# p-layer

> p-layer — humanerd-drew-p-layer. Use this tool when you need to manage and enforce layered rules in AI agent memory, solving issues of rule consistency and data integrity. It provides a structured interface for inputs and outputs, utilizing SQLite and PostgreSQL databases, and is particularly useful in contexts where rule-based decision-making is critical. By leveraging p-layer, AI agents can efficiently store and evaluate rules, ensuring reliable and consistent performance.

Canonical page: https://skillsregistry.net/skills/humanerd-drew-p-layer  
JSON: https://api.skillsregistry.net/v1/skills/humanerd-drew-p-layer

## Description

Memory for AI agents with rules that are kept, not just written - P0-P6 layered rules enforced in code, SQLite + PostgreSQL, MCP server, eval harness. PyPI: p-layers

## Trust

- **Trust score (0–1):** 0.61
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/humanerd-drew/p-layer)

## 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": "humanerd-drew-p-layer"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/humanerd-drew-p-layer` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/humanerd-drew-p-layer/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt