agentmods.dev

engrava AGENTS.md

Repository instructions for coding agents working on engrava, a Python graph-memory database for AI agents. They explain what to read before making commits and how to keep public repository changes safe and consistent.

In plain words
What is it for?
Use them when editing engrava, preparing commits or pull requests, naming branches, or checking repository-specific testing and contribution requirements.
Why use it?
They give agents the project context and contribution rules before they change code. This helps avoid poorly formatted commits, unsafe public wording, and incorrect branches.

Instructions file for CodexOpenCode

5 9d ago A scan 723 tokens original MIT
View the source file sovantica/engrava
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/sovantica/engrava/agents-md
Clone the repo
git clone --depth 1 https://github.com/sovantica/engrava

Made for: Codex, OpenCode.

Per session 723 This file is loaded in full into every session.
When invoked 723 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 $0.00723 $0.00723
Opus 5 $0.00362 $0.00362
Sonnet 5 $0.00145 $0.00145
Haiku 4.5 $0.00072 $0.00072

Measured 2d ago against content hash 6bf53da7a3ea, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

engrava 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 2d 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:

AGENTS.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — engrava

Canonical guidance for AI coding agents (Claude Code, GitHub Copilot, Cursor, Aider, Continue, OpenAI Codex CLI, and others) working in this repository.

Repository context

This is a public repository (engrava) — Graph memory database for AI agents. License: MIT. Primary language: python 3.11+.

Assume every commit message, PR title, PR body, branch name, code comment, and docstring is world-visible. Public-safe by default.

Required reading before generating commits

  1. CLAUDE.md — full context for this repo (quality bar, testing conventions, commit format, branching).
  2. CONTRIBUTING.md — contribution flow.
  3. .github/copilot-instructions.md — architecture, naming patterns, scope reference.
  4. BRANCHING.md — branch naming + flow.

Commit hygiene

  • Conventional Commits 1.0.0 — <type>(<scope>): <description>. Types in CLAUDE.md.
  • Public release tags (vX.Y.Z) and this repo's GitHub issue/PR numbers (#N) are your durable identifiers.
  • Generic feature descriptions and Conventional Commit headers are always welcome.

A pre-commit hook is provided locally to give you a fast feedback loop on commit-message hygiene. Run git commit and you'll see the check fire on any message that wouldn't be appropriate for a public open-source project.

Branch hygiene

Branch names: <type>/<kebab-description>. Plain English. No internal identifiers. ≤ 50 chars.

Examples:

  • feature/priority-signal-hybrid-search
  • fix/empty-count-on-mindql
  • docs/quickstart-update

Self-check before every commit

Before finalising a commit message, ask:

  1. Could this message, branch name, or referenced symbol reveal information about a workflow that should stay private?
  2. Am I using only durable public identifiers (release tags, this repo's issue/PR numbers)?
  3. Is the message format Conventional Commits?
  4. Is the branch name plain English in <type>/<kebab-description> form?

Read the full file on GitHub · 59 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. 2d ago First seen · 59 lines · 723 tokens per session scan A 6bf53da7a3ea

Subscribe to this mod's changes

engrava AGENTS.md is an instructions file published in the GitHub repository sovantica/engrava (5 stars, last pushed 9d ago), licensed MIT. It adds 723 tokens to every session, about $0.0036 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.

Autonomous Agentsagent-memoryai-agentsaudit-traildurable-memorygraph-database
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