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1. Unslop

Unslop is an agent skill that audits an AI-built interface for familiar shortcuts—purple gradients, excessive glow, emoji labels, shouty copy, and inconsistent spacing—then revises it in deliberate design passes. It works with Codex or Claude Code across React, Vue, Svelte, Tailwind, and plain HTML/CSS, and can inspect a live URL. Its most useful extra is a short before-and-after story video, turning design cleanup into a clear, shareable proof.

Operational value: Give landing pages and internal tools a repeatable visual review before calling them finished.

Quick video: Run the skill on one visibly generic dashboard and show the interface correcting itself in a sub-ten-second story clip.

Setup / license reality: The skill is CC BY 4.0, not a conventional software license; reuse and modification require attribution. Playwright and Chromium are needed for live capture, and FFmpeg for story video. Optional generated imagery can call paid services, but the skill is designed to show the prompt and ask first.

2. Mika 0.1.37

Mika is an on-device Android agent that can work with apps, websites, files, voice, and tasks handed over from a computer through CLI, skill, or MCP. There is no Mika relay server: screen and prompt data go to the model provider you configure. Version 0.1.37 improves task recovery, ADB pairing, virtual-display behavior, and MCP naming, while adding separately authorized root-assisted startup and root commands for already-rooted phones.

Operational value: Bridge desktop agents into Android-only workflows without routing every action through a vendor-controlled orchestration cloud.

Quick video: Send a harmless research task from Codex to an Android phone, watch it use Chrome on a virtual display, then return the result.

Setup / license reality: Apache-2.0 app code; Android 11+ supports chat/web/files, while phone control needs Android 12+ and one-time wireless-debugging or USB setup. Use the published APK hash, a spare profile, and a limited provider key. Leave root commands disabled unless a specific test truly needs them.

3. agent-autoloop 1.6.0

agent-autoloop keeps command-line agents working after the terminal closes, routes uncertain decisions back as cards, and resumes the same session after an answer. Rules can watch schedules, webhooks, or issue feeds; plugins connect Codex, Claude Code, ACP, and other commands. Version 1.6.0 adds native Windows support, Task Scheduler persistence, desktop notifications, protected local backups, pause/resume controls, and Windows-aware path handling. That is meaningful progress for local unattended work.

Operational value: Let bounded maintenance and monitoring continue in the background while preserving explicit human decision points.

Quick video: Start a two-step Codex job, close the terminal, answer its decision card, and prove the same session finishes afterward.

Setup / license reality: MIT code; Windows, macOS, and Linux support require Node 24+, Git, and an already authenticated CLI agent. The one-line installer executes remote code, so inspect or pin it first. Begin with a local demo rule, no production secrets, no automatic merge, and strict channel/plugin permissions.

4. rea-video

rea-video analyzes a reference video shot by shot and estimates what was filmed, AI-generated, rendered in 3D, or built in code. It separates audio, identifies pacing and captions, then proposes lighting, camera setups, prompts, or a code path for recreating the structure. Tools-only mode can run offline; adding a vision model helps with recompressed social clips. Crucially, it produces a remake plan rather than pretending every shot needs expensive generation.

Operational value: Convert visual inspiration into an editable production map before committing models, footage, or budget.

Quick video: Feed it a 30-second mixed-media clip and reveal which shots should be filmed, coded, or generated—and why.

Setup / license reality: Apache-2.0 code requiring Python 3.10+ and FFmpeg. Optional Gemini or Doubao analysis incurs provider cost and data transfer. Its verdicts are evidence-based estimates, not provenance certificates. Use only videos you may lawfully analyze; reconstruction plans do not grant rights to the original music, footage, likenesses, or style.

5. Muse4AI

Muse4AI is an unofficial Windows bridge that exposes a signed-in Meta Muse session to local agents through REST, MCP, and OAuth. It can queue image and video jobs, extend or edit results, manage a local library, and issue scoped, revocable keys. The bridge binds to localhost and says tokens and history remain under the user’s AppData folder. The integration is appealing, but its newness and reliance on automating a third-party web session make it a cautious trial.

Operational value: Let an agent request and organize Muse generations without manually moving prompts and files between interfaces.

Quick video: Authorize one local MCP client, queue an image and short video, then revoke that client’s scoped token.

Setup / license reality: MIT bridge code, but Meta Muse is proprietary and separately governed. The Windows installer is currently unsigned, the project has no tagged release, and web-session automation can break or conflict with service terms. Prefer source review and a disposable account/profile; never expose the localhost API through port forwarding.

Apache-2.0 PEFT remains the practical route to LoRA and other parameter-efficient tuning methods, reducing trainable parameters and storage while integrating directly with the wider Transformers ecosystem.

Ray’s Apache-2.0 distributed runtime still earns attention when Python workloads outgrow one process or machine, especially for parallel inference, data pipelines, training, and evaluation at scale.

OpenHands remains a capable self-hostable coding-agent platform with sandboxed workspaces and broad model support. Inspect licensing by directory: the core is MIT, while enterprise components use PolyForm terms.

LiteLLM’s OpenAI-compatible gateway remains useful for switching providers, applying budgets, logging usage, and centralizing retries without rewriting every application; inspect enterprise folders and provider terms separately.

PyTorch remains the BSD-3-Clause foundation behind an enormous share of local AI research and production, with mature acceleration, debugging, export, and an ecosystem that keeps experiments portable.