# project-memory

> project-memory — hozakar-project-memory. Use this tool when you need to capture and preserve the context and reasoning behind code changes, enabling better collaboration and knowledge sharing among developers. It solves problems of lost knowledge and technical debt by providing a searchable memory of engineering decisions. The tool integrates with git, accepting code commits as input and outputting a searchable database of decision history.

Canonical page: https://skillsregistry.net/skills/hozakar-project-memory  
JSON: https://api.skillsregistry.net/v1/skills/hozakar-project-memory

## Description

Engineering decision memory for AI-assisted coding — captures the why behind your code

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/hozakar/project-memory)

## 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": "hozakar-project-memory"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/hozakar-project-memory` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/hozakar-project-memory/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