quinotospec-agent-train

quinotospec-agent-train is a skill for Claude Code, Codex from Quinoto-Tech/QuinotoSpec. It costs 27 tokens per session (2,627 once invoked), scanned A, original, MIT.

A workflow for designing specialised coding agents from a project's structure and prior discovery work. It offers suggestions and can edit an existing agent, but does not create agent profiles automatically.

In plain words
What is it for?
Use it to suggest or edit a primary agent or subagent, optionally choosing its name, model, or type. It first refreshes the project's discovery information and then analyses the project structure.
Why use it?
It helps decide what kinds of agents a project needs without designing them from scratch. The suggestions are based on the codebase rather than guesswork.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; mentions OpenCode.

Good fit Use it to suggest or edit a primary agent or subagent, optionally choosing its name, model, or type. It first refreshes the project's discovery information and then analyses the project structure.

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Install with agentmods
npx agentmods add skills/quinoto-tech/quinotospec/quinotospec-agent-train
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.

Any agent
npx skills add Quinoto-Tech/QuinotoSpec --skill quinotospec-agent-train
Clone the repo
git clone --depth 1 https://github.com/Quinoto-Tech/QuinotoSpec

Made for: Claude Code, Codex.

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 quinotospec-agent-train

README.md
[![agentmods](https://agentmods.dev/badge/skills/quinoto-tech/quinotospec/quinotospec-agent-train/github.svg)](https://agentmods.dev/skills/quinoto-tech/quinotospec/quinotospec-agent-train)
Your own site
<a href="https://agentmods.dev/skills/quinoto-tech/quinotospec/quinotospec-agent-train"><img src="https://agentmods.dev/badge/skills/quinoto-tech/quinotospec/quinotospec-agent-train/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for quinotospec-agent-train

Your own site · 80×15
<a href="https://agentmods.dev/skills/quinoto-tech/quinotospec/quinotospec-agent-train"><img src="https://agentmods.dev/badge/skills/quinoto-tech/quinotospec/quinotospec-agent-train.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,627 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00027 $0.02627
Opus 5 $0.00014 $0.01314
Sonnet 5 $0.00005 $0.00525
Haiku 4.5 $0.00003 $0.00263

Measured 3d ago against content hash 1f74088c2f91, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

quinotospec-agent-train 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.

agent-dist/skills/quinotospec-agent-train/SKILL.md · 327 lines

How it starts

The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.

[INSTRUCCIÓN MAESTRA]

Workflow: Agent Train

Este workflow ayuda al desarrollador a crear agentes abstractos especializados, haciendo sugerencias basadas en el discovery y la estructura del proyecto. No genera perfiles automáticamente, sino que guía y sugiere basándose en el análisis del código.


Parámetros

Parámetro Descripción Valores
AGENT_NAME Nombre del agente a crear (opcional) Nombre especificado por el usuario
--suggest Modo de sugerencias Genera sugerencias basadas en análisis
--edit Modo edición Editar un agente existente
--model Forzar modelo específico (opcional) opencode/big-pickle, opencode-go/mimo-v2-pro, opencode-go/mimo-v2-omni, opencode-go/glm-5.1
--type Forzar tipo de agente (opcional) primary, subagent

Flujo Principal

Paso 0 — Refrescar Discovery

OBLIGATORIO: Ejecutar quinotospec.refresh-discovery.md para asegurar datos actualizadas.

  1. Ejecutar el workflow quinotospec.refresh-discovery.md
  2. Si no existe discovery, informar al usuario y ofrecer crear uno primero.

Paso 1 — Analizar Estructura del Proyecto

Escanear la estructura del proyecto para detectar áreas potenciales:

  1. Detectar estructura de directorios:

    • Buscar en raíz y nivel 1-2
    • Identificar: src/, app/, modules/, packages/, services/, apps/, lib/
  2. Detectar sub-módulos:

    • Listar directorios con código fuente
    • Identificar patrones: auth/, users/, payments/, products/, etc.
  3. Detectar stack tecnológico:

    • Buscar: package.json, go.mod, requirements.txt, pyproject.toml, Cargo.toml, pom.xml
  4. Analizar complejidad:

    • Cantidad de archivos y líneas de código
    • Número de integraciones externas
    • Complejidad de lógica de negocio
  5. Presentar análisis al usuario:

    Análisis del proyecto:
    
    [Stack Detectado]
    - Lenguaje: {detectado}
    - Framework: {detectado}
    - Build tool: {detectado}
    
    [Estructura]
    - Directorios principales: {lista}
    - Sub-módulos: {lista}
    - Complejidad: {baja/media/alta}
    
    [Sugerencias de agentes]
    1. {suggestion 1}
    2. {suggestion 2}
    3. {suggestion 3}
    

Read the full file on GitHub · 327 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 Changed · +6 lines 1f74088c2f91
  2. 12d ago First seen · 321 lines · 27 tokens per session scan A 62b99b38bef1

Subscribe to this mod's changes

quinotospec-agent-train is a skill published in the GitHub repository Quinoto-Tech/QuinotoSpec (20 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 2,627 once invoked, about $0.0001 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.

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