Alternatives
Products that do what Sigil – compile a SKILL.md the model MUST run, not "might" does
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- 2HT
2017 · blog.klipse.tech
- 3RR
I built a single-file Python script that lets you run LLM prompts from the command line with templating, structured outputs, and the ability to chain prompts together. When I discovered Google's Dotprompt format (frontmatter + Handlebars templates), I realized it was perfect for something I'd been wanting: treating prompts as first-class programs you can pipe together Unix-style. Google uses Dotprompt in Firebase Genkit and I wanted something simpler - just run a .prompt file directly on the command line. Here's what it looks like: --- model: anthropic/claude-sonnet-4-20250514 output:…
Nov 2025 · github.com
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- 5IM
At my work they provided a single Claude subscription for everyone on the team. To be honest I like kiro better as it provides a way better SDD management. But the company can't provide it and I can't afford it yet. Turns out I had the skill creator skill in my claude instance so I made use of it to create this Skill. I made it fully by using Claude but I wanted to make it open source, so I asked it to help me make tests and preparations for it, even a CI to run python tests. Well, we got this results with it: - Phase 2A: 67 static assertions (Python script, runs in CI) - Phase 2B: 15…
May 2026 · github.com
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- 7GS
I created this tool after writing a few agent skills by hand and noticing this pattern was repetitive. Paste a documentation URL, enter your own model API key, and it gets the page content client-side to create a reusable SKILL.md. There is no backend/proxy, so it stays as secure as possible. I would like feedback on the structure of the output and the edge cases.
May 2026 · getskillify.dev
- 8CI
One of the most frequent questions one faces while running LLMs locally is: I have xx RAM and yy GPU, Can I run zz LLM model ? I have vibe coded a simple application to help you with just that. Update: A lot of great feedback for me to improve the app. Thank you all.
2025 · can-i-run-this-llm-blue.vercel.app
- 9MT
Creating high-quality skills for Claude manually is complex, requiring specific technical knowledge of the file system structure (like SKILL.md), YAML metadata configuration, and precise prompt engineering to ensure the agent behaves correctly. MakeSkill eliminates this friction by automating the technical implementation. Instead of writing code and configuration files from scratch, users interact with MakeSkill's AI to refine their ideas. The platform then generates the complete, optimized skill package—following all best practices—ready to be downloaded and imported directly into Claude…
Nov 2025 · makeskill.cc
- 10CE
We built ProgramAsWeights (PAW) — https://programasweights.com You describe a function in English — like "classify if this message is urgent" — and PAW compiles it into a tiny neural program (22 MB) that runs locally like a normal Python function. No API keys, no internet after compilation, deterministic output. It's for tasks that are easy to describe but hard to code with rules: urgency triage, JSON repair, log filtering, tool routing for agents. pip install programasweights import programasweights as paw f = paw.compile_and_load("Classify if this is urgent or not.") f("Need your…
Apr 2026
- 11LW
Apr 2026 · github.com
- 12SA
I've been working on a new programming language for AI agents. I would love your input on what makes programming languages good for AI agents, especially syntax, compiler, and tooling that could help AI agents write code. What makes Sigil good for coding agents? I've turned conventions into compiler rules whenever possible. The compiler owns the canonical printer and every AST has one accepted textual representation. For almost every syntax feature I tried to save tokens. Order and naming conventions are enforced. No more "I think this argument is important so it should come first." Most…
Apr 2026
- 13AC
Jun 2026 · github.com
- 14KL
LLM agents often place raw JSON tool outputs directly in the prompt. After a few tool calls, earlier results get compacted or truncated and answers become incorrect or inconsistent. I built Sift, a drop-in MCP gateway that stores tool outputs as local artifacts (filesystem blobs indexed in SQLite) and returns an `artifact_id` plus compact schema hints when responses are large or paginated. Instead of reasoning over full JSON in the prompt, the model runs a small Python query: def run(data, schema, params): return max(data, key=lambda x: x["magnitude"])["place"] Query code runs in a…
Mar 2026 · github.com
- 15RL
I've been looking for a way to run LLMs safely without needing to approve every command. There are plenty of projects out there that run the agent in docker, but they don't always contain the dependencies that I need. Then it struck me. I already define project dependencies with mise. What if we could build a container on the fly for any project by reading the mise config? I've been using agent-en-place for a couple of weeks now, and it's working great! I'd love to hear what y'all think
Jan 2026 · github.com
- 16TO
2025 · github.com
- 17OR
Oct 2025 · github.com
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- 19LR
I built localLLLM: a small community project for running local models. Live: https://locallllm.fly.dev The goal is simple: if someone has model + OS + GPU + RAM, they should get steps that actually work (ideally one liner) I need help populating and validating guides. If you run local models, please submit one working recipe (or report what failed). Would love to hear general feedback as well!
Apr 2026 · locallllm.fly.dev
- 20RA
Hey all! I'm the maintainer of mistral.rs. I just landed support for OpenAI-compatible Agent Skills via a /v1/skills endpoint, and it works with local open models. Until now Skills have basically been locked to closed models, and with the ability to have private, local intelligence becoming increasingly important, but this feature allows you to do XYZ with local models. It's fully compatible with OpenAI's /v1/skills API, so you can drop mistral.rs into your existing code with minimal difficulty. We support the accompanying tools too: /v1/files or input_file for…
Jun 2026
- 21IM
Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results. High-Level Concept Flow Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps And the bast part: Every step can be saved and reused as…
2025 · github.com
- 22IV
I wanted to evaluate C# source generators to see whether "compile time PetaPoco like" project can be built with it.
2024 · github.com
- 23IB
For the last 6 months, I've been building ORUS Builder, an open-source AI code generator. My goal was to fix the biggest issue I have with tools like v0, Lovable, etc. – they generate broken, non-compiling code that needs hours of debugging. ORUS Builder is different. It uses a "Compiler-Integrity Generation" (CIG) protocol, a set of cognitive validation steps that run before the code is generated. The result is a 99.9% first-time compilation success rate in my tests. The workflow is simple: 1.Describe an app in a single prompt. 2.It generates a full-stack application…
Nov 2025
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