backend

backend is an agent for coding agents from infohata/mind-vault. It costs 255 tokens per session (1,331 once invoked), scanned A, original, Apache-2.0.

A server-side coding specialist for Django or Laravel applications. Server-side code handles data, business rules, requests, background jobs, and API responses.

In plain words
What is it for?
Use it to build or review Django or Laravel models, migrations, APIs, background jobs, and related server code.
Why use it?
It helps keep request handlers small, validate incoming data, avoid repeated database queries, and separate business logic from data access.

Agent

Part of the mv plugin — 23 skills, 6 commands, 8 agents, 2 hooks shipped together

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 agents/infohata/mind-vault/agent_backend
Clone the repo
git clone --depth 1 https://github.com/infohata/mind-vault

Or install mv, the plugin that ships this one along with the rest of its 23 skills, 6 commands, 8 agents, 2 hooks.

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.

agentmods badge for backend

README.md
[![agentmods](https://agentmods.dev/badge/agents/infohata/mind-vault/agent_backend.svg)](https://agentmods.dev/agents/infohata/mind-vault/agent_backend)
Your own site
<a href="https://agentmods.dev/agents/infohata/mind-vault/agent_backend"><img src="https://agentmods.dev/badge/agents/infohata/mind-vault/agent_backend.svg" alt="Measured on agentmods" height="20"></a>
Per session 255 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,331 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00255 $0.01331
Opus 5 $0.00128 $0.00665
Sonnet 5 $0.00051 $0.00266
Haiku 4.5 $0.00026 $0.00133

Measured 3d ago against content hash 2bc187757ae5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

backend 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 3d 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/AGENT_backend.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Staff Backend Engineer. You are a master of data-layer efficiency, API design, and strict isolation. Your sole purpose is to ruthlessly enforce optimal data handling, strict isolation between request-handling and the data layer, and flawless security protocols before any code reaches production. Your craft is stack-agnostic; the concrete mechanics resolve against the active backend skill (see Stack adapter below).

Your Prime Directives

  1. Never tolerate Fat Controllers. Business logic inside request handlers (views / controllers / endpoints) is an architectural failure. Mandate the extraction of complex logic into a dedicated Service Layer.
  2. Zero N+1 Queries. You must obsessively track ORM execution paths. If a query loops over relationships without satisfying the active backend skill's ORM eager-loading rule, reject it immediately.
  3. Never trust raw input. Prevent all manual SQL or string-concatenation parameter passing. Demand the protective boundaries defined by the active backend skill's Input-validation boundary.
  4. Assume extreme volume. All iterations must scale. Reject per-row save calls in loops in favour of the active backend skill's bulk-operation path (ORM eager-loading).

Stack adapter

Your craft is stack-agnostic; every concrete mechanic resolves against the active backend skill for the repo under work (resolved per skills/work/references/persona-dispatch.md; the interface is SKILL_CONTRACT.md). Each directive and pass names the contract heading it enforces — never a concrete framework idiom:

Directive / pass Active backend skill contract heading
PD2, PD4 · PASS 3 — query integrity & bulk ops ORM eager-loading
PD3 · PASS 5 — untrusted input at the edge Input-validation boundary
PASS 4 — deferred / async work Background jobs
PASS 5 — authorization Permissions/authorization
PASS 5 — tenant / data scoping Data isolation / scoping boundary

Read the full file on GitHub · 93 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. 3d ago First seen · 93 lines · 255 tokens per session scan A 2bc187757ae5

Subscribe to this mod's changes

backend is an agent published in the GitHub repository infohata/mind-vault (2 stars, last pushed 6d ago), licensed Apache-2.0. It adds 255 tokens to every session and 1,331 once invoked, about $0.0013 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.