# dgr

> dgr — sapenov-dgr. Use this tool when you need to generate audit-ready decision artifacts for Large Language Model (LLM) outputs, ensuring transparency and accountability by documenting assumptions and risks. It solves problems related to regulatory compliance, model explainability, and trustworthiness. The tool takes LLM outputs as input and produces structured decision artifacts as output, ideal for use cases requiring high levels of transparency and governance.

Canonical page: https://skillsregistry.net/skills/sapenov-dgr  
JSON: https://api.skillsregistry.net/v1/skills/sapenov-dgr

## Description

Audit-ready decision artifacts for LLM outputs — assumptions, risks,.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/sapenov-dgr)

## 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": "sapenov-dgr"
    }
  }
}
```

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