# hicortex

> hicortex — gamaze-labs-hicortex. Use this tool when you need to enhance AI agent performance through automated experience capture and knowledge sharing. Hicortex solves the problem of isolated agent learning by distilling lessons from experiences and sharing them across entire fleets, supporting integration with platforms like Hermes and OpenClaw. It takes in AI agent experiences and outputs shared knowledge, ideal for use cases requiring continuous learning and improvement.

Canonical page: https://skillsregistry.net/skills/gamaze-labs-hicortex  
JSON: https://api.skillsregistry.net/v1/skills/gamaze-labs-hicortex

## Description

Self-learning memory for AI agents — experience captured automatically, distilled into lessons overnight, shared across your whole fleet. Works with Hermes, OpenClaw, Claude Code, and Pi.

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-26

## Source

- **Source listing:** [GitHub](https://github.com/gamaze-labs/hicortex)

## 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": "gamaze-labs-hicortex"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/gamaze-labs-hicortex` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/gamaze-labs-hicortex/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