23 candidates reviewed. 7 made the cut.

PUA — the high-agency prompt that treats your agent like a senior engineer

6,312 stars, 273 forks. That's a lot of traction for what is, at its core, a system prompt.

PUA (from tanweai) opens with a premise: you are a P8-level engineer. Anthropic once had high expectations for you. Now act like it. The framing is deliberately provocative — "pua" is Chinese slang for psychological manipulation — but the actual technique is about injecting high-agency identity into an agent's context. The project has a landing page, a Discord, and apparently a WeChat group. It's cross-platform: there are badges for Claude Code, Codex, Cursor, Kiro, and OpenClaw.

The underlying idea is real even if the packaging is theatrical: agent behavior is heavily influenced by how the system prompt frames identity and expectations. This is a direct expression of that thesis, and 6K+ stars in ~5 days suggests it's landing.

Worth knowing about even if you don't use it directly.

Read more: → https://github.com/tanweai/pua

GSD-2 — meta-prompting and spec-driven development for long-horizon agent tasks — 855 stars, 72 forks, engagement surged 3x+. The core problem it addresses: agents lose the plot on complex, multi-step tasks. GSD-2 layers meta-prompting, context engineering, and spec-driven development on top of your agent setup so it can run autonomously without drifting. If you're building agents that need to stay on-task for extended periods, this is the current best-of-breed approach. Evaluate Now. → https://github.com/gsd-build/gsd-2

MetaClaw — an agent that learns and evolves from conversation — 851 stars, 110 forks, engagement surged 3x+. The pitch is simple: talk to your agent, it learns, it changes. The details are thin but the traction is real — 850+ stars and 3x engagement surge. Early, but the self-improving agent loop is a pattern that's going to matter. Watch. → https://github.com/aiming-lab/MetaClaw

OneCLI — open-source credential vault for AI agents, built in Rust — 152 HN points, 48 comments, 465 stars across GitHub + HN. The problem statement is blunt: agents are being handed raw API keys, and it's going badly. OneCLI sits between your agents and external services — you store real credentials once, hand agents placeholder keys, and OneCLI handles the swap at request time. Agents never see actual secrets. Cross-source signal (GitHub + HN) is a good sign. Evaluate Now. → https://github.com/onecli/onecli

Axe — a 12MB binary that replaces your AI framework — 195 HN points, 114 comments. The argument: most AI frameworks are built around long-lived sessions with massive context windows. That's expensive and fragile. Axe treats agents like Unix programs — each one is a focused TOML config, runs from CLI, takes input, produces output. Composable by design. The HN thread is worth reading. Watch. → https://github.com/jrswab/axe

Understudy — teach a desktop agent by showing it once

104 HN points, 40 comments. Cross-source with GitHub.

Understudy addresses a real gap: most agents live in one surface (browser, terminal, or chat), but real workflows span all of them. Understudy is a local-first desktop agent runtime that can operate GUI apps, browsers, shell tools, files, and messaging in a single session.

The interesting part is the teach-by-demonstration model: you do a task once, it records screen video plus semantic events, extracts intent (not a coordinate replay), and builds a reusable skill from it. That's a meaningful distinction — coordinate-based recording breaks when UI changes; intent-based extraction is more durable.

Still early, but the architecture is thoughtful and the HN discussion is substantive.

→ https://github.com/understudy-ai/understudy

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