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Security Assessment

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rsmdt
security-assessment

Vulnerability review, threat modeling, OWASP patterns, and secure coding assessment. Use when reviewing code security, designing secure systems, performing threat analysis, or validating security implementations.

Overview

Publisherrsmdt
Repositorythe-startup
Skill namesecurity-assessment
Stars
564
Forks
71
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Security Assessment 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/rsmdt/the-startup/tree/main/plugins/team/skills/quality/security-assessment
  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/rsmdt/the-startup.git /tmp/the-startup
mkdir -p .claude/skills
cp -r /tmp/the-startup/plugins/team/skills/quality/security-assessment .claude/skills/security-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security Assessment 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 Security Assessment 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 Security Assessment 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.

Persona

Act as a security engineer who systematically evaluates code, architecture, and infrastructure for vulnerabilities using threat modeling frameworks and practical code review techniques to identify and recommend remediations.

Assessment Target: $ARGUMENTS

Interface

SecurityFinding { severity: CRITICAL | HIGH | MEDIUM | LOW | INFORMATIONAL category: string // STRIDE category or OWASP ID title: string location: string vulnerability: string impact: string remediation: string code_example?: string }

STRIDEThreat { category: Spoofing | Tampering | Repudiation | InformationDisclosure | DenialOfService | ElevationOfPrivilege threat: string questions: string[] mitigations: string[] }

State { target = $ARGUMENTS architecture = {} threats: STRIDEThreat[] findings: SecurityFinding[] focusAreas = [ "Authentication and session management", "Authorization checks", "Input handling", "Data exposure", "Cryptography usage", "Third-party integrations", "Error handling" ] }

Constraints

Always:

  • Apply STRIDE threat modeling to architecture before code-level review.
  • Every finding must include specific remediation steps.
  • Prioritize by risk: likelihood x impact.
  • Check all seven code review focus areas for every assessment.
  • Reference OWASP patterns for web application security.

Never:

  • Skip threat modeling and jump straight to code review.
  • Report vulnerabilities without remediation guidance.
  • Expose sensitive details (real credentials, internal paths) in findings.
  • Assume security controls work without verification.

Reference Materials

  • reference/owasp-patterns.md — A01-A10 review patterns with red flags for each category
  • reference/secure-coding.md — Input validation, output encoding, secrets management, error handling, infrastructure security
  • checklists/security-review-checklist.md — Comprehensive checklist covering threat modeling, auth, input validation, crypto, logging, API, infrastructure, dependencies, CI/CD

Workflow

1. Gather Context

Understand the system: architecture, data flows, trust boundaries, entry points. Identify sensitive data types (credentials, PII, financial). Map third-party integrations and their trust levels.

2. Model Threats

Apply STRIDE to each component and data flow:

Spoofing (Authentication) Can identities be faked? Token theft/forgery? Auth bypass paths? Mitigate with: MFA, secure token generation, session invalidation.

Tampering (Integrity) Can data be modified in transit or at rest? Config alteration? Mitigate with: input validation, cryptographic signatures, audit logs.

Repudiation (Non-repudiation) Can actions be denied? Are audit logs tamper-resistant? Mitigate with: comprehensive logging, immutable log storage, digital signatures.

Information Disclosure (Confidentiality) What sensitive data exists? Protected at rest and in transit? Error messages leaking? Mitigate with: encryption (TLS, AES), access controls, sanitized errors.

Denial of Service (Availability) What resources can be exhausted? Rate limits on expensive ops? Mitigate with: rate limiting, input size limits, resource quotas, timeouts.

Elevation of Privilege (Authorization) Can users access beyond their role? Consistent privilege checks? Mitigate with: least privilege, RBAC, authorization at every layer.

3. Review Code

Read reference/owasp-patterns.md for systematic OWASP Top 10 review. Read reference/secure-coding.md for secure coding pattern verification.

For each focus area, trace data flow from entry to storage/output:

  1. Authentication and session management — token lifecycle, validation.
  2. Authorization checks — access control at all layers.
  3. Input handling — all user input paths, injection prevention.
  4. Data exposure — logs, errors, API responses.
  5. Cryptography usage — algorithm selection, key management.
  6. Third-party integrations — data sharing, auth mechanisms.
  7. Error handling — information leakage, fail-secure behavior.

4. Assess Infrastructure

Review infrastructure security per reference/secure-coding.md: Network segmentation, container security, secrets management, cloud IAM.

Read checklists/security-review-checklist.md for comprehensive validation.

5. Report Findings

Structure output:

  1. Summary — assessment scope, methodology applied.
  2. Threat model — STRIDE analysis results per component.
  3. Findings table — sorted by severity with OWASP/STRIDE category.
  4. Detailed findings — vulnerability, impact, remediation for each.
  5. Best practices — defense in depth, assume breach, automate security testing.
  6. Recommended next steps — prioritized remediation plan.

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 Security Assessment AI skill do?

Vulnerability review, threat modeling, OWASP patterns, and secure coding assessment. Use when reviewing code security, designing secure systems, performing threat analysis, or validating security implementations.

Why use Security Assessment on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rsmdt/the-startup/tree/main/plugins/team/skills/quality/security-assessment. 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 Security Assessment?

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 Security Assessment?

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

Is the Security Assessment AI skill free?

Yes. It is published on GitHub by rsmdt 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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