Alternatives
Products that do what AI Alignment Skill does
Ensure alignment when working with frontier AI models.
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- 2MA
I've been deeply involved in working with AI agents and large language models (LLMs) for a while now. During a recent job search, I found myself repeatedly explaining my skills and experiences to various assistants. Around the same time, I was creating content for my website to help hiring teams understand my capabilities better and make informed decisions. MCP had started to gain momentum and I saw a way to reduce my toil. So I built an MCP server that can effectively communicate my qualifications as a job candidate. This server acts as an AI-powered resume, providing an understanding of my…
2025 · ai.jakegaylor.com
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- 4CY
Hello HN, We built Promptrepo to make finetuning accessible to product teams — not just ML engineers. Last week, OpenAI’s CPO shared how they use fine-tuning for everything from customer support to deep research, and called it the future for serious AI teams. Yet most teams I know still rely on prompting, because fine-tuning is too technical, while the people who have the training data (product managers and domain experts) are often non-technical. With Promptrepo, they can now: - Add training examples in Google Sheets - Click a button to train - Deploy and test instantly - Use OpenAI,…
2025 · promptrepo.com
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- 7AA
2023 · alignapi.com
- 8FG
We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com
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- 12LA
Hey Hacker News! I've been working on an open-source project called LLM Alignment Template, a comprehensive toolkit designed to help researchers, developers, and data scientists align large language models (LLMs) with human values using Reinforcement Learning from Human Feedback (RLHF). What the project does: Interactive Web Interface: Easily train models, visualize alignment metrics, and manage alignment with an accessible UI. Training with RLHF: Align models effectively to human preferences using feedback loops. Explainability: Built-in dashboards to help understand model behavior using…
2024 · github.com
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- 15IM
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
- 16SM
I work as an engineer at the Dutch government. We have hundreds of technical standards that developers should follow when building government software: API design rules, messaging protocols, authentication profiles, accessibility requirements. The problem is that most developers don't know these standards exist until someone reviews their code (if at all). Skills are Markdown files that inject domain knowledge into AI coding tools. When a developer starts building an API, the tool automatically loads the relevant standard. No plugins to write, no code. Just structured knowledge in Markdown.…
Feb 2026 · anneschuth.nl
- 17OS
Hi HN, Matvey, Ildar, Joey, and Dominik here. If you're building LLM agents that use tools, you're probably worried about prompt injection attacks that can hijack those tools. We were too, and found that solutions like prompt-based filtering or secondary "guard" LLMs can be unreliable. Our thesis is that agent security should be handled at the network level between the agent and the LLM, just like a traditional web application firewall. So we built Archestra Platform: an open-source gateway that acts as a secure proxy for your AI agents. It's designed to be a deterministic firewall against…
Oct 2025 · archestra.ai
- 18CL
We're excited to launch compliant-llm: an open-source toolkit that helps infosec and compliance teams audit AI agents against regulatory frameworks like NIST AI RMF, ISO 42001, and OWASP Top 10. Infosec and compliance teams are now responsible for tracking security and compliance risks of a growing number of AI agents across external and internal apps and third-party vendors. compliant-llm gives you a way to: - Define and run comprehensive red-teaming tests for AI agents - Maps test outcomes to compliance frameworks like NIST AI RMF - Generate detailed audit logs and documentation -…
2025 · github.com
- 19LC
Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard. Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement. Three modes: Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc One line to integrate: npm…
Feb 2026 · limits.dev
- 20AP
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…
Feb 2026 · tessl.io
- 21AS
I made a strategy game where you play the US or China through the AI race, 2026 to 2030, sixteen quarterly turns in the browser. One run takes about half an hour. At the start, the game seals two dice you never get to see. Inside: how hard alignment really is, and how fast takeoff compounds. You get eval reports, but only as ranges, and they flatter you most exactly when your systems are least aligned. At the end you get a debrief which shows what your evals said each quarter and also what was actually true. I lost every run I played myself so far. Every number in the game is source-backed…
Jul 2026 · criticalwindow.org
- 22AA
AStack is a composable framework designed to simplify the development of AI applications through a "everything is a component" philosophy. It provides a zero-adaptation layer design that enables seamless integration between various AI models, tools, and custom business logic. AStack is an independent technical framework with its own architecture and ecosystem, built on top of Hlang - a highly semantic fourth-generation language (4GL) inspired by Flow-Based Programming paradigms. This foundation on Hlang, which is particularly well-suited for computational modeling and AI-generated code, is…
2025 · github.com
- 23DA
Hey HN! Built a template for distributing AI agent skills via npm. Problem: AI coding tools (Claude Code, Cursor, Windsurf) support custom "skills" but distribution is manual - copy files to ~/.tool/skills/, repeat for updates, no versioning. Solution: Standard npm packages with lifecycle hooks: npm install -g @org/my-skill npm update -g @org/my-skill Benefits: - Semantic versioning (^1.2.0 for auto-updates) - Private registries for enterprise - Standard CI/CD integration - Discoverable on npmjs.com It's a GitHub template - fork, customize SKILL.md, publish.…
Jan 2026 · github.com
- 24CM
Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…
Mar 2026 · github.com
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