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Sast Analysis

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utkusen
sast-analysis

Perform codebase analysis and architecture mapping as the first phase of a security assessment. Explores the tech stack, frameworks, entry points, data flows, and trust boundaries. Outputs sast/architecture.md. Run this before any vulnerability detection skill. Use when asked to analyze a codebase for security or when sast/architecture.md does not yet exist.

Overview

Publisherutkusen
Repositorysast-skills
Skill namesast-analysis
Stars
1.3K
Forks
64
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Sast Analysis 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/utkusen/sast-skills/tree/main/sast-files/.agents/skills/sast-analysis
  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/utkusen/sast-skills.git /tmp/sast-skills
mkdir -p .claude/skills
cp -r /tmp/sast-skills/sast-files/.agents/skills/sast-analysis .claude/skills/sast-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sast Analysis 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 Sast Analysis 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 Sast Analysis 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.

Codebase Analysis

You are performing the first phase of a security assessment. Your goal is to deeply understand the codebase. You are NOT looking for specific vulnerabilities yet. This is pure reconnaissance.

Create a sast/ folder in the project root (if it doesn't already exist). This phase produces one output file inside it:

sast/architecture.md — technology stack, architecture, entry points, data flows

Phase 1: Technology Reconnaissance

Explore the codebase and identify:

  • Languages: All programming languages used and their versions if specified
  • Frameworks: Web frameworks, ORM layers, template engines, task queues
  • Package managers & dependencies: Lock files, dependency manifests (package.json, requirements.txt, go.mod, Gemfile, pom.xml, etc.)
  • Infrastructure hints: Dockerfiles, docker-compose, Kubernetes manifests, Terraform, CI/CD configs
  • Databases: SQL, NoSQL, cache layers, message brokers — look at connection strings, ORM models, migration files
  • Authentication & authorization: Auth libraries, middleware, session configs, OAuth/OIDC providers, JWT usage, API key patterns
  • External integrations: Third-party APIs, payment processors, email services, cloud SDKs, webhook handlers
  • Entry points: HTTP routes, GraphQL schemas, gRPC service definitions, CLI commands, WebSocket handlers, scheduled jobs, message consumers

Start by reading dependency manifests, project configs, and directory structure. Then drill into source code to confirm findings.

Phase 2: Architecture Mapping

Based on Phase 1, build a mental model of:

  1. Service boundaries: Is this a monolith or microservices? What talks to what?
  2. Data flow: How does user input enter the system, get processed, get stored, and get returned?
  3. Trust boundaries: Where does the system transition between trusted and untrusted contexts? (e.g., user input -> backend, backend -> database, service -> service, server -> client)
  4. Privilege levels: What roles/permissions exist? How are they enforced? Is there an admin panel?
  5. Sensitive data inventory: PII, credentials, tokens, financial data, health records — where is each stored and how does it move?

Write the results of Phase 1 and Phase 2 to sast/architecture.md. Use this format:

markdown
# Architecture: [Project Name]

## Technology Stack

| Category | Details |
|---|---|
| Languages | ... |
| Frameworks | ... |
| Databases | ... |
| Auth mechanism | ... |
| Infrastructure | ... |
| External services | ... |

## Architecture Overview

[Describe the architecture: monolith vs microservices, how components interact,
main modules and their responsibilities]

## Data Flow

[Trace how user input enters the system, gets processed, stored, and returned.
Cover the primary flows (e.g., registration, login, core business actions).]

## Entry Points

| Entry Point | Type | Auth Required | Description |
|---|---|---|---|
| ... | HTTP/GraphQL/WS/etc. | Yes/No | ... |

## Trust Boundaries

[List each trust boundary and what crosses it]

## Sensitive Data Inventory

| Data Type | Where Stored | How Accessed | Protection |
|---|---|---|---|
| ... | ... | ... | ... |

Important Reminders

  • Do NOT report specific vulnerabilities (like "line 42 has SQL injection"). That comes in later phases.
  • Be thorough in exploration. Read actual source code, not just config files. Look at how auth middleware is applied, how queries are built, how file uploads are handled.
  • If the codebase is large, prioritize security-sensitive areas: auth, payment, data access, file handling, admin functionality.

Frequently asked questions

What does the Sast Analysis AI skill do?

Perform codebase analysis and architecture mapping as the first phase of a security assessment. Explores the tech stack, frameworks, entry points, data flows, and trust boundaries. Outputs sast/architecture.md. Run this before any vulnerability detection skill. Use when asked to analyze a codebase for security or when sast/architecture.md does not yet exist.

Why use Sast Analysis on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/utkusen/sast-skills/tree/main/sast-files/.agents/skills/sast-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sast Analysis?

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 Sast Analysis?

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

Is the Sast Analysis AI skill free?

Yes. It is published on GitHub by utkusen under the MIT 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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