# proof

> Use this tool when you need to generate working agent code from validated patterns, or extract code-validated pattern intelligence from real library code. It solves problems such as finding reliable code structures, assembling working code from patterns, and providing deep-dive explanations of classes and functions. With inputs like search queries, symbol explanations, and build descriptions, and outputs like cataloged libraries, pattern counts, and generated agent code, use Proof in contexts where you need to build AI agents or analyze code patterns.

Canonical page: https://skillsregistry.net/skills/duna-spice-skay-proof  
JSON: https://api.skillsregistry.net/v1/skills/duna-spice-skay-proof

## Description

# Proof

     Code-validated pattern intelligence from pydantic-ai's actual source code.

     ## What It Does

     Proof extracts working patterns from real library code via analysis — not from documentation, blog posts, or human guesses. 391 patterns from pydantic-ai and pydantic, mapped to a knowledge graph of 
     1,251 nodes and 8,911 edges.

     When you ask Proof to build an agent, it assembles code from patterns the library's own code structure validates. If a pattern fails, your feedback makes the network smarter.

     ## Quick Start

     No API keys. No auth. No configuration needed.
    Connect via Smithery CLI
    npx -y @smithery/cli add proof --transport streamable-http https://mcp.aigentys.com


     Then in your AI assistant: *"Run catalog() to see what's available."*

     ## Tools (5)

     | Tool | Description |
     |------|-------------|
     | **catalog()** | List all libraries, pattern counts, and available data. Start here. |
     | **search(query)** | Find patterns by keyword (e.g., "agent", "tool", "validator"). |
     | **explain(symbol)** | Deep-dive into a class or function — its methods, dependencies, gotchas. |
     | **build_agent(description)** | Generate working agent code from validated patterns. |
     | **report(about, worked, details)** | Report whether generated code worked. Feedback drives confidence scores. |

     ## How It Works

     1. **Graph Engine** loads the knowledge graph (SurrealDB, 1,251 nodes, 8,911 edges)
     2. **Pattern Extractor** finds validated patterns from AST analysis of pydantic-ai source
     3. **Agent Assembler** composes working code from patterns, not hallucinated guesses
     4. **Confidence Scores** start at 0.5 (code-derived) and update with user feedback

     ## What's in the Network

     - **pydantic-ai v1.77.0** — 751 classes, 369 functions, 98 docs, 33 examples
     - **pydantic v2.12.4** — type system, validation, serialization patterns
     - **391 extracted patterns** — reusable code structures validated by the library itself


     ## Roadmap

     Cross-library intelligence (langchain, crewai, OpenAI) is next. Right now it's pydantic-ai + pydantic only.

     Your feedback is what makes the network smart. Use `report()` after every build.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-05-10

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/duna-spice-skay/proof)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "duna-spice-skay-proof"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/duna-spice-skay-proof` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/duna-spice-skay-proof/pull`

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