agentmods.dev

aigentify AGENTS.md

An instruction file describing how to use Aigentify, a tool that checks whether a product is ready for software agents and creates the public information they need.

In plain words
What is it for?
Use it to understand Aigentify's library, command-line interface, and MCP connection, plus the expected audit and artifact-generation process.
Why use it?
It gives contributors one documented loop for finding agent-readiness gaps, fixing them, and generating the missing public files.

Instructions file for CodexOpenCode

0 4d ago A scan 244 tokens original MIT
View the source file pooriaarab/aigentify
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/pooriaarab/aigentify/agents-md
Clone the repo
git clone --depth 1 https://github.com/pooriaarab/aigentify

Made for: Codex, OpenCode.

Per session 244 This file is loaded in full into every session.
When invoked 244 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 $0.00244 $0.00244
Opus 5 $0.00122 $0.00122
Sonnet 5 $0.00049 $0.00049
Haiku 4.5 $0.00024 $0.00024

Measured yesterday against content hash 5a46e6ab7389, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

aigentify 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 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.

AGENTS.md · 39 lines

What it actually says

aigentify

Mental model

aigentify audits whether a product is ready for agents and creates the public files that agents need.

The faces

  • Library: import auditTarget and generate from aigentify.
  • CLI: use aigentify audit, aigentify init, aigentify gen, or aigentify mcp.
  • MCP: connect to aigentify-mcp over stdio.

MCP tools

Tool Input Result
audit { target } A score and a list of agent-readiness gaps.
gen { artifact, params } Generated artifact text.

The loop

  1. Audit the product directory or URL.
  2. Fix the highest-impact gap.
  3. Generate the missing public artifact.
  4. Audit again.

Rules

  • Treat product facts as source facts.
  • Keep public instructions about the product and its use.
  • State prices plainly.
  • Use the same artifact names across the library, CLI, and MCP tool.

Offer

aigentify is free to use.

Price: USD 0

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. yesterday First seen · 39 lines · 244 tokens per session scan A 5a46e6ab7389

Subscribe to this mod's changes

aigentify AGENTS.md is an instructions file published in the GitHub repository pooriaarab/aigentify (0 stars, last pushed 4d ago), licensed MIT. It adds 244 tokens to every session, about $0.0012 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.

Data and AI
Related

Other instructions, from other repositories

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,182 tokens

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).

microsoft/vscode · 6,785 tokens

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.

langchain-ai/langchain · 4,345 tokens

vscode oss-third-party-notices.instructions.md

Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).

microsoft/vscode · 5,001 tokens

next.js AGENTS.md

Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,296 tokens