# hyperstack

> Use this tool when you need to track and verify the decision-making process of AI agents, providing a transparent and explainable memory layer. Hyperstack solves problems of accountability and trust in AI systems by tracing the origin and evolution of agent knowledge. It takes in agent actions and outputs a graph of provenance, enabling auditing and debugging of AI decision-making.

Canonical page: https://skillsregistry.net/skills/deeqyaqub1-cmd-hyperstack  
JSON: https://api.skillsregistry.net/v1/skills/deeqyaqub1-cmd-hyperstack

## Description

The Agent Provenance Graph for AI agents — the only memory layer where agents can prove what they knew, trace why.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-05-18

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/deeqyaqub1-cmd-hyperstack)

## 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": "deeqyaqub1-cmd-hyperstack"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/deeqyaqub1-cmd-hyperstack` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/deeqyaqub1-cmd-hyperstack/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