What is coderadius AGENTS.md?

Instructions for AI coding agents working on CodeRadius, a command-line tool that maps architecture and dependencies across codebases. It covers the project’s commands, runtime, dashboard, tests, and graph-database integrations.

Use it to install dependencies, compile the project, run the CLI or dashboard, execute unit and integration tests, and run evaluation tests.

It gives agents the repository-specific rules needed to build, run, test, and change the project correctly.

How coderadius-ai/coderadius configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to coderadius-ai/coderadius. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/coderadius-ai/coderadius/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/coderadius-ai/coderadius

Made for: Codex, OpenCode.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/coderadius-ai/coderadius/agents-md/github.svg)](https://agentmods.dev/instructions/coderadius-ai/coderadius/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/coderadius-ai/coderadius/agents-md"><img src="https://agentmods.dev/badge/instructions/coderadius-ai/coderadius/agents-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.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/coderadius-ai/coderadius/agents-md"><img src="https://agentmods.dev/badge/instructions/coderadius-ai/coderadius/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 5,788 This file is loaded in full into every session.

When invoked 5,788 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.05788	$0.05788
Opus 5.5	$0.02315	$0.02315
Sonnet 5.5	$0.01158	$0.01158
Haiku 4.5	$0.00579	$0.00579

Measured 2mo ago against content hash a35114300a55, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

coderadius AGENTS.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 2mo ago.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

coderadius CLAUDE.md — 98% identical, 4 lines differ

AGENTS.md · 263 lines

How it starts

The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

This file provides guidance to AI coding agents working with code in this repository.

What This Is

CodeRadius is a CLI tool that builds an architectural knowledge graph from polyglot codebases. It statically analyzes source code (TypeScript, PHP, Go, Python), extracts infrastructure dependencies via LLM-driven semantic analysis, persists everything to a Neo4j/Memgraph graph database, and enables cross-repo impact analysis ("blast radius") and governance policy enforcement.

Build & Run Commands

Runtime: Bun (not Node.js for development). The project uses ES modules ("type": "module").

bun install                # Install dependencies
bun run build              # TypeScript compilation (tsc, noEmit)
bun run dev                # Run CLI directly: bun run src/cli/index.ts
bun run dev:dashboard      # Dashboard dev server with live reload (http://localhost:3456)

Testing

bun run test:unit                                    # All unit tests
bun vitest run tests/unit/path/to/file.test.ts       # Single unit test
bun run test:integration                             # Integration tests (requires running Memgraph)
make test-eval-golden      # Eval tests (LLM golden + replay-cached patterns)
make test-patterns         # Pattern fixtures, deterministic subset (no LLM, no DB)

The repo organises tests by determinism level and dependency footprint. Pick the right tier when adding a feature or fixing a bug:

Unit tests (tests/unit/): pure logic, no external services. Defaults for sanitizer rules, schema validation, regex guards, in-memory pipelines. Run: bun run test:unit (~7s).
Integration tests (tests/integration/): exercise real graph mutations against Memgraph. Required for any change to src/graph/mutations/, the welder (dynamic-infra-resolver.ts), or DB-backed pipelines. Run sequentially (no file parallelism). Run: bun run test:integration (~20s).
Eval tests — agents (tests/eval/agents/): LLM extraction quality on per-function snippets, replay-cached. Use when a fix changes prompt rules, sanitizer behaviour visible to the LLM, or the LLM output schema. Three modes via EVAL_LLM_MODE:
- replay (default): cached LLM outputs from tests/eval/.llm-cache/, ~2s, deterministic
- live: real LLM calls, saves to cache
- refresh: real LLM calls, overwrites cache

Read the full file on GitHub · 263 lines

Changes

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.

2mo ago First seen · 263 lines · 5,788 tokens per session scan A a35114300a55

Subscribe to this mod's changes

coderadius AGENTS.md is an instructions file published in the GitHub repository coderadius-ai/coderadius (23 stars, last pushed 22d ago), licensed Apache-2.0. It adds 5,788 tokens to every session, about $0.0232 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.

architecture blast-radius bun cli developer-tools governance

coderadius AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
coderadius AGENTS.mdcoderadius-ai/coderadius	Instructions file	23	22d ago	Apache-2.0	A	5,788	Codex, OpenCode
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

coderadius: Instructions file for Codex