Results for “package”

Use when working with Aws Lambda — aWS Lambda function analysis, invocation metrics, cold start analysis, layer management, and concurrency tracking. Covers function configuration review, error rate analysis, duration percentiles, provisioned concurrency utilization, and deployment package optimization.

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Java Javadoc best practices. Use when adding or reviewing documentation for Java types, methods, packages, and public APIs.

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Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.

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AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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Python research assistant with Context7 MCP. Use for Python library research, evaluating packages, enforcing strict Python coding standards, or fetching up-to-date library docs via Context7.

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Drive the AgentWiki knowledge platform via its CLI (`@aiagentwiki/cli`, command `agentwiki`) and MCP server (`https://api.agentwiki.cc/mcp`). Use this skill whenever the user mentions AgentWiki, agentwiki.cc, app.agentwiki.cc, the `agentwiki` CLI, the `@aiagentwiki/cli` npm package, the AgentWiki MCP server, or asks to create/list/search/share/publish documents, manage folders or tags, upload files or images, deploy static sites, traverse a knowledge graph, or query their AgentWiki workspace from the terminal or as an AI agent. Triggers include phrases like "agentwiki login", "aw_… API key", "create a doc on agentwiki", "search my wiki", "publish to agentwiki", "agentwiki MCP", or any task that would otherwise require opening app.agentwiki.cc manually.

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Run or maintain the full tdmcp repo quality-audit team: command sweeps, all package/Makefile/CI gates, security review, usability/flow review, refactor/test-gap analysis, coverage hardening, QA, and follow-up fix waves. Use whenever the user asks for a complete audit, improve repo/code quality, test all commands, find security/usability failures, refactor debt, add missing tests, re-run the audit, continue a previous quality wave, or verify the repo is ready.

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Verification methodology and gate checklist for the tdmcp Connectors Directory submission — run the docs build, build and inspect the .mcpb bundle, sweep for stale .dxt references, and cross-check the form-answer draft against the real requirements. Use to validate the submission package before a human submits, by executing commands and quoting evidence, not by trusting that work was done.

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Updates tdmcp docs and roadmap for newly shipped or newly merged capabilities. Use when asked to update docs, update the roadmap, reconcile release docs, document new features, sync docs with CHANGELOG/package release, refresh cookbook examples after a feature wave, re-run/continue/fix a docs-roadmap pass, or check whether docs/ROADMAP.md is current. Runs a sub-agent workflow: release audit -> roadmap/docs edit + cookbook sync -> QA.

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Orchestrates the tdmcp Anthropic Connectors Directory (Desktop Extension / MCPB) submission prep — designs the package, writes the privacy page, migrates .dxt→.mcpb, drafts all form answers, and QAs the result. Use for ANY work on the tdmcp directory/marketplace submission, including re-runs after a rejection ("update the submission", "redo the form answers", "re-migrate the bundle", "fix the privacy page", "the directory rejected us"). Drives a 4-agent pipeline (architect → docs-author ∥ bundle-engineer → QA).

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Build efficient, scalable monorepos that enable code sharing, consistent tooling, and atomic changes across multiple packages and applications.

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Use when proactively checking for and installing updates to the AOS (BDB Agent OS) package via npm.

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Apply platform accessibility best practices to SwiftUI, UIKit, and AppKit code. Essential companion to any SwiftUI, UIKit, or AppKit skill — always use together. Use whenever writing, editing, or reviewing ANY SwiftUI views, UIKit view controllers, AppKit views/window controllers, or platform UI — even when the user doesn't mention accessibility. Also use when the user mentions VoiceOver, Voice Control, Dynamic Type, Reduce Motion, screen reader, a11y, WCAG, accessibility audit, Nutrition Labels, accessibilityLabel, UIAccessibility, NSAccessibility, assistive technologies, or Switch Control. Not for server-side Swift, non-UI packages, or CLI tools.

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Reconcile App Store Connect subscriptions and in-app purchases with RevenueCat products, entitlements, offerings, and packages using asc and RevenueCat MCP. Use when setting up or syncing subscription catalogs across ASC and RevenueCat.

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Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.

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Scaffold, build, and package SwiftPM-based macOS apps without an Xcode project. Use when you need a from-scratch macOS app layout, SwiftPM targets/resources, a custom .app bundle assembly script, or signing/notarization/appcast steps outside Xcode.

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Orchestrate Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.

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Review resumes, cover letters, application packages, and evidence maps from an HR perspective. Use when the user asks for 자기소개서 리뷰, 자소서 검토, HR 검토, 제출 전 점검, unsupported-claim detection, company-name residue checks, or risk-focused revision advice before applying.

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Fetch and inspect external Git repositories and npm packages with their native command-line tools.

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Prepare a named version: preflight, version consistency, build/package, smoke test, checksums/notes, and release readiness. Publishing/tagging/deploy need separate authorization. Explicit-only.

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Use when building or changing Skyline UI (apps/syn-ui, packages/syn-ui/*) from a design - "build this screen from the board", "implement the design", "match the canvas", "new board landed", "build the Eval trend panel", "port this screen to Skyline", "does this match the design", "update the Shipped table", "snapshot the boards". Covers reading a `*.dc.html` board and the Skyline spec, building from them, comparing 1440px and 390px screenshots against the board, and recording the shipped screen. Do NOT use for small tweaks to a screen that has already shipped (change the code directly, no board needed), for the frozen React app apps/syn-dashboard-ui (never edit it), or for refreshing the CI screenshot baselines alone (see design/README.md).

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Mirror an iOS Simulator into the Codex in-app browser and render SwiftUI previews from importable Swift packages in that simulator with hot reload. Use when a user wants to watch or interact with an iOS app in the browser, see a SwiftUI preview outside Xcode Canvas, iterate live on a preview, or capture browser-visible simulator proof.

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Expert patterns for Salesforce platform development including Lightning Web Components (LWC), Apex triggers and classes, REST/Bulk APIs, Connected Apps, and Salesforce DX with scratch orgs and 2nd generation packages (2GP). Use when: salesforce, sfdc, apex, lwc, lightning web components.

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