Alternatives
Products that do what ctxlint does
Linter for AI agent context files (AGENTS.md, CLAUDE.md)
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2025 · github.com
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Recently several AI labs have published experiments where they tried to get AI coding agents to complete large software projects. - Cursor attempted to make a browser from scratch: https://cursor.com/blog/scaling-agents - Anthropic attempted to make a C Compiler: https://www.anthropic.com/engineering/building-c-compiler I have been wondering if there are software packages that can be easily reproduced by taking the available test suites and tasking agents to work on projects until the existing test suites pass. After playing with this concept by having…
Feb 2026 · github.com
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ctx is a local SQLite-backed skill for Claude Code and Codex that stores context as a persistent workstream that can be continued across agent sessions. Each workstream can contain multiple sessions, notes, decisions, todos, and resume packs. It essentially functions as a /resume that can work across coding agents. Here is a video of how it works: https://www.loom.com/share/5e558204885e4264a34d2cf6bd488117 I initially built ctx because I wanted to try a workstream that I started on Claude and continue it from Codex. Since then, I’ve added a few quality of life…
Apr 2026 · github.com
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Attach reference projects for AI coding tools
Apr 2026 · marketplace.visualstudio.com
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Coding agents don't have long-term memory. But you do have months of full-fidelity agent transcripts stored on your machine. A simple solution that goes a long way: ingest those transcripts and logs into a structured SQLite database, then search them with ranked text match. Everything is fully local and doesn't require anything fancy like a graph database or hosted memory service. This is the idea behind ctx, a Rust CLI that handles the ingestion and searching. We give our agents a skill that tells them to reference past sessions before working in an area. Usually we do this through an…
Jul 2026 · github.com
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Hi HN, Zidan here. I’ve been experimenting with AI-assisted debugging and noticed a recurring gap: most tools optimize for agent-led exploration (ex: giving claude code a browser to click around and try to reproduce an issue). But in many cases, I've already found the bug myself. What I actually want is a way to hand the agent the exact context I just saw - without retyping steps, copying logs, or hoping it can reproduce the behavior. So we built FlowLens, an open-source MCP server + Chrome extension that captures browser context and lets coding agents inspect it as structured, queryable…
Nov 2025 · github.com
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Hey HN, Gabe and Alexander here from Hatchet. Today we're releasing Pickaxe, a Typescript library to build AI agents which are scalable and fault-tolerant. Here's a demo: https://github.com/user-attachments/assets/b28fc406-f501-442... Pickaxe provides a simple set of primitives for building agents which can automatically checkpoint their state and suspend or resume processing (also known as durable execution) while waiting for external events (like a human in the loop). The library is based on common patterns we've seen when helping Hatchet users run millions of…
2025 · github.com
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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
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The context layer between your files and your AI Agents
Jun 2026 · ctxd.dev
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npx agentseed init AGENTS.md (https://agents.md) is a standard file used by AI coding agents to understand a repo (stack, commands, conventions). Agentseed generates it directly from the codebase using static analysis. Optional LLM augmentation is supported by bringing your own API key. Extracts languages, frameworks, dependencies, build/test commands, directory structure, and monorepo boundaries.
Feb 2026 · github.com
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AI coding assistants are productive but sloppy. They produce code that looks right but: - Imports packages that don't exist - Uses placeholder functions that do nothing - Leaks patterns from JavaScript, Java, Ruby into Python - Leaves behind dead code and duplicates - Uses mutable default arguments I built sloppylint to catch these "AI slop" patterns before they hit production. pip install sloppylint sloppylint . It detects 100+ patterns across categories: - Hallucinated imports (20% of AI imports reference non-existent packages) - Placeholder code (`pass`, `...`, `TODO`) - Wrong-language…
Dec 2025 · github.com
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