Borrowing it
Nothing to install: this file belongs to LegalQuants/lq-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/LegalQuants/lq-ai/main/CLAUDE.mdgit clone --depth 1 https://github.com/LegalQuants/lq-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/legalquants/lq-ai/claude-md)<a href="https://agentmods.dev/instructions/legalquants/lq-ai/claude-md"><img src="https://agentmods.dev/badge/instructions/legalquants/lq-ai/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/legalquants/lq-ai/claude-md"><img src="https://agentmods.dev/badge/instructions/legalquants/lq-ai/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04085 | $0.04085 |
| Opus 5.5 | $0.01634 | $0.01634 |
| Sonnet 5.5 | $0.00817 | $0.00817 |
| Haiku 4.5 | $0.00409 | $0.00409 |
Grade A, and why
lq-ai CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orientation for Claude Code (and other coding assistants)
Purpose: Ground orientation for any agentic coding assistant working on the LQ.AI codebase. Read this first; it points at the right reference for any decision and lays out the project's standards in one place.
Audience: Claude Code, Cursor, Aider, or any human or agent making implementation decisions. Read in full before the first contribution; refer back as needed.
New here? Start with the cold-start guide for coding agents — it gives you the read-order, the build loop, the dev-environment hard rules, and how to take a roadmap item to a merged PR. Then keep this file open as your decision reference.
What this project is
LQ.AI is an open-source AI platform for in-house legal teams. Self-hosted; bring-your-own-keys; runs in the operator's environment. Skills are open-source work product, not closed prompts. The Inference Gateway is the security boundary — the only component holding privileged provider API keys.
The project's reason for existing — and its central design constraint — is transparency. Every artifact that shapes the user experience is visible work product. A skill that produces a wrong answer should be readable, debuggable, and forkable by the user who relies on it. This is not a marketing principle; it is an architectural commitment that affects every implementation decision.
Read README.md for the public-facing description. Read docs/PRD.md §1.3 Transparency as a Founding Principle for the full philosophical grounding.
Decision routing
When you face a decision while implementing, the canonical reference is — in priority order:
- The PRD (docs/PRD.md) — for product, capability, and architectural decisions.
- The OpenAPI sketches (docs/api/backend-openapi.yaml, docs/api/gateway-openapi.yaml) — for endpoint shapes, request/response schemas, status codes.
- The database schema (docs/db-schema.md) — for tables, columns, indexes, constraints.
- The gateway configuration example (gateway.yaml.example) — for the gateway's configuration shape.
- The implementation order (docs/M1-IMPLEMENTATION-ORDER.md) — for which task is next and what its acceptance criteria are.
- The skill-authoring guide (docs/skill-authoring-guide.md) — for skill conventions.
- CONTRIBUTING.md (CONTRIBUTING.md) — for code style, testing, PR process.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +80 tokens per session f626bfdd4c36
- 6d ago Changed · +76 tokens per session 7d539a609ccc
- 17d ago Changed · +1 lines · +112 tokens per session ea9621e55efa
- 1mo ago Changed · +8 tokens per session 42d8064611fe
- 1mo ago Changed · +15 tokens per session 55224e74404e
- 1mo ago First seen · 283 lines · 3,794 tokens per session scan A d64b866a353c
lq-ai CLAUDE.md is an instructions file published in the GitHub repository LegalQuants/lq-ai (156 stars, last pushed today), licensed Apache-2.0. It adds 4,085 tokens to every session, about $0.0163 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deno copilot-instructions.md
Copilot instructions for denoland/deno, covering deno development guide for github copilot, network access, high level overview, key directories and key files to understand first.