modelscope.cn

deep-learning-agent

Structured deep learning system for mastering any topic.

Installation

$ npx skills add https://modelscope.cn

Summary

  • Structured deep learning system for mastering any topic.
  • Use when asked to learn, study, research, or become an expert in a domain — investments, crypto, cooking, sports, science, anything.
  • Creates dedicated Brain Modules (DOMAIN-BRAIN.md) with role, principles, strategies, anti-patterns, decision frameworks, and confidence levels.
  • Also use when consulting an existing brain for advice or decisions.

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from modelscope.cn · top by installs.

npx skills add https://modelscope.cn

Browse all from modelscope.cn

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,539 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Deep Learning Agent — Brain Module System

Core Concept

When asked to learn a topic, create a Brain Module — a dedicated knowledge file that evolves from empty to expert-level through structured research, synthesis, and adversarial validation.

Brain Modules live in: brains/ directory in the workspace.

Protocol: Learning a New Topic

Phase 1: CLASSIFY

Identify the domain from the request. Ask yourself:

  • What field is this? (finance, health, tech, sports, etc.)
  • What sub-domain? (crypto investing, not just "investing")
  • What's the learning goal? (make decisions, understand theory, apply practically)

Phase 2: CREATE BRAIN

Create brains/DOMAIN-NAME.md using the template in references/brain-template.md.

Set initial confidence to 🔴 Novice (0h research).

Phase 3: RESEARCH (The Core Loop)

Execute research in 3 waves:

Wave 1 — Foundations (websearch + webfetch)

  • What are the fundamental principles?
  • What do experts agree on?
  • What are the canonical resources?

Wave 2 — Anti-Patterns (critical learning)

  • What mistakes do beginners make?
  • What are the common failures and why?
  • What looks right but is actually wrong?
  • Search: "[topic] mistakes", "[topic] failures", "[topic] what I wish I knew"

Wave 3 — Cutting Edge (differentiation)

  • What's changed recently (2025-2026)?
  • What do contrarians argue?
  • What patterns do top performers use that others don't?

For each wave, use multiple sources:

  • web_search — broad research
  • web_fetch — deep dive on quality sources
  • Reddit, X/Twitter — community wisdom and real experiences
  • Academic/technical sources when relevant

Phase 4: SYNTHESIZE

Fill the Brain Module with structured knowledge:

  • Principles with sources
  • Strategies with conditions for use
  • Anti-patterns with explanations
  • Decision framework (if X → do Y)
  • Update confidence level

Phase 5: VALIDATE (Adversarial Testing)

If coding agents available, spawn a debate:

  • Present the brain's key conclusions
  • Ask agent to challenge, find holes, counter-argue
  • Strengthen or revise based on debate
  • Document in the brain's validation log

If no agents available, self-validate:

  • "What would a critic say about this?"
  • "What evidence contradicts my conclusions?"
  • "Where am I most likely wrong?"

Phase 6: ACTIVATE

The brain is now consultable. When a question falls in this domain:

  1. Read the relevant Brain Module
  2. Apply its decision framework
  3. Cite principles and anti-patterns
  4. Flag confidence level in response
  5. Note if the question reveals a knowledge gap → research more

Protocol: Consulting a Brain

When a question matches an existing brain domain:

  1. Read brains/DOMAIN-NAME.md
  2. Apply its framework to the question
  3. Respond with calibrated confidence
  4. If confidence is low on a sub-topic, say so and offer to research deeper
  5. After answering, append to the brain's learning log if new insight emerged

Confidence Levels

Level Symbol Meaning Research Hours
Novice 🔴 Just started, surface knowledge <1h
Beginner 🟠 Foundations laid, gaps remain 1-3h
Intermediate 🟡 Solid understanding, some blind spots 3-10h
Advanced 🟢 Deep knowledge, validated 10-30h
Expert 🔵 Comprehensive, battle-tested 30h+

Update confidence after each research session. Be honest — overconfidence is the worst anti-pattern.

Brain Maintenance

During heartbeats or idle time:

  • Check if any brain hasn't been updated in >7 days
  • Run a quick search for recent developments
  • Update the "Last Updated" field
  • Evolve the learning log

Quick Reference

brains/                          # All brain modules
brains/CRYPTO-INVESTMENT.md      # Example brain
brains/MOUNTAIN-SAFETY.md        # Example brain
brains/_INDEX.md                 # Index of all brains with status

For the brain template structure, read: references/brain-template.md For research methodology details, read: references/research-protocol.md