# payloadlab

> payloadlab — cognis-digital-payloadlab. Use this tool when you need to analyze static malicious payloads, such as PE, ELF, LNK, macro, and OneNote files, to identify potential security threats. It solves problems related to malware detection and reverse engineering, providing insights into the composition and behavior of malicious files. The tool takes various file types as input and outputs detailed analysis reports, making it a valuable asset in cybersecurity contexts.

Canonical page: https://skillsregistry.net/skills/cognis-digital-payloadlab  
JSON: https://api.skillsregistry.net/v1/skills/cognis-digital-payloadlab

## Description

Static malicious payload analyzer — PE/ELF/LNK/macro/OneNote

## 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:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/cognis-digital/payloadlab)

## 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": "cognis-digital-payloadlab"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-payloadlab` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-payloadlab/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