Alternatives
Products that do what CLI Agent Lint does
Is your CLI ready for agentic use?
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Scan your website to see how ready it is for AI agents.
Apr 2026 · isitagentready.com
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Connect AI agents to 1000+ apps directly from your terminal
Mar 2026 · composio.dev
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I've been building computer-use tools for a while, and I quietly launched this about a month ago (122 Stars on GH). I figured it was worth sharing here. Over the last few months, a lot of computer-use agents have come out: Codex, Claude Code, CUA, and others. Most of them seem to work roughly like this: 1. Take a screenshot 2. Have the model predict pixel coordinates 3. Click x,y 4. Take another screenshot 5. Repeat That works, but it's slow, expensive in tokens, and fragile. If the UI shifts a few pixels, things break. And the model still doesn't know what any element actually is. But the…
May 2026 · github.com
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Hi HN! Smooth CLI (https://www.smooth.sh) is a browser that agents like Claude Code can use to navigate the web reliably, quickly, and affordably. It lets agents specify tasks using natural language, hiding UI complexity, and allowing them to focus on higher-level intents to carry out complex web tasks. It can also use your IP address while running browsers in the cloud, which helps a lot with roadblocks like captchas (https://docs.smooth.sh/features/use-my-ip). Here’s a demo: https://www.youtube.com/watch?v=62jthcU705k Docs start at…
Feb 2026 · docs.smooth.sh
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Our team lives in Slack, but we don’t have access to the Slack MCP and couldn’t find anything out there that worked for us, so we coded our own agent-slack CLI * Can paste in Slack URLs * Token efficient * Zero-config (auto auth if you use Slack Desktop) Auto downloads files/snippets. Also can read Slack canvases as markdown! MIT License
Feb 2026 · github.com
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TLDR; I built a tool that turns any API into a CLI designed for ai agents --- Got tired of dealing with bloated context windows from MCP servers and skills that stuff entire API docs into the agent's context CLIs fix this, agents run a single command to self-discover everything an API has to offer So, built a tool to generate them for any api. All CLIs are written in Go, fast and lightweight, no dependencies Help text (via the --help flag) is the killer feature: all context for each command/endpoint/parameter is extracted directly from the user-facing API docs and enhanced with…
Mar 2026 · instantcli.com
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Hi HN. I'm the founder of Phoenix Labs (ex TikTok, Applied AI) and we're open sourcing our internal tooling today which is like a toolchain / meta-harness for CLI agents useful for really scaling eng and creative work. We are a very small team who's building a very ambitious product so we had to find ways to squeeze every ounce of efficiency that we could get our hands on. Harness strengths of different models (Claude, GPTs) and CLI-harnesses (Claude Code, Codex), safe/robust browser integration to speed up UX/QA testing, teams cli to speed up security reviews and parallelize…
May 2026 · agents-cli.sh
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That's a bold claim. But I genuinely feel like I might have actually solved computer use (demo: https://x.com/mdlahfir/status/2088109763783700827?s=20) For context, I've been building agent-desktop (Inspired by agent-browser by Vercel Labs), an automation CLI for desktop apps. It's like Playwright but for desktops, not just native, but for Chromium apps as well. Trust me, yes, Chromium apps whose accessibility tree is dense. MacOS is GA; I'm almost close to launching for Windows and Linux! So, how did I solve it? Basically interoperability. The biggest issue with…
23d ago · github.com
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Is your API ready for AI agents? Get an AI readiness score
Jun 2026 · github.com
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Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
Mar 2026 · github.com
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