Finding Report

Organization

SpecterOps

finding-report

Generate a structured pentest finding report in markdown. Use when documenting a vulnerability, writing up a finding, or creating engagement notes.

Overview

Publisher	SpecterOps
Repository	`skills`
Skill name	`finding-report`
Stars	706
Forks	80
Bundled files	3
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by SpecterOps on GitHub. Read the source before you install it.

Installation

Install the Finding Report AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/SpecterOps/skills/tree/main/plugins/report-drafting/skills/finding-report
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/SpecterOps/skills/tree/main/plugins/report-drafting/skills/finding-report

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/SpecterOps/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/report-drafting/skills/finding-report .claude/skills/finding-report

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finding Report in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Finding Report on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Finding Report is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pentest Finding Report

Generate a structured markdown finding report suitable for inclusion in a professional penetration test report.

Parse the user's input to determine the finding title and severity:

$finding-report "Kerberoastable Service Account with Path to DA" → create finding with default High severity
$finding-report "Open S3 Bucket" --severity critical → create finding with specified severity
$finding-report → ask for a title

Steps

Create the output directory if it doesn't exist (mkdir -p findings/) and create a new file at findings/<title-slugified>.md (convert title to kebab-case)
Use this template structure:

markdown
# [Title]

| Field | Value |
|---|---|
| **Severity** | [Critical/High/Medium/Low/Info] |
| **CVSS 3.1** | [Score] ([Vector String]) |
| **CWE** | [CWE-ID: Name] |
| **Affected Asset(s)** | [hosts, URLs, or services] |
| **Status** | Open |

## Description

[2-3 sentences explaining what the vulnerability is and where it exists. Be specific about the affected component.]

## Impact

[What can an attacker achieve by exploiting this? Be concrete — data exposure, lateral movement, privilege escalation, etc.]

## Evidence

[Screenshots, command output, or proof. Use fenced code blocks for command output.]

[command or request used]

[response or output showing the vulnerability]



## Reproduction Steps

1. [Step-by-step instructions to reproduce]
2. [Be specific enough that another tester could verify]
3. [Include exact commands, URLs, parameters]

## Remediation

**Short-term**: [Immediate mitigation]

**Long-term**: [Root cause fix]

## References

- [Relevant CVE, blog post, or documentation]

Fill in the template based on the information provided. If information is missing, insert [TODO: ...] placeholders
For CVSS scoring, calculate based on the described impact:
- Network-accessible + no auth + high impact = Critical (9.0+)
- Requires auth or local access + high impact = High (7.0-8.9)
- Limited impact or requires chaining = Medium (4.0-6.9)
- Minor information disclosure = Low (0.1-3.9)
Map to appropriate CWE IDs (common ones):
- SQL Injection: CWE-89
- XSS: CWE-79
- Command Injection: CWE-78
- Path Traversal: CWE-22
- SSRF: CWE-918
- Broken Auth: CWE-287
- IDOR: CWE-639
- Weak Crypto: CWE-327
- Kerberoasting: CWE-916
- ACL Misconfiguration: CWE-732
- Certificate Template Abuse: CWE-295
Report the file path back to the user

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Finding Report AI skill do?

Generate a structured pentest finding report in markdown. Use when documenting a vulnerability, writing up a finding, or creating engagement notes.

Why use Finding Report on TypingMind?

Because you install it once and use it with any model. Finding Report is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Finding Report in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SpecterOps/skills/tree/main/plugins/report-drafting/skills/finding-report. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Finding Report?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Finding Report?

As many as you like. As long as a model supports skills, you can use Finding Report with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Finding Report AI skill free?

Yes. It is published on GitHub by SpecterOps under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Pentest Finding Report

Steps

Bundled files

Frequently asked questions

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