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.
npx skills add Quinoto-Tech/QuinotoSpec --skill quinotospec-agent-traingit clone --depth 1 https://github.com/Quinoto-Tech/QuinotoSpecWrote 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.
[](https://agentmods.dev/skills/quinoto-tech/quinotospec/quinotospec-agent-train)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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.
- Ejecutar el workflow
quinotospec.refresh-discovery.md - 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:
-
Detectar estructura de directorios:
- Buscar en raíz y nivel 1-2
- Identificar:
src/,app/,modules/,packages/,services/,apps/,lib/
-
Detectar sub-módulos:
- Listar directorios con código fuente
- Identificar patrones:
auth/,users/,payments/,products/, etc.
-
Detectar stack tecnológico:
- Buscar:
package.json,go.mod,requirements.txt,pyproject.toml,Cargo.toml,pom.xml
- Buscar:
-
Analizar complejidad:
- Cantidad de archivos y líneas de código
- Número de integraciones externas
- Complejidad de lógica de negocio
-
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}
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.
- 3d ago Changed · +6 lines 1f74088c2f91
- 12d ago First seen · 321 lines · 27 tokens per session scan A 62b99b38bef1
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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