NornicDB AGENTS.md

NornicDB AGENTS.md is an instructions file for Codex, OpenCode from orneryd/NornicDB. It costs 5,957 tokens per session, scanned A, original, MIT.

A development guide for NornicDB, a graph database designed to work with Neo4j-compatible applications. It sets expectations for performance, correctness, compatibility, tests, and documentation.

In plain words
What is it for?
Use it when changing NornicDB code, especially for benchmarking, memory checks, compatibility tests, coverage, API documentation, and test-driven bug fixes.
Why use it?
It gives coding agents project rules for proving changes are useful and avoiding regressions.

Instructions file for CodexOpenCode

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/orneryd/nornicdb/agents-md
Clone the repo
git clone --depth 1 https://github.com/orneryd/NornicDB

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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README.md
[![agentmods](https://agentmods.dev/badge/instructions/orneryd/nornicdb/agents-md.svg)](https://agentmods.dev/instructions/orneryd/nornicdb/agents-md)
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<a href="https://agentmods.dev/instructions/orneryd/nornicdb/agents-md"><img src="https://agentmods.dev/badge/instructions/orneryd/nornicdb/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 5,957 This file is loaded in full into every session.
When invoked 5,957 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 $0.05957 $0.05957
Opus 5 $0.02978 $0.02978
Sonnet 5 $0.01191 $0.01191
Haiku 4.5 $0.00596 $0.00596

Measured 5d ago against content hash d99a2b788f94, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

NornicDB 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 5d 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.

AGENTS.md · 842 lines

How it starts

The opening of the file, as written. The whole thing — 842 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NornicDB Development Guide for AI Agents

Version: 1.0.0
Last Updated: 2024-12-01
Target Audience: AI coding agents (Claude, GPT-4, etc.)


Core Philosophy

NornicDB is an enterprise-grade, high-performance graph database that prioritizes:

  1. Neo4j Compatibility - Drop-in replacement with zero code changes
  2. Performance - 3-52x faster than Neo4j across benchmarks
  3. Correctness - 90%+ test coverage, regression prevention mandatory
  4. Maintainability - Clean architecture, separation of concerns, DRY principles
  5. Documentation - Every public API fully documented with real-world examples

🎯 Golden Rules

1. Prove Value Before Merging

Every change must demonstrate measurable improvements without significant regressions:

  • Performance: Benchmark before/after. Show ops/sec improvements.
  • Memory: Profile memory usage. No >10% increases without justification.
  • Code Quality: Maintain or improve test coverage (90% minimum).
  • Compatibility: All Neo4j compatibility tests must pass.

Example:

// ❌ BAD: No proof of improvement
func optimizeQuery() { /* new algorithm */ }

// ✅ GOOD: Benchmarked and documented
// BenchmarkQueryOptimization shows 2.3x speedup:
//   Before: 4,252 ops/sec
//   After:  9,780 ops/sec
//   Memory: -15% (reduced allocations)
func optimizeQuery() { /* new algorithm with proof */ }

2. Test-Driven Bug Fixes

MANDATORY WORKFLOW for all bugs:

  1. Write failing test - Reproduce the exact bug condition
  2. Verify test fails - Confirm it catches the bug
  3. Fix the bug - Implement minimal fix
  4. Verify test passes - Confirm fix works
  5. Add regression tests - Prevent future occurrences

Example from codebase:

// File: pkg/cypher/aggregation_bugs_test.go
// BUG #1: WHERE ... IS NOT NULL combined with WITH aggregation returns empty results

func TestBug_WhereIsNotNullWithAggregation(t *testing.T) {
    // 1. Setup test data that triggers bug
    setupAggregationTestData(t, store)
    
    // 2. Execute query that fails in production
    result, err := exec.Execute(ctx, `
        MATCH (f:File)
        WHERE f.extension IS NOT NULL
        WITH f.extension as ext, COUNT(f) as count
        RETURN ext, count
    `, nil)
    
    // 3. Assert expected behavior (this WILL fail before fix)
    require.NoError(t, err)
    assert.Equal(t, int64(2), extCounts[".ts"])
    assert.Equal(t, int64(3), extCounts[".md"])
}

Read the full file on GitHub · 842 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. 5d ago First seen · 842 lines · 5,957 tokens per session scan A d99a2b788f94

Subscribe to this mod's changes

NornicDB AGENTS.md is an instructions file published in the GitHub repository orneryd/NornicDB (852 stars, last pushed 6d ago), licensed MIT. It adds 5,957 tokens to every session, about $0.0298 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 2026-08-30.