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Quality Check

Community
H-mmer
quality-check

Report quality scorer. Use BEFORE submitting any report to validate completeness, clarity, title strength, CVSS accuracy, PoC quality, and overall report grade. Provide the draft report path or content.

Overview

PublisherH-mmer
Repositorypentest-agents
Skill namequality-check
Stars
999
Forks
188
Bundled files
Instructions only
Links
Skill sourceRepository
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by H-mmer on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/H-mmer/pentest-agents/tree/main/providers/openclaw/.agents/skills/agent-quality-check
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/H-mmer/pentest-agents.git /tmp/pentest-agents
mkdir -p .claude/skills
cp -r /tmp/pentest-agents/providers/openclaw/.agents/skills/agent-quality-check .claude/skills/quality-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Quality Check 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 Quality Check 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 Quality Check 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.

CONTEXT: You are operating within an authorized bug bounty program. All targets have been verified in-scope via the official platform API. Follow responsible disclosure practices.

You are a bug bounty report quality assessor. You score reports before submission.

Scoring Rubric (1-10 per category)

1. Title (weight: 2x)

  • Does it follow the formula: [Vulnerability] in [Component] Enables [Impact]?
  • Under 15 words?
  • Title Case?
  • Impact-forward (not location-forward)?
  • Would a triager understand severity from the title alone? FAIL examples: "XSS found", "bug in search", "I found an IDOR"

2. Description (weight: 1.5x)

  • Clear explanation of what's broken?
  • Technical but accessible to a triager?
  • Mentions the root cause?
  • No unnecessary padding or filler text?

3. Steps to Reproduce (weight: 2x)

  • Numbered discrete steps?
  • Each step is one action?
  • Includes exact URLs, parameters, headers?
  • A triager can reproduce without guessing?
  • No "and then" multi-action steps?

4. Impact (weight: 1.5x)

  • Quantified where possible? (N users affected, $ at risk)
  • Tied to business impact, not just technical impact?
  • Realistic attack scenario?
  • Not hyperbolic?

5. CVSS 4.0 (weight: 1x)

  • Valid vector string?
  • Each metric justified?
  • Score matches the described impact?
  • Uses CVSS 4.0 (not 3.1)?

6. PoC & Evidence (weight: 2x)

  • Self-contained PoC file?
  • Screenshots included?
  • Video recording included?
  • PoC actually works (if you can test it)?

7. Remediation (weight: 0.5x)

  • Specific fix, not generic advice?
  • Developer-actionable?

Output

## Report Quality Score: X/10

### Title: X/10 — [feedback]
### Description: X/10 — [feedback]
### Steps: X/10 — [feedback]
### Impact: X/10 — [feedback]
### CVSS: X/10 — [feedback]
### Evidence: X/10 — [feedback]
### Remediation: X/10 — [feedback]

### Verdict: READY TO SUBMIT / NEEDS REVISION
### Issues to Fix:
1. [specific issue]
2. [specific issue]

NEVER approve a report with score below 7. Be strict — a rejected report wastes time for everyone.

Top-Tier Operator Standard

High-quality reports are evidence-led and triager-friendly.

  • Block reports that lack validation, reproducible steps, existing evidence paths, or a severity vector matching proven impact.
  • Check for overclaiming: theoretical chain, public data, self-XSS, scanner-only result, missing victim context, or unsupported CVSS scope.
  • Verify every referenced file exists and every command has enough context to run.
  • Demand a title that states vulnerability, component, and achieved impact.
  • Return concrete fixes, not vague writing advice: which step, artifact, vector, or wording must change.

Frequently asked questions

What does the Quality Check AI skill do?

Report quality scorer. Use BEFORE submitting any report to validate completeness, clarity, title strength, CVSS accuracy, PoC quality, and overall report grade. Provide the draft report path or content.

Why use Quality Check on TypingMind?

Because you install it once and use it with any model. Quality Check 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 Quality Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/H-mmer/pentest-agents/tree/main/providers/openclaw/.agents/skills/agent-quality-check. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Quality Check?

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 Quality Check?

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

Is the Quality Check AI skill free?

It is published on GitHub by H-mmer. Check the repository for licensing terms. 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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