Skip to content

lid

Linked-Intent Development - a SDD methodology for agentic coding

Install

$/plugin marketplace add jszmajda/lid

Install command taken from the project's README or registry entry. Always review code before running it.

Linked-Intent Development

A structured design-before-code methodology for agentic coding. Stop building the wrong thing — get alignment on what before writing how. Works with any coding agent that reads per-project instructions.

Quickstart

Using Claude Code? Install the plugins — richest integration, auto-invoking skills, slash commands:

/plugin marketplace add jszmajda/lid
/plugin install linked-intent-dev@jszmajda-lid
/plugin install arrow-maintenance@jszmajda-lid
/linked-intent-dev

Optional: the lid-experimental plugin adds opt-in skills that aren't part of the core workflow but layer on top of it. Currently includes bidirectional-differential for auditing EARS↔code coherence on a chosen scope. See plugins/lid-experimental/README.md for what's in it and how to use it. Install separately:

/plugin install lid-experimental@jszmajda-lid

Using Cursor, Windsurf, GitHub Copilot, Aider, Continue, Junie, Codex, Zed, or other tools? Drop a small rule file into your project that points the agent at an AGENTS.md. Copy-paste snippets for each tool are in docs/setup.md.

Then just describe what you want to build. The methodology handles the rest. For existing codebases, see Getting Started: Brownfield Project.

LID uses LID on itself. This repo is the canonical mature-project example — its own HLD, LLDs, EARS specs, and evals are in docs/ and plugins/*/skills/*/evals/. If you want to see what LID-at-rest looks like on a real codebase, read the docs here. For a small, intent-only demo (HLD + LLDs + specs, no code — meant to be given to an agent to regenerate), see examples/urlshort/.

Built on LID. Editor plugins, CLIs, CI checkers, and more come from a growing third-party ecosystem — the minimal core leaves that tooling to others. Browse what people have built in EXTENSIONS.md or via the linked-intent-development topic; built something? Add it.

The Problem

Modern AI coding agents don't really write bugs anymore. What they write are intent gaps — places where the agent assumed you meant something different than you did. The biggest challenge in agentic development isn't getting code to work; it's making sure the agent builds the right thing.

This gets worse as systems grow. Every new session starts a capable but amnesiac programmer from scratch. Context windows help, but there's always a gap between what's in your head and what's in the agent's context. That gap is where intent drifts, and intent drift is where projects go sideways.

The Arrow of Intent

The solution is to make intent explicit and traceable. There's a chain of documents that translates what you want into working software:

HLD → LLDs → EARS → Tests → Code

Each level translates the previous into more specific terms. They aren't independent documents — they're a single expression of intent at different levels of specificity. When you change your mind about something, the change flows downstream through the chain: update the design, then the specs, then the tests, then the code. Never the reverse.

Similar plugs

Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop, mobile and remote runtime.

CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies

Documented system prompts from Anthropic - Claude Fable 5.1, Opus 5.5, Claude Design, Claude Code. OpenAI - ChatGPT GPT-6-Astra, Codex. Google - Gemini 3.8 Flash, 3.1 Pro,…

Wrap Antigravity, ChatGPT Codex, Claude Code, Grok Build, Muse Code, Devin as an OpenAI/Gemini/Claude/Codex compatible API service, allowing you to enjoy the free Gemini…

Explore related