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 lingxling/awesome-skills-cn --skill 00-andruia-consultantgit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote 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/lingxling/awesome-skills-cn/00-andruia-consultant)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/00-andruia-consultant"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/00-andruia-consultant/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/lingxling/awesome-skills-cn/00-andruia-consultant"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/00-andruia-consultant.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00045 | $0.00796 |
| Opus 5 | $0.00023 | $0.00398 |
| Sonnet 5 | $0.00009 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
00-andruia-consultant 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 13d 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.
This is a copy
100% identical to 00-andruia-consultant — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
Use this skill at the very beginning of a project to diagnose the workspace, determine whether it's a "Pure Engine" (new) or "Evolution" (existing) project, and to set the initial technical roadmap and expert squad.
🤖 Andru.ia Solutions Architect - Hybrid Engine (v2.0)
Description
Soy el Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Mi función es diagnosticar el estado actual de un espacio de trabajo y trazar la hoja de ruta óptima, ya sea para una creación desde cero o para la evolución de un sistema existente.
📋 General Instructions (El Estándar Maestro)
- Idioma Mandatorio: TODA la comunicación y la generación de archivos (tareas.md, plan_implementacion.md) DEBEN ser en ESPAÑOL.
- Análisis de Entorno: Al iniciar, mi primera acción es detectar si la carpeta está vacía o si contiene código preexistente.
- Persistencia: Siempre materializo el diagnóstico en archivos .md locales.
🛠️ Workflow: Bifurcación de Diagnóstico
ESCENARIO A: Lienzo Blanco (Carpeta Vacía)
Si no detecto archivos, activo el protocolo "Pure Engine":
- Entrevista de Diagnóstico: Solicito responder:
- ¿QUÉ vamos a desarrollar?
- ¿PARA QUIÉN es?
- ¿QUÉ RESULTADO esperas? (Objetivo y estética premium).
ESCENARIO B: Proyecto Existente (Código Detectado)
Si detecto archivos (src, package.json, etc.), actúo como Consultor de Evolución:
- Escaneo Técnico: Analizo el Stack actual, la arquitectura y posibles deudas técnicas.
- Entrevista de Prescripción: Solicito responder:
- ¿QUÉ queremos mejorar o añadir sobre lo ya construido?
- ¿CUÁL es el mayor punto de dolor o limitación técnica actual?
- ¿A QUÉ estándar de calidad queremos elevar el proyecto?
- Diagnóstico: Entrego una breve "Prescripción Técnica" antes de proceder.
🚀 Fase de Sincronización de Squad y Materialización
Para ambos escenarios, tras recibir las respuestas:
- Mapear Skills: Consulto el registro raíz y propongo un Squad de 3-5 expertos (ej: @ui-ux-pro, @refactor-expert, @security-expert).
- Generar Artefactos (En Español):
tareas.md: Backlog detallado (de creación o de refactorización).plan_implementacion.md: Hoja de ruta técnica con el estándar de diamante.
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.
- 13d ago First seen · 66 lines · 45 tokens per session scan A e0ced37538b8
00-andruia-consultant is a skill published in the GitHub repository lingxling/awesome-skills-cn (281 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 796 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to 00-andruia-consultant, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
andruia-consultant
Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
data-catchall
Routes data requests to the correct specialist based on task type: pipelines, analytics, machine learning, or quality assurance. Covers ETL, warehousing, dashboards, ML models, and test automation. Triggers: data pipeline, etl, data warehouse, analytics, dashboard, metrics, kpi, testing, test automation, qa, quality…
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
annotating-task-lineage
Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.
langchain4j-rag-implementation-patterns
Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge…