Alternatives
Products that do what A package manager for agent skills with built-in evals does
I'm Guy, the founder behind Snyk — now building Tessl, a package manager for agent skills. We’ve recently witnessed that most teams still treat skills as static artifacts: markdown files, created or copied from repo to repo. This approach offers a strong initial boost, but quickly creates debt: - Skills are duplicated, and updates never roll out. - Poor quality skills go unseen, misguiding agents instead of helping. - Skill knowledge grows stale, and don’t keep up with the systems and practices they describe. Without a way to evaluate skills, teams have no clear way to understand how good a…
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May 2026 · github.com
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May 2026 · github.com
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Hi HN! We're Giacomo and Roberto, authors of Ratel (https://github.com/ratel-ai/ratel) We used to help SaaS companies build agents on top of their products. Whenever we wanted to expand the agents’ complexity/scope, by adding more and more tools and instructions, we always run in the same issue: context bloat, with frequent hallucinations and sky high token bills. So we started constantly engineering the agents, dynamically loading tools, splitting them into subagents, inventing our own way to support skills And that's exactly when we started building Ratel: a…
Jul 2026 · github.com
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Hello HN, I’ve been building AI agents lately and ran into a common "Context Bloat" problem. When an agent has 20+ skills, stuffing every system prompt, reference doc, and tool definition into a single request quickly hits token limits and degrades model performance (the "lost in the middle" problem). To solve this, I built OpenSkills, an open-source SDK that implements a Progressive Disclosure Architecture for agent skills. The Core Concept: Instead of loading everything upfront, OpenSkills splits a skill into three layers: Layer 1 (Metadata): Light-weight tags and triggers (always loaded…
Jan 2026
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I’ve spent the past 10 years working on AI in finance, with much of that time focused on building evaluation systems for production environments. As agents become more widely adopted, more software engineering and product people have start building them. But I’ve noticed that many teams are not yet fluent in systematic evaluation, or in the processes needed to keep agent quality high over time. For large organizations, that gap is rarely the bottleneck due to dedicated teams. But after speaking with a number of startups, it became clear that building strong, up-to-date evals is much harder…
May 2026 · github.com
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I'm an "ideas person" who messes around with AI on a low budget. I got tired of watching my tokens vanish and context windows filling up while agents fumbled around trying to find the right thing. Agents don't flail like they used to with shell tools, but there are still weak/blind spots and back-and-forth episodes — especially when using tools in combination/sequence. So I built "tilth" today. Or rather, AI built it — every line is Opus 4.6. I spent a lot of my precious tokens getting it to "not shit" (at least several of the different vendors' AI overlords assure me it's not…
Feb 2026 · github.com
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I built a collection of webhook skills because AI coding agents are surprisingly bad at webhook integrations. The generated code looks reasonable until you run it, then signature verification fails, raw body handling is wrong, or the middleware order breaks everything. PostHog's research on LLM code generation (https://posthog.com/blog/correct-llm-code-generation) found that agents produce more reliable code when referencing known-working examples rather than reconstructing from training data. That's the approach here. `webhook-skills` is a collection of provider-specific…
Feb 2026 · github.com
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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
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I'm one of the creators of The Edge Agent (TEA). We built this because we needed a way to deploy agents that was verifiable and robust enough for production/edge cases, moving away from loose scripts. The architecture aims to solve critical gaps in deterministic orchestration identified by *Prof. Claudionor Coelho Jr. (Stanford alum, ML/DL Faculty at Santa Clara Univ., and Senior Fellow for AI at Majestic Labs)* during our work on the Kiroku project. *Key Technical Features:* * *Neurosymbolic Native:* We integrated Prolog to logically validate LLM outputs. This combines neural…
Jan 2026 · fabceolin.github.io
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Was so sick of reading walls of codex/claude prose, in markdown plans and just in the chat, that I started playing with ideas that force coding agents to display information differently. The human visual cortex is an amazing thing, and getting coding agents to let me use it has been pretty nice so far. Been iterating on this a lot internally for the last few months, polishing and mostly removing stuff.
25d ago · humanlayer.com
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I built this because I was tired of creating pull requests in 20 repositories just to change a single line of workflow job version. With Infra as AI, just mention the change. Agents work on all repos in parallel, read the docs, make a bunch of PRs and fill in the description. You can see the demo of the actual dashboard in the landing. Let me know your thoughts :) It means a lot to me!
Sep 2025 · infrastructureas.ai
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10 evidence-first workflows for AI coding agents
Jul 2026 · ai-agent-skills-pack.vercel.app
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Hi HN — I'm Scott. Skillscript is a small language I built to write what I want my local agent to actually do, in a form I can read and version, instead of hoping the model gets it right each time. The itch started with something small. I wanted my NanoClaw agent to run my morning brief the same way every day. Check overnight tickets, summarize the deploy pipeline, flag anything urgent. Every session, it would re-figure out how to do this from scratch, drift a little, and cost tokens for what's basically a fixed procedure. I could put it in a system prompt or an MD skill file, but those are…
Jul 2026 · github.com
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Coding agent management is all the rage right now, and many tools are being created to fill the gap. As a power user for all tools I've used since I've started my software engineering career, I've always taken the time to test multiple tools thoroughly before deciding on one, and an agentic manager was no different. I've tested many tools, but ultimately landed on Agent of Empires (AoE for short). Why ? Because it's fast, the development is active and it's feature complete, and easy to contribute to. So I did (contribute). In my day to day workflow for my job, I need the ability to start…
May 2026 · github.com
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We built meta-agent: an open-source library that automatically and continuously improves agent harnesses from production traces. Point it at an existing agent, a stream of unlabeled production traces, and a small labeled holdout set. An LLM judge scores unlabeled production traces as they stream. A proposer reads failed traces and writes one targeted harness update at a time, such as changes to prompts, hooks, tools, or subagents. The update is kept only if it improves holdout accuracy. On tau-bench v3 airline, meta-agent improved holdout accuracy from 67% to 87%. We open-sourced meta-agent.…
Apr 2026 · github.com
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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
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Hi HN! Sean from MindStudio here. I wanted to share something we've been working on that I think introduces some new ideas into the "AI coding agent" space. Remy is an AI agent that builds full-stack TypeScript apps from a spec written in a new flavor of annotated markdown. The spec has two layers: prose describing what the app does, and annotations that carry the technical precision (data types, edge cases, validation rules, code snippets). The agent then "compiles" this into code: backend methods, typed schemas, frontends, test scenarios, and everything else are derived artifacts of the…
Apr 2026 · remy.msagent.ai
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