DevClip is an open-source clipboard management tool that upgrades your workflow via the ⁠Alt + V⁠ shortcut, built using Golang (Backend) and React + Wails v2 (Frontend). Tailored specifically for developers, it integrates handy offline utilities directly into your clipboard history, including automatic JSON/SQL formatting/minifying, split-view JWT decoding with expiration tracking, two-way Unix timestamp conversion, smart search/filtering, and drag-and-drop pinning. It even includes a local dashboard for usage statistics.
Check out the source code, contribute, or drop a ⭐️ star to support the project at: https://github.com/hieudeptrai196/dev_clip

[Showcase] Cave Prompt — A Semantic Prompt Compiler for Claude Code

👉 Check out the repo here: Link

Have you ever written a detailed request, sent it to an AI, and gotten an answer that was technically correct but completely missed the point?

The AI isn't the problem—it's the "noise" in your prompt. Key constraints get buried at the end, or the core intent gets lost in conversational filler.

Cave Prompt is a compiler skill that runs before your AI processes your request. It extracts your true intent, surfaces hidden requirements, resolves conflicting constraints, and restructures everything into a high-density execution prompt—so the AI works on what you actually need, not just what you literally said.

Key Advantages:

Attention front-loading: Critical constraints go first, where the model weighs them most heavily.

Hidden requirement extraction: Finds what you didn't explicitly say but genuinely need.

Constraint conflict resolution: Catches contradictions before the AI goes in the wrong direction.

Vague → specific: Transforms fuzzy ideas (e.g., "track my finances") into structured specs (e.g., "a 3-sheet Google Sheets dashboard with SKU-level margin tracking").

Who is this for?

Non-technical users: Those who describe things conversationally and aren't sure how to structure a prompt.

Product managers & business owners: Anyone who knows what they want but struggles to translate it into precise AI instructions.

High-stakes tasks: Anyone where a misread from the AI would cost real time or money.

Teams: For standardizing prompt quality across members with different communication styles.

When to use it:

Use it for long, multi-constraint requests where clarity matters. Skip it for simple, single-intent prompts—the overhead isn't worth it there.

This is my first skill build, so there may be rough edges—I truly appreciate your patience and any feedback you might have!

As a developer, I’m putting a lot of heart into this project. A ⭐ on the repo would be a huge boost for my work and personal growth—it really motivates me to keep building and improving. If you find the idea useful, I’d be incredibly grateful for the support. Thanks for reading and for helping me grow! 🙏