“adapters”的搜索结果

Phase C of a WinForms-to-Avalonia migration: implement platform and infrastructure adapters for dependencies discovered in Phase A/B, such as persistence, process execution, dialogs, notifications, or environment capabilities. Use only in an explicit migration when such dependencies leak into Forms/core or when the orchestrator routes here. Do not invent adapters for capabilities the product does not have, and do not implement main-window XAML.

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Skill for the Adapters area of paddock. 108 symbols across 22 files.

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Fetch any URL and convert to markdown using baoyu-fetch CLI (Chrome CDP with site-specific adapters). Built-in adapters for X/Twitter, YouTube transcripts, Hacker News threads, and generic pages via Defuddle. Handles login/CAPTCHA via interaction wait modes. Use when user wants to save a webpage as markdown.

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Build command-only adapters for `eval` run mode using strict stdin/stdout JSON contracts.

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Use when the project follows hexagonal (ports & adapters) architecture. Prevents domain code from depending on Spring or JPA. Use when you see packages like domain/, application/, infrastructure/, or adapters/ in the project structure.

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Deploy with unified build/test/deploy intent across providers using .agent-loom/deploy.yml and per-provider adapters. Load when the user asks to deploy, ship to preview, release to production, or run a provider-agnostic deploy flow. Also triggers on "deploy anywhere", "deploy to Vercel", "deploy with GitHub Actions", "preview deploy", or "ship this". Runs preflight before any deploy — stops on missing secrets. Pairs with ci-cd-and-automation for pipeline design. Ships vercel and github-actions adapters; extensible adapter interface.

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用Python实现文字生成图像、现有图像风格转换和指定区域补全，并指导如何控制构图与风格、调整生成参数及减少内存占用。

• 根据文字生成图像
• 将现有图像转为水彩风格
• 补全照片中的指定区域

为native Paperclip Runner接入新的执行工具或提供商，从需求整理、通信协议调查到实现、打包，以及在实际环境中验证完整流程。

• 为Runner新增连接
• 调查执行工具的通信协议和能力
• 验证连接的取消与恢复

设计并验证Dot等外部机器人与Paperclip的双向工作连接，涵盖邀请、权限、结果保存、重启恢复及Cloud部署，并在真实账户和实际部署环境中确认运行情况。

• 与外部机器人双向协作
• 构建邀请及连接设置界面
• 验证重启和重复通知后的恢复

将成功任务中的步骤和经验保存为学习模式，并整合到长期存储中。支持按领域追加学习、分别检查不同存储位置的学习结果，以及压缩已保存数据。

• 保存成功任务中的经验
• 整合累积的任务历史
• 按领域创建追加学习数据

Helps agents prepare image, video, speech, music, and avatar generation requests with an exact model and API host. It explains service-specific inputs, request validation, and job resumption so unavailable models are not silently replaced.

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• Specify an exact model and API host for media generation
• Prepare image edits with reference images and masks
• Resume a timed-out generation job without submitting it again

Build HyperFrames animations from small motion recipes, scene templates, and transitions. The skill guides runtime selection, including GSAP, Lottie, and Three.js, and provides an audit script to examine timing and choreography while following HyperFrames requirements for reproducible rendering.

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• Combine motion recipes into a scene
• Build a multi-phase brand reveal from a template
• Design transitions between scenes

Build and review HyperFrames animations using motion recipes, scene templates, and transitions. Find guidance for seven animation runtimes, including GSAP and Three.js, and inspect existing scenes for timing gaps and inconsistent motion.

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• Combine motion recipes for text and shapes
• Build scenes with several animation phases
• Choose an animation runtime

Move reusable code and its tests from OpenHuman into the appropriate tiny* submodule. Separate host responsibilities, update module interfaces, verify the migration, and align release versions and integrity hashes when a loadable module changes.

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• Extract reusable logic from the OpenHuman core
• Move implementation tests into the owning submodule
• Update TinyBus contracts and host adapters

Configure supervised fine-tuning for models that process images and text. Define which components remain unchanged, set LoRA adapters for updating selected model weights, choose image resolution limits, and investigate training runs whose evaluation results fail to improve.

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• Configure training for an image-and-text task
• Decide whether to train vision layers
• Investigate training with flat evaluation scores

Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.

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• Running one-off generation or batch jobs from a terminal
• Checking a new model's basic operation
• Inspecting accepted inputs before running a pipeline

Coordinate multiple agents to maintain context-mode, from sorting GitHub issues and reviewing pull requests to preparing releases and announcements. Workflows require reproduced bugs, source evidence, test-first changes, and checks across supported platforms.

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• Investigating reported bugs and their history
• Reviewing contributor pull requests
• Preparing releases and compatibility checks

Uses OpenCLI to inspect and interact with Chrome pages, including logged-in websites. Agents can fill forms, extract page data, inspect captured network requests, take screenshots, and manage Chrome tabs, groups, and windows.

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• Filling forms and clicking controls on logged-in websites
• Extracting page text and taking screenshots
• Inspecting network requests for API investigation

Diagnose OpenCLI commands broken by website changes, collect execution traces, patch the adapter that connects to the site, and retry. After a verified repair, prepares a GitHub issue for submission with user approval.

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• Repair commands broken by website layout changes
• Adapt to changed API responses
• Distinguish empty results from adapter failures

Adapt large language models to specific tasks using LoRA and QLoRA, methods that train a small set of added parameters. Get guidance on preparing examples, choosing training settings, evaluating outputs, and saving or merging trained adapters.

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• Adapting models to specialized terminology and output formats
• Planning training with limited GPU memory
• Preparing instruction-response datasets

Adapt large language models to specific tasks through fine-tuning, or additional training. Prepares and validates datasets, configures LoRA or QLoRA to train a small subset of parameters, and supplies training settings, evaluation scripts, and deployment optimization guidance.

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• Preparing and validating JSONL training data
• Choosing LoRA or QLoRA for available GPU memory
• Configuring training and monitoring overfitting

Plan and review new Omnigent harness integrations, the adapters connecting agent runtimes to the platform. Compare SDK and subprocess approaches with native interface wrappers, using capability matrices and checklists for implementation and testing.

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• Plan a new agent runtime integration
• Compare SDK and native integration requirements
• Review approval, streaming, and session support

Prepare a Claude Ads project by organizing business goals, ad accounts, data sources, credential references, data retention rules, and approval limits. The result is a validated setup profile for subsequent advertising work.

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• Preparing a new advertising project
• Organizing credential references and data sources
• Defining data retention and deletion rules

Diagnose paid advertising performance using account evidence, financial constraints, and experiments. Draft ranked changes to budgets, bids, or ad delivery, and apply supported operations only after approval and state checks, retaining a way to reverse them.

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• Reviewing budget allocation and bids
• Planning ad pauses and creative rotation
• Assessing search terms before adding negative keywords