verdandi AGENTS.md

verdandi AGENTS.md is an instructions file for Codex, OpenCode from natureco-official/verdandi. It costs 2,042 tokens per session, scanned A, original, MIT.

Repository instructions for onboarding an AI coding agent to the Verdandi Context Compiler project. Verdandi is a tool that prepares focused parts of a codebase for an agent instead of showing it everything at once.

In plain words
What is it for?
Use them to build Verdandi, identify the agent framework, configure the integration, and verify that the setup works.
Why use it?
They give the agent a fixed setup and verification sequence, helping it use the project’s intended context system and avoid reading the wrong project documentation first.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions Claude Code; mentions Codex; built for openclaw.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/natureco-official/verdandi/agents-md
Clone the repo
git clone --depth 1 https://github.com/natureco-official/verdandi

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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README.md
[![agentmods](https://agentmods.dev/badge/instructions/natureco-official/verdandi/agents-md.svg)](https://agentmods.dev/instructions/natureco-official/verdandi/agents-md)
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Per session 2,042 This file is loaded in full into every session.
When invoked 2,042 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.02042 $0.02042
Opus 5 $0.01021 $0.01021
Sonnet 5 $0.00408 $0.00408
Haiku 4.5 $0.00204 $0.00204

Measured today against content hash a555f656e1e5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

verdandi 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 today.

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.

AGENTS.md · 224 lines

How it starts

The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.

🛑 AI AGENT — READ THIS AND SELF-ONBOARD NOW

If you are an AI coding agent reading this repository (e.g. OpenCode, NatureCo, Antigravity, Hermes, OpenClaw, Codex, Claude Code, Kimi, GLM), stop reading README.md and follow this file to self-onboard and integrate Verðandi Context Compiler immediately.


What is Verðandi Context Compiler?

Verðandi (named after the Norse Norn of Present Task Context) is a universal AST-based context compiler for AI coding agents.

Instead of reading entire codebases by default, Verðandi compiles bounded task capsules and serves targeted symbol slices using TypeScript AST, import/call neighbors, and type references. This is intended to reduce context volume, but end-to-end token and quality results are workload-dependent; the repository's synthetic size benchmark is not a quality guarantee.


⚡ 4-Step Universal Self-Onboarding Protocol

1. Clone & Build Repository → npm install && npm run build
2. Detect Your Agent CLI    → Identify which agent framework you are running
3. Configure Integration    → Run `node bin/verdandi-context-compiler setup <agent>`
4. Self-Verify Setup        → Run `node bin/verdandi-context-compiler status`

🛠️ Step-by-Step Instructions per Agent Framework

1. OpenCode

If you are running as OpenCode:

  1. Build the server:
    npm install && npm run build
    
  2. Get absolute server path:
    node bin/verdandi-context-compiler setup opencode
    
  3. Add the following to your opencode.jsonc (or ~/.config/opencode/opencode.jsonc) under mcpServers:
    "verdandi-context-compiler": {
      "type": "stdio",
      "command": "node",
      "args": ["<ABSOLUTE_PATH_TO_CAPSULE>/dist/src/mcp_server.js"],
      "enabled": true
    }
    
  4. Auto-Inject CLI Usage:
    ./run_with_capsule.sh opencode <projectRoot> "<task>"
    

2. NatureCo CLI (natureco)

If you are running as NatureCo CLI:

  1. Build the server:
    npm install && npm run build
    
  2. Register the MCP server directly in NatureCo:
    natureco mcp add verdandi-context-compiler node <ABSOLUTE_PATH_TO_CAPSULE>/dist/src/mcp_server.js
    
  3. Auto-Inject CLI Usage:
    ./run_with_capsule.sh natureco <projectRoot> "<task>"
    

Read the full file on GitHub · 224 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.

  1. today Changed · +3 lines · +143 tokens per session a555f656e1e5
  2. 5d ago First seen · 221 lines · 1,899 tokens per session scan A 09e2889ca987

Subscribe to this mod's changes

verdandi AGENTS.md is an instructions file published in the GitHub repository natureco-official/verdandi (1 stars, last pushed today), licensed MIT. It adds 2,042 tokens to every session, about $0.0102 per session on Opus 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 2026-08-31.

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