Findings Report logo

Findings Report

Organization
zero-lab-ai
findings-report

Writes security findings that survive triage: reproducible, severity-justified, and fixable, in a fixed structure with a severity rubric. Use when turning a discovered vulnerability, misconfiguration, or weakness into a report entry, ticket, or disclosure.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namefindings-report
Stars
232
Forks
14
Bundled files
4
LicenseApache-2.0
Links
  • 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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zero-lab-ai on GitHub. Read the source before you install it.

Installation

Install the Findings Report 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/zero-lab-ai/skills/tree/main/plugins/engagement-operations/skills/findings-report
  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/zero-lab-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/engagement-operations/skills/findings-report .claude/skills/findings-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Findings 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 Findings 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 Findings 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.

Writing security findings

A finding is an argument: here is a flaw, here is how to see it yourself, here is what it costs you, here is what to do. Most findings fail at triage because one of those four is missing or because the severity was asserted rather than derived. This skill produces findings in a fixed shape and checks the argument before prose can hide a missing part.

When to Use

  • Turning a discovered vulnerability, misconfiguration, or weakness into a report entry, ticket, or disclosure.
  • Use only within explicit authorization and the stated rules of engagement.

When NOT to Use

  • Do not treat this skill as permission to access, test, or change a target.
  • Do not convert missing evidence into a clean result or a stronger claim.

Quick start

  1. Collect, before writing a word: the exact reproduction (input, steps, observed result), the affected component and version, and evidence (output, screenshot path, request/response). If you cannot reproduce it, it is a lead, not a finding; label it as such.
  2. Fill references/finding-template.md from the checked record. Every section is required. "N/A" is acceptable only for References.
  3. Write the fix as a concrete change, not a category. "Validate input" is a category. "Reject path values that resolve outside DATA_ROOT after os.path.realpath" is a fix.
  4. Reread the title. It should name the component and the flaw, and a reader should be able to guess the severity from it.

Finding or lead

Evidence stateReport asWhy
Reproduced flaw with bounded impactFinding with severityThe argument can be independently checked
Strong signal, reproduction incompleteUnrated leadThe signal deserves follow-up but impact is not established
Version or pattern match onlyObservation or leadApplicability is not proven by a label
Control claimed but not verifiedOpen preconditionA stated control cannot reduce severity without evidence
Work blocked by scope or safety limitLimitationBlocked work is not a negative result

Example: a response containing another user's record supports an authorization finding. A route name containing admin does not; it is a lead until access and impact are demonstrated.

Rules that keep findings credible

  • One flaw per finding. Two flaws in one entry get one fix and one severity, and one of them is wrong.
  • Reproduce in the reader's environment, not yours. Replace your paths, hostnames and tokens with the reader's or with placeholders they can fill.
  • Quote the evidence, do not describe it. Paste the response body, the log line, the stack trace. Trim, do not paraphrase.
  • Impact is what an attacker gets, not what the bug is. "SQL injection" is the bug. "Read any row in users, including password hashes, as an unauthenticated caller" is the impact.
  • Quantifiers are evidence claims. One tested account and two records prove that account reached those records. They do not prove every account, every record, or exploitation at scale. State the demonstrated set, then label broader blast radius as a hypothesis requiring coverage evidence.
  • Likelihood is about preconditions. Say what the attacker needs: network position, an account, a victim click, a race. Each precondition lowers likelihood; say so explicitly.
  • Never pad with speculation. "Could potentially lead to RCE" without a demonstrated path is a lead. Put it in Notes, not Impact.
  • Fix and mitigation are different. The fix removes the flaw. A mitigation reduces exposure while the fix is pending. Label each.

Severity in one line each

SeverityShape of the justification
CriticalUnauthenticated or low-privilege attacker gets full control or all data, no victim interaction
HighSignificant data or control, with one precondition (account, victim action, adjacent network)
MediumLimited data or control, or significant impact behind two or more preconditions
LowInformation useful for other attacks, or impact confined to the attacker's own session
InfoNo direct security impact; hygiene, defense in depth, or a deviation from stated policy

Full rubric with worked examples: references/severity-rubric.md.

Rationalizations to reject

  • "The prose makes the severity obvious." Readers can disagree; the normalized factors and review make the decision reproducible.
  • "The screenshot exists somewhere." If the record cannot point to it, triage cannot verify the claim.
  • "This lead is probably critical." Unreproduced escalation stays an unrated lead until evidence establishes its impact.
  • "The IDs are predictable, so every record is exposed." Predictability supports a follow-up hypothesis; only tested coverage or authoritative access logic supports a universal impact claim.
  • "The workaround is good enough to call a fix." A mitigation reduces exposure and states its limit; only a change that removes the flaw is a fix.

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 Findings Report AI skill do?

Writes security findings that survive triage: reproducible, severity-justified, and fixable, in a fixed structure with a severity rubric. Use when turning a discovered vulnerability, misconfiguration, or weakness into a report entry, ticket, or disclosure.

Why use Findings Report on TypingMind?

Because you install it once and use it with any model. Findings 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 Findings Report in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/engagement-operations/skills/findings-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 Findings 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 Findings Report?

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

Is the Findings Report AI skill free?

Yes. It is published on GitHub by zero-lab-ai 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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