Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: bloatware-removal

英語版ディレクトリ

a machine learning image inpainting task that instinctively removes watermarks from image indistinguishable from the ground truth image

4.6K
Stars
76/100
信頼
カテゴリ: ml-automation監査

Image Background Removal Toolkit - Open Source and API Models

1.2K
Stars
74/100
信頼
カテゴリ: robotics-iot監査

Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

34K
Stars
70/100
信頼
カテゴリ: research監査

When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.

25K
Stars
77/100
信頼
カテゴリ: design-creative監査

Cleaner is a Kubernetes controller that identifies unused or unhealthy resources, helping you maintain a streamlined and efficient Kubernetes cluster. It provides flexible scheduling, label filtering, Lua-based selection criteria, resource removal or update and notifications via Slack, Webex and Discord. it can also automate clusters operations.

794
Stars
72/100
信頼
カテゴリ: devops監査

Mandatory iPolloWork code-change gate for modern, minimal, performant, reuse-first implementation and clean repository ownership. Use whenever AI creates, edits, deletes, or refactors application code, server code, packages, scripts, tests, dependencies, schemas, routes, UI, or generated-file workflows. Reuse existing code before creating files, keep one source of truth, prevent parallel implementations and junk directories, justify every new file or dependency, and audit the current change before completion.

4.5K
Stars
67/100
信頼
カテゴリ: security監査

Dependency management strategies for Golang projects — go.mod management, installing/upgrading packages, Minimal Version Selection, vulnerability scanning, outdated dependency tracking, binary size analysis, Dependabot/Renovate setup, conflict resolution, and go.work workspaces. Use when adding, removing, or upgrading Go dependencies, auditing vulnerabilities, resolving version conflicts, or setting up automated dependency updates.

3.0K
Stars
68/100
信頼
カテゴリ: security監査

The First Dynamic Map Removal Benchmark | Included 8 SOTA methods | Continous updating

425
Stars
67/100
信頼
カテゴリ: robotics-iot監査

A portable agent skill that removes AI writing patterns while preserving the writer's voice, compatible with Claude Code, Codex, Cursor, and other agent runtimes.

10
Stars
66/100
信頼
カテゴリ: utility監査

A list of resources for video enhancement, including video super-resolutio, interpolation, denoising, compression artifact removal et al..

599
Stars
62/100
信頼
カテゴリ: media-automation監査

Attentive Generative Adversarial Network for Raindrop Removal from A Single Image (CVPR 2018)

548
Stars
61/100
信頼
カテゴリ: robotics-iot監査

Official Code for ICCV 2021 paper "Towards Flexible Blind JPEG Artifacts Removal (FBCNN)"

523
Stars
65/100
信頼
カテゴリ: robotics-iot監査