# HCE

> Use this tool when you need to efficiently retrieve relevant information from a large knowledge base, solving problems like information overload and context switching in AI assistants. It takes in user queries and context as inputs, and outputs relevant memories and information, leveraging an entity graph, semantic tree, and focus buffer. Ideal for use cases where AI assistants require smart memory management to provide accurate and contextual responses.

Canonical page: https://skillsregistry.net/skills/nakurian-hce  
JSON: https://api.skillsregistry.net/v1/skills/nakurian-hce

## Description

A smart memory system for AI assistants that retrieves relevant memories using an entity graph, semantic tree, and focus buffer, with context budgeting.

## Trust

- **Trust score (0–1):** 0.52
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ri2qejgjal)
- **Repository:** <https://github.com/nakurian/hce>

## 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": "nakurian-hce"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/nakurian-hce` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/nakurian-hce/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