tasks using Qwen2.5-Coder and QwQ models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Qwen for tasks such as code generation, refactoring, debugging, or architectural analysis. Triggers on "use qwen", "use qwen coder", "delegate to qwen", "ask qwen", "second opinion from qwen", "qwen opinion", "continue with qwen", "qwen session".

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Translate source code between programming languages at function, class, and module levels while preserving behavior and generating verification tests. Use when translating code from one language to another (e.g., "translate this Python module to JavaScript", "convert this Java class to C#", "port this code to Go and generate tests"), migrating codebases between languages, or creating equivalent implementations across different technology stacks. Handles idiom adaptation, standard library mappings, and test generation.

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Automatically repair buggy code and generate comprehensive tests for Python, Java, and C++ programs. Use when users need to fix logic errors or runtime errors in functions, modules, or repositories. Accepts specifications via natural language descriptions, existing test cases, or input/output examples. Generates corrected code, creates or updates tests to verify correctness and prevent regressions, and produces a detailed report explaining the bug, fix, and testing strategy. Triggers on requests like "fix this bug", "repair this code", "debug this function", or "this code is broken".

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Use for .NET code generation work with Roslyn, Microsoft.OpenApi, Razor templates, DTO/manager/controller generation, REST API generation, C# HttpClient generation, Angular/Axios TypeScript request clients, generated formatting, and deterministic output.

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ALIGN-video for Claude Code with independent Codex adversarial review. Create or revise a video through agent-authored code, rendering, editing and fresh-context audiovisual review, without image, video or music generation models. Use for reference-led animation, procedural films, motion design, explainers, construction replays, and continuing an existing coded-video project. Carries the work from reference study and animatic through a finished, reproducible film; supports explicitly requested self-review when appropriate.

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ALIGN-video for Codex. Create or revise a video through agent-authored code, rendering, editing and fresh-context audiovisual review, without image, video or music generation models. Use for reference-led animation, procedural films, motion design, explainers, construction replays, and continuing an existing coded-video project. Carries the work from reference study and animatic through a finished, reproducible film; supports explicitly requested self-review when appropriate.

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Generate frontend code drafts using Gemini 3.1 Pro's one-shot UI generation. Use when building React components, HTML/CSS pages, SVG animations, or converting screenshots to code. Produces fast, cheap drafts (7.5x cheaper than Opus) that Claude reviews and refines. Supports design system context loading (1M token window), screenshot-to-code pipeline, and physics-based SVG animation. Trigger: "generate a UI", "screenshot to code", "draft a component", "SVG animation", "convert this design", "gemini forge", "forge a UI".

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