# ProcWitness

> ProcWitness — teknesyum-procwitness. Use this tool when you need to investigate and analyze process behavior on Windows, Linux, and macOS systems. ProcWitness solves problems related to cybersecurity and incident response by recording process activity and generating AI-ready evidence bundles. It takes system process data as input and produces a comprehensive evidence bundle as output, ideal for use in forensic analysis and threat detection contexts.

Canonical page: https://skillsregistry.net/skills/teknesyum-procwitness  
JSON: https://api.skillsregistry.net/v1/skills/teknesyum-procwitness

## Description

Passive process forensics for Windows, Linux and macOS. Records process behavior locally and produces an AI-ready evidence bundle.

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** AGPL-3.0
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/Teknesyum/ProcWitness)

## 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": "teknesyum-procwitness"
    }
  }
}
```

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