Reqord

What it does

Reqord stores requirements as structured YAML + Markdown inside your Git repo — version-controlled alongside your code. No SaaS, no backend needed. It enforces traceability: Requirement → Specification → GitHub Issue, with a feedback loop flowing insights back upstream. • SMART quality scoring for requirements • PR-based approval workflow • AI-ready context across sessions • Web dashboard & Claude Code plugin

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  Relace Repos2025 · relace.ai · ▲294

  GitHub for Agents

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  I'm Creating a GitHub Repo Quality Bot2018 · github.com · ▲58

  :cop: GitHub Bot For Validating Pull Request Mergability Requirements and Issues Structure - Schachte/Git-Enforcer

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  A Karpathy-style LLM wiki your agents maintain (Markdown and Git)Apr 2026 · github.com · ▲260

  I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…

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  Git for AI AgentsMay 2026 · github.com · ▲129

  hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like "why did you do it?", "when did u delete this folder? why?", etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same core capabilities with ai agents so... i developed an open source solution for that (currently supporting claude code) would love to…

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  GitHub-assistant – Natural language questions from your GitHub data2024 · github.com · ▲49

  Simon(sfarshid) and I spend a lot of time on GitHub. As data nerds we put together a quick tool to explore your repository’s data. How it works: - Data Loading: We use dlt to pull data (issues, PRs, commits, stars) from GitHub - Semantic Layer: Relta wraps the underlying dataset into a semantic layer so the LLM doesn’t hallucinate. - Text-to-SQL: A text-to-SQL agent transforms your plain-English question into a query using the semantic layer - Generative Charts: assistant-ui dynamically generates a chart based on the SQL query - Refinements: If the semantic layer can’t handle your question,…

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  Continue – Source-controlled AI checks, enforceable in CIFeb 2026 · docs.continue.dev · ▲44

  We now write most of our code with agents. For a while, PRs piled up, causing review fatigue, and we had this sinking feeling that standards were slipping. Consistency is tough at this volume. I’m sharing the solution we found, which has become our main product. Continue (https://docs.continue.dev) runs AI checks on every PR. Each check is a source-controlled markdown file in `.continue/checks/` that shows up as a GitHub status check. They run as full agents, not just reading the diff, but able to read/write files, run bash commands, and use a browser. If it finds…

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  I spent 3 years reverse-engineering a 40 yo stock market sim from 1986▲721

  Hello my name is Ben Ward for the past 3 years I have been remastering the financial game Wall Street Raider created by Michael Jenkins originally on DOS in 1986. It has been a rough journey but I finally see the light at the end of the tunnel. I just recently redid the website and thought maybe the full story of how this project came to be would interest you all. Thank you for reading.

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