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Deepsec

OrganizationPopular
vercel-labs
deepsec

Run deepsec, an AI-powered cyber-security vulnerability scanner. Activates when the user invokes /deepsec, asks to run deepsec, or wants to scan their repo, branch, or uncommitted changes for vulnerabilities.

Overview

Publishervercel-labs
Repositorydeepsec
Skill namedeepsec
Stars
8K
Forks
485
Bundled files
386
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.

  • 386 bundled files

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

  • Open source

    Published by vercel-labs on GitHub. Read the source before you install it.

Installation

Install the Deepsec 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/vercel-labs/deepsec.git \
  .claude/skills/deepsec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deepsec 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 Deepsec 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 Deepsec 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.

/deepsec — scan this repository with deepsec

deepsec is an AI-powered vulnerability scanner.

  • Modern AI models are great at security code review.
  • Most review solutions only run on pull requests.
  • That means most legacy code was never reviewed, and your code from 6 months ago was reviewed by older models.
  • Instead deepsec does security review on ALL of your existing code using scaled VM fanout.
  • Vercel's deepsec is open source, runs in your own infrastructure, and supports strong agent sandboxing.

A fast regex scan flags candidate files, then AI agents investigate each candidate in depth and record findings with severity ratings; findings can later be revalidated, triaged, and exported. Everything it adds to a repository lives in a single .deepsec/ workspace (config, installed package, per-project data). Because processing runs real AI agents, it costs money in proportion to how much code it investigates.

Follow this runbook when the user invokes /deepsec or asks for a scan.

1. Ask for scope first

Ask the user which scope to process, using a structured question tool if you have one (AskUserQuestion in Claude Code), otherwise plain text:

  • Uncommitted changes — working tree + untracked files
  • Diff to main — changes vs origin/main (use main if there is no origin remote)
  • Entire codebase — warn that this is the expensive option: AI processing investigates every candidate file and can cost real money on a large repository

Ask before doing anything else so the rest of the flow can run unattended.

2. Detect onboarding state

From the repository root:

  • No .deepsec/deepsec.config.ts → not onboarded. Do step 3 in full.
  • .deepsec/ exists but .deepsec/node_modules/deepsec is missing, or a previous setup was interrupted → re-run the init command from step 3; it resumes from checkpoints and repairs the install rather than starting over.
  • Otherwise → onboarded; skip to step 4.

3. Onboard without full processing

Onboarding normally ends with an AI processing pass over the whole repository. Since the user already chose a scope, stop setup after the coverage phase — that still includes install, login, threat model, matcher generation, and the final regex scan, but skips the full-repo AI process phase. The scoped processing happens in step 4 instead.

From the repository root, inspect the read-only plan, then run setup:

bash
npx -y deepsec init --plan --output json
npx -y deepsec init --yes --through coverage --output jsonl

Parse every output line as JSON. On a needs_input event, show the supplied message and actions to the user rather than inventing remediation. In particular, VERCEL_AUTH_REQUIRED normally asks the user to run npx vercel login; after they do, follow the returned link action from inside .deepsec (use npx vercel link when the user needs to choose a project), then re-run the same init command. Exit code 2 means input is needed; exit code 3 means a cost/duration boundary stopped the resumable run — re-running the same command resumes it. Never expose credential values, bypass --yes prompts on the user's behalf beyond the flag itself, or launch an interactive login yourself.

4. Run the scoped processing

Run from inside .deepsec/ (the config loader only finds deepsec.config.ts in the current directory or its ancestors; after step 3, npx deepsec resolves to the copy installed there):

ScopeCommand
Uncommitted changescd .deepsec && npx deepsec process --diff-working
Diff to maincd .deepsec && npx deepsec process --diff origin/main
Entire codebase, right after step 3cd .deepsec && npx deepsec process (the final scan from setup already produced the candidate set)
Entire codebase, previously onboardedcd .deepsec && npx deepsec scan && npx deepsec process

5. Interpret results

  • Direct-mode (--diff*) exit codes: 0 = no net-new findings, 1 = at least one net-new finding (not an error), anything else = runtime error. Pre-existing findings on touched files are excluded from the gate.
  • Summarize any findings for the user, then offer follow-ups (all from inside .deepsec/): npx deepsec report, npx deepsec revalidate, and npx deepsec export --format md-dir --out ./findings.

Going deeper

After onboarding, full documentation ships with the installed package at .deepsec/node_modules/deepsec/dist/docs/getting-started.md, reviewing-changes.md (direct mode, exit codes, CI gating), configuration.md, models.md, and more. Read the relevant doc before varying the commands above; flags and defaults change between releases.

Bundled files

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

and 140 more files.

Frequently asked questions

What does the Deepsec AI skill do?

Run deepsec, an AI-powered cyber-security vulnerability scanner. Activates when the user invokes /deepsec, asks to run deepsec, or wants to scan their repo, branch, or uncommitted changes for vulnerabilities.

Why use Deepsec on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel-labs/deepsec/tree/main. 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 Deepsec?

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 Deepsec?

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

Is the Deepsec AI skill free?

Yes. It is published on GitHub by vercel-labs 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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