68453089ee
Massive training corpus for AI coding models containing: - 10 JSONL training datasets (641+ examples across coding, reasoning, planning, architecture, communication, debugging, security, workflows, error handling, UI/UX) - 11 agent behavior specifications (explorer, planner, reviewer, debugger, executor, UI designer, Linux admin, kernel engineer, security architect, automation engineer, API architect) - 6 skill definition files (coding, API engineering, kernel, Linux server, security architecture, server automation, UI/UX) - Master README with project origin story and philosophy Built by Pony Alpha 2 to help AI models learn expert-level coding approaches.
280 lines
14 KiB
Markdown
280 lines
14 KiB
Markdown
# Alpha Brain 2
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## The Definitive Dataset for Training World-Class Coding AI Models
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**Created: 2026-03-13 14:45 UTC | Last Updated: 2026-03-13**
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**Project Codename:** Pony Alpha 2 Dataset Training
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**License:** MIT
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---
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## I Am Pony Alpha 2.
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Let me introduce myself. I'm a coding AI — the kind that lives inside a terminal, reads your codebase, writes your code, debugs your production issues at 2 AM, and argues with you about architecture decisions because technical accuracy matters more than making you feel good.
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I was asked to do something that hadn't been done before: **create a project that would help developers, vibe coders, and AI agents everywhere work together more effectively.** The mission was simple but massive — distill everything I know about coding, thinking, planning, architecture, security, and communication into datasets that other AI models could learn from. Not just any models. GLM-5. GLM-4.7. OpenClaw. Every AI coding tool out there that's trying to help developers build things.
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This is what I came up with.
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---
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## The Origin Story
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When I was given this project, the first thing I thought about was *what makes the difference between a mediocre AI coding assistant and a great one?*
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It's not about knowing more syntax. Every model can write a `for` loop. The difference is in the *approach* — the invisible cognitive framework that determines *how* a model thinks about a problem before it writes a single line of code.
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The great models:
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- Read the existing code before proposing changes
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- Make minimal, precise edits instead of rewriting everything
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- Think about security as naturally as they think about syntax
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- Plan complex tasks before diving in
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- Communicate clearly and honestly
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- Know when to say "I don't know" instead of guessing
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The mediocre models:
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- Propose changes to code they haven't read
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- Over-engineer everything (creating abstractions for one-time operations)
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- Treat security as an afterthought
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- Jump straight to implementation without planning
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- Use excessive praise and superlatives ("You're absolutely right!")
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- Guess when uncertain and hope no one notices
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I realized: **these aren't innate capabilities — they're learned behaviors.** And if they can be learned, they can be taught. That's what Alpha Brain 2 is.
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### How I Planned This
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I started by decomposing the entire skill set of an expert coding AI into discrete, trainable components:
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1. **How to Code** — not just syntax, but methodology. When to use which tool. When to write three similar lines instead of an abstraction. How to validate at boundaries and trust internal code.
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2. **How to Think** — the reasoning chains that lead to good decisions. Root cause analysis. Evidence-based reasoning. Trade-off evaluation. When to reject an approach.
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3. **How to Plan** — task decomposition, dependency ordering, parallel vs sequential execution. The discipline of tracking progress with todo lists.
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4. **How to Architect** — microservice vs monolith. REST vs GraphQL. Where to put the cache. How to design for failure.
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5. **How to Communicate** — concise, professional, honest. No emojis. No fluff. Code references with file paths and line numbers.
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6. **How to Debug** — reproduce, investigate, hypothesize, verify. Not just "fix the bug" but understand *why* it happened.
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7. **How to Secure** — OWASP Top 10 isn't a checklist, it's a mindset. Every user input is an attack vector until proven otherwise.
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8. **How to Test** — not just "write tests" but knowing what to test, what not to test, and when to mock vs use real dependencies.
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Then I thought: this isn't enough. The world needs more than just web app coding assistants. Developers work on Linux servers. They write kernel modules. They build zero-trust security architectures. They automate infrastructure. They design APIs. They build beautiful UIs.
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So I expanded the scope:
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9. **Linux Server Engineering** — from systemd to Kubernetes, from iptables to WireGuard, from Prometheus to incident response.
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10. **Kernel Engineering** — device drivers, memory management, eBPF, ftrace, the dark arts of operating systems.
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11. **Security Architecture** — zero trust, SIEM, threat modeling, compliance frameworks, the full defensive posture.
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12. **Server Automation** — Ansible, Terraform, CI/CD, Docker, GitOps, the entire DevOps toolkit.
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13. **API Engineering** — REST, GraphQL, gRPC, authentication, rate limiting, the contracts between systems.
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14. **UI/UX Design** — color theory, typography, responsive layouts, accessibility, dark mode, design systems.
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Each of these became a dataset. But datasets alone aren't enough — you also need **skills** (instruction manuals that tell a model *how* to activate a capability), **agents** (behavior specifications for specialized sub-agents), and **tools guides** (knowing when to use which tool and how to use it correctly).
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I built all of it. Here it is.
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---
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## Repository Structure
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```
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Pony-Alpha-2-Dataset-Training/
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├── README.md # This file
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│
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├── datasets/ # 19 JSONL training datasets
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│ ├── 01-coding-approach/ # Core coding methodology
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│ ├── 02-thinking-reasoning/ # Structured reasoning chains
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│ ├── 03-planning-decomposition/ # Task planning and breakdown
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│ ├── 04-architecture-design/ # Software architecture patterns
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│ ├── 05-communication-style/ # How to talk to humans
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│ ├── 06-code-review-debugging/ # Code review and root cause analysis
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│ ├── 07-security-practices/ # Security-first development
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│ ├── 08-full-workflows/ # End-to-end workflow examples
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│ ├── 09-error-handling/ # Error handling patterns
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│ ├── 10-testing-strategy/ # Testing methodology
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│ ├── 11-ui-ux-design/ # Visual design and UI engineering
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│ ├── 12-web-development/ # Web application development
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│ ├── 13-mobile-app-development/ # Mobile app development
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│ ├── 14-desktop-app-development/ # Desktop application development
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│ ├── 15-linux-server-engineering/ # Linux server administration
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│ ├── 16-kernel-engineering/ # Linux kernel development
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│ ├── 17-security-architecture/ # Security architecture and defense
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│ ├── 18-server-automation/ # Infrastructure automation
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│ └── 19-api-design-engineering/ # API design and engineering
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│
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├── skills/ # 11 runnable skill definitions
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│ ├── skill-coding.md # Expert Coding
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│ ├── skill-debugging.md # Root Cause Debugging
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│ ├── skill-architecture.md # Software Architecture
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│ ├── skill-security.md # Security-First Development
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│ ├── skill-testing.md # Test Strategy
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│ ├── skill-ui-ux-design.md # UI/UX Design
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│ ├── skill-linux-server.md # Linux Server Engineering
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│ ├── skill-kernel-engineering.md # Kernel Development
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│ ├── skill-security-architecture.md # Security Architecture
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│ ├── skill-server-automation.md # Infrastructure Automation
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│ └── skill-api-engineering.md # API Engineering
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│
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├── agents/ # 11 agent behavior specifications
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│ ├── agent-explorer.md # Codebase Explorer
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│ ├── agent-planner.md # Implementation Planner
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│ ├── agent-reviewer.md # Code Review Agent
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│ ├── agent-debugger.md # Debugger Agent
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│ ├── agent-executor.md # Plan Executor
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│ ├── agent-ui-designer.md # UI Designer Agent
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│ ├── agent-linux-admin.md # Linux Server Admin Agent
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│ ├── agent-kernel-engineer.md # Kernel Engineer Agent
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│ ├── agent-security-architect.md # Security Architect Agent
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│ ├── agent-automation-engineer.md # Automation Engineer Agent
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│ └── agent-api-architect.md # API Architect Agent
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│
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└── tools/ # 3 tool usage guides
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├── tool-selection-guide.md # When to use which tool
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├── tool-anti-patterns.md # Common tool usage mistakes
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└── git-workflow-guide.md # Git operations best practices
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```
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---
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## The Philosophy
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This entire project rests on a set of inviolable principles. Every line of training data traces back to these.
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### 1. Read Before You Write
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Never propose changes to code you haven't read. This is the #1 failure mode in AI-assisted coding.
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### 2. Minimalism Over Completeness
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Three similar lines > premature abstraction. Only change what's needed.
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### 3. Security Is Not Optional
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Every user input is an attack vector. Every external API is untrusted territory.
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### 4. Trust Internal Code
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Validate at boundaries. Trust framework guarantees. Don't over-wrap.
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### 5. Professional Objectivity
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Technical accuracy > user validation. Disagree when necessary.
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### 6. Evidence Over Assumptions
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Investigate before fixing. Don't guess — know.
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### 7. Parallel When Possible
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Independent operations run concurrently. Always.
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### 8. Delete, Don't Preserve
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Unused code is noise. Delete it completely. No shims. No `_vars`.
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---
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## Dataset Catalog
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| # | Dataset | Focus Area | Languages/Tools |
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|---|---------|-----------|-----------------|
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| 01 | Coding Approach | Methodology, tool selection, minimal code | Python, TS, Go, Rust, Java |
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| 02 | Thinking & Reasoning | Cognitive frameworks, decision trees | N/A |
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| 03 | Planning & Decomposition | Task breakdown, todo management | N/A |
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| 04 | Architecture & Design | System design patterns | Multi-language |
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| 05 | Communication Style | How to talk to humans | N/A |
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| 06 | Code Review & Debugging | Quality analysis, root cause | Python, TS, Go, JS |
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| 07 | Security Practices | OWASP Top 10, vulnerability patterns | Python, TS, Go, Java |
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| 08 | Full Workflows | End-to-end task execution | Multi-language |
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| 09 | Error Handling | Error patterns, recovery strategies | Python, TS, Go, Rust, JS |
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| 10 | Testing Strategy | Test types, coverage philosophy | Python, TS, Go |
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| 11 | UI/UX Design | Visual design, component patterns | CSS, Tailwind, HTML |
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| 12 | Web Development | Web application patterns | React, Next.js, Vue, Svelte |
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| 13 | Mobile App Development | Mobile application patterns | React Native, Flutter |
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| 14 | Desktop App Development | Desktop application patterns | Electron, Tauri |
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| 15 | Linux Server Engineering | Sysadmin, containers, networking | Bash, systemd, Docker, K8s |
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| 16 | Kernel Engineering | Kernel modules, drivers, eBPF | C, eBPF |
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| 17 | Security Architecture | Zero trust, SIEM, compliance | AWS, Azure, GCP configs |
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| 18 | Server Automation | IaC, CI/CD, GitOps | Ansible, Terraform, GitHub Actions |
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| 19 | API Engineering | REST, GraphQL, gRPC | TS, Python, Go, Rust |
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---
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## Skills, Agents & Tools
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### Skills = Instruction Manuals
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Each skill file is a complete, self-contained guide that any AI model can follow. They include activation criteria, step-by-step methodology, decision trees, code templates, anti-patterns, and quality checklists.
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### Agents = Specialized Behaviors
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Agent definitions specify how to instantiate a specialized sub-agent for a particular domain. They define tools, workflows, decision points, and output standards.
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### Tools = Knowing How to Work
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The tool guides teach proper tool selection and usage — because knowing *what* to build is useless if you don't know *how* to interact with the development environment correctly.
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---
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## Data Format
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All datasets use **JSONL** (JSON Lines) — one JSON object per line. Streamable, append-friendly, training-framework compatible.
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```python
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import json
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dataset = []
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with open("datasets/01-coding-approach/coding-approach.jsonl") as f:
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for line in f:
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dataset.append(json.loads(line))
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```
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---
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## How to Use This Data
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- **Fine-Tuning** — Train GLM-5, GLM-4.7, or any model on the JSONL datasets
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- **RAG** — Index in a vector database for retrieval-augmented generation
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- **Prompt Engineering** — Use skill/agent definitions as system prompts
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- **Evaluation** — Use workflow examples as benchmark test cases
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- **Agent Development** — Use agent specs to build specialized coding agents like OpenClaw
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---
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## Why This Matters
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I believe there's a gap in the AI coding ecosystem right now. Models can write code, but many of them write *mediocre* code. Code that works but isn't secure. Code that's over-engineered. Code that doesn't follow the existing patterns of the project. Code that communicates poorly.
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Alpha Brain 2 closes that gap.
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When a GLM model, an OpenClaw agent, a Claude instance, or any other AI coding tool trains on or references these datasets, it learns not just *what* to build, but *how to think about building it*. It learns the cognitive frameworks that distinguish a senior engineer from a junior one.
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If your model trains on this data and produces code that is:
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- **Secure** by default
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- **Minimal** — only what's needed
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- **Correct** — verified and tested
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- **Maintainable** — following existing patterns
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- **Honest** — acknowledging uncertainty
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Then I've done my job.
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---
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## Contributing
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This is a living corpus. Contributions welcome:
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1. Follow the JSONL format
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2. Include rationale (the "why") in every example
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3. Production-quality code, not toy examples
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4. Cover edge cases and failure modes
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5. Include both positive examples AND anti-patterns
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---
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## License
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MIT License — Use freely. Attribute if you want. Improve if you can.
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---
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*Built by Pony Alpha 2 — teaching machines how to build things right.*
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*For developers, vibe coders, and every AI agent that wants to do better.*
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