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Fp Check

OrganizationPopular
trailofbits
fp-check

Systematically verifies suspected security bugs to eliminate false positives, producing a TRUE POSITIVE or FALSE POSITIVE verdict with documented evidence for each. Use when asked whether a specific finding is real, exploitable, or a false positive, or to verify or validate a suspected vulnerability — not for hunting or discovering new bugs.

Overview

Publishertrailofbits
Repositoryskills
Skill namefp-check
Stars
7.1K
Forks
611
Bundled files
8
LicenseCC-BY-SA-4.0
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.

  • 8 bundled files

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

  • Open source

    Published by trailofbits on GitHub. Read the source before you install it.

Installation

Install the Fp 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.
  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/trailofbits/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/fp-check/skills/fp-check .claude/skills/fp-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fp 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 Fp 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 Fp 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.

False Positive Check

When to Use

  • "Is this bug real?" or "is this a true positive?"
  • "Is this a false positive?" or "verify this finding"
  • "Check if this vulnerability is exploitable"
  • Any request to verify or validate a specific suspected bug

When NOT to Use

  • Finding or hunting for bugs ("find bugs", "security analysis", "audit code")
  • General code review for style, performance, or maintainability
  • Feature development, refactoring, or non-security tasks
  • When the user explicitly asks for a quick scan without verification

Rationalizations to Reject

If you catch yourself thinking any of these, STOP.

RationalizationWhy It's WrongRequired Action
"Rapid analysis of remaining bugs"Every bug gets full verificationReturn to task list, verify next bug through all phases
"This pattern looks dangerous, so it's a vulnerability"Pattern recognition is not analysisComplete data flow tracing before any conclusion
"Skipping full verification for efficiency"No partial analysis allowedExecute all steps per the chosen verification path
"The code looks unsafe, reporting without tracing data flow"Unsafe-looking code may have upstream validationTrace the complete path from source to sink
"Similar code was vulnerable elsewhere"Each context has different validation, callers, and protectionsVerify this specific instance independently
"This is clearly critical"LLMs are biased toward seeing bugs and overrating severityComplete devil's advocate review; prove it with evidence

Step 0: Understand the Claim and Context

Before any analysis, restate the bug in your own words. If you cannot do this clearly, ask the user for clarification. Half of false positives collapse at this step — the claim doesn't make coherent sense when restated precisely.

Document:

  • What is the exact vulnerability claim? (e.g., "heap buffer overflow in parse_header() when content_length exceeds 4096")
  • What is the alleged root cause? (e.g., "missing bounds check before memcpy at line 142")
  • What is the supposed trigger? (e.g., "attacker sends HTTP request with oversized Content-Length header")
  • What is the claimed impact? (e.g., "remote code execution via controlled heap corruption")
  • What is the threat model? What privilege level does this code run at? Is it sandboxed? What can the attacker already do before triggering this bug? (e.g., "unauthenticated remote attacker vs privileged local user"; "runs inside Chrome renderer sandbox" vs "runs as root with no sandbox")
  • What is the bug class? Classify the bug and consult bug-class-verification.md for class-specific verification requirements that supplement the generic phases below.
  • Execution context: When and how is this code path reached during normal execution?
  • Caller analysis: What functions call this code and what input constraints do they impose?
  • Architectural context: Is this part of a larger security system with multiple protection layers?
  • Historical context: Any recent changes, known issues, or previous security reviews of this code area?

Route: Standard vs Deep Verification

After Step 0, choose a verification path.

Standard Verification

Use when ALL of these hold:

  • Clear, specific vulnerability claim (not vague or ambiguous)
  • Single component — no cross-component interaction in the bug path
  • Well-understood bug class (buffer overflow, SQL injection, XSS, integer overflow, etc.)
  • No concurrency or async involved in the trigger
  • Straightforward data flow from source to sink

Follow standard-verification.md. No task tracking — work through the linear checklist sequentially, documenting findings inline.

Deep Verification

Use when ANY of these hold:

  • Ambiguous claim that could be interpreted multiple ways
  • Cross-component bug path (data flows through 3+ modules or services)
  • Race conditions, TOCTOU, or concurrency in the trigger mechanism
  • Logic bugs without a clear spec to verify against
  • Standard verification was inconclusive or escalated
  • User explicitly requests full verification

Follow deep-verification.md. Track each phase as a task with explicit dependencies, and execute the phases using the plugin's analysis agents.

Default

Start with standard. Standard verification has two built-in escalation checkpoints that route to deep when complexity exceeds the linear checklist.

Batch Triage

When verifying multiple bugs at once:

  1. Run Step 0 for all bugs first — restating each claim often collapses obvious false positives immediately
  2. Route each bug independently (some may be standard, others deep)
  3. Process all standard-routed bugs first, then deep-routed bugs
  4. After all bugs are verified, check for exploit chains — findings that individually failed gate review may combine to form a viable attack

Final Summary

After processing ALL suspected bugs, provide:

  1. Counts: X TRUE POSITIVES, Y FALSE POSITIVES
  2. TRUE POSITIVE list: Each with brief vulnerability description
  3. FALSE POSITIVE list: Each with brief reason for rejection

References

  • Standard Verification — Linear single-pass checklist for straightforward bugs
  • Deep Verification — Full task-based orchestration for complex bugs
  • Gate Reviews — Six mandatory gates and verdict format
  • Bug-Class Verification — Class-specific verification requirements for memory corruption, logic bugs, race conditions, integer issues, crypto, injection, info disclosure, DoS, and deserialization
  • False Positive Patterns — 13-item checklist and red flags for common false positive patterns
  • Evidence Templates — Documentation templates for data flow, mathematical proofs, attacker control, and devil's advocate reviews

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 Fp Check AI skill do?

Systematically verifies suspected security bugs to eliminate false positives, producing a TRUE POSITIVE or FALSE POSITIVE verdict with documented evidence for each. Use when asked whether a specific finding is real, exploitable, or a false positive, or to verify or validate a suspected vulnerability — not for hunting or discovering new bugs.

Why use Fp Check on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trailofbits/skills/tree/main/plugins/fp-check/skills/fp-check. 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 Fp 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 Fp Check?

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

Is the Fp Check AI skill free?

Yes. It is published on GitHub by trailofbits under the CC-BY-SA-4.0 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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