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Security Review

Community
shawnpang
security-review

When the user needs a security assessment — threat modeling, vulnerability review, auth flow audit, dependency scanning, or says "is this secure", "review for vulnerabilities", "threat model", "security audit", "pen test prep".

Overview

Publishershawnpang
Repositorystartup-founder-skills
Skill namesecurity-review
Stars
368
Forks
43
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 shawnpang on GitHub. Read the source before you install it.

Installation

Install the Security Review 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/shawnpang/startup-founder-skills/tree/main/skills/security-review
  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/shawnpang/startup-founder-skills.git /tmp/startup-founder-skills
mkdir -p .claude/skills
cp -r /tmp/startup-founder-skills/skills/security-review .claude/skills/security-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security Review 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 Review 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 Review 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.

Security Review

When to Use

  • The user wants a security audit of their application, infrastructure, or specific feature
  • They need a threat model before launching or a penetration test preparation review
  • They have a dependency vulnerability alert and need remediation guidance
  • They are handling sensitive data (PII, payment, health) and need verification
  • Code audit, secrets detection, or compliance assessment is requested

Context Required

From startup-context: tech stack, deployment environment, compliance requirements, data types. Also ask:

  • Scope — Full app, feature, auth system, single PR, infrastructure, or cloud environment
  • Data types — PII, payment, health, credentials, or other sensitive data handled
  • Compliance requirements — SOC 2, HIPAA, PCI-DSS, GDPR, ISO 27001
  • Authorization — Confirm written authorization exists before any active testing

Workflow

Follow a five-phase methodology. Automated scanning precedes manual review. Authorization verification is mandatory before active testing.

  1. Scope definition — Establish attack surface boundaries. Identify all components, data flows, and trust boundaries. Confirm authorization. Define in-scope and out-of-scope.
  2. Automated scanning — Execute tooling before manual review:
    • SAST: semgrep --config=auto across the codebase
    • Dependency audit: npm audit / pip-audit / govulncheck / trivy fs .
    • Secrets detection: Scan for hardcoded credentials, API keys, tokens in source
    • Container scanning: trivy image for containerized deployments
    • Record all automated findings for validation in the next phase.
  3. Manual code review — Conduct contextual analysis that automated tools miss:
    • Authentication and authorization flow tracing end-to-end
    • Business logic vulnerabilities (price manipulation, race conditions, privilege escalation)
    • Data flow analysis for sensitive information (where does PII enter, transit, and persist?)
    • STRIDE threat modeling against each component and data flow
  4. Validation and classification — Test findings and assign severity:
    • Validate automated findings to eliminate false positives
    • Assign CVSS v3.1 scores; assess exploitability in context
    • Classify by business impact, not just technical severity
  5. Reporting — Document vulnerabilities with precise locations, business impact, and corrective actions. Deliver a prioritized remediation roadmap.

Output Format

markdown
# Security Review: [Scope Description]

## Executive Summary
Overall risk posture (Critical / High / Medium / Low), top findings count, and business impact summary.

## Threat Model (STRIDE)
| Threat | Category | Asset | Impact | Likelihood | Risk |

## Findings
### Critical / High / Medium / Low
- **[SEC-N] Title** — CVSS X.X — file:line — description, business impact, remediation with code example

## Auth Flow Assessment
End-to-end trace of authentication and authorization with findings.

## Dependency Vulnerabilities
| Package | Current Version | CVSS | Fix Version | Exploitable in Context? |

## Remediation Roadmap
Prioritized action list with timelines.

Frameworks & Best Practices

STRIDE Threat Modeling

Apply to every component and data flow:

  • Spoofing — Can attackers forge tokens or impersonate users? Are API keys rotatable?
  • Tampering — Can requests be modified in transit? Are webhooks signed? Is data integrity verified?
  • Repudiation — Are critical actions logged? Are logs tamper-evident?
  • Information Disclosure — Stack traces in error responses? PII encrypted at rest and in transit?
  • Denial of Service — Rate limits in place? Can one user exhaust resources for all?
  • Elevation of Privilege — Can regular users access admin functions? Are role checks server-side?

OWASP Top 10 Checks

  1. Injection — Parameterize SQL/NoSQL; check OS commands, SSTI, LDAP
  2. Broken Auth — argon2id/bcrypt, session timeout, rate limiting on login
  3. Data Exposure — TLS 1.2+, PII encrypted at rest, HSTS headers
  4. XXE — Disable DTD processing, prefer JSON over XML
  5. Access Control — Server-side authz on every endpoint, no IDOR, CORS whitelist
  6. Misconfig — Debug mode off, default credentials removed, security headers present
  7. XSS — Output encoding, Content Security Policy, HTTP-only cookies
  8. Deserialization — Validate schema, prefer JSON, reject untrusted serialized objects
  9. Vulnerable Deps — npm audit, pip-audit, trivy, govulncheck
  10. Logging — Auth events, admin actions, access violations logged with alerts

CVSS v3.1 Scoring Guide

  • Critical (9.0-10.0): RCE, auth bypass, full data breach, complete system compromise
  • High (7.0-8.9): Privilege escalation, significant data exposure, SSRF to internal services
  • Medium (4.0-6.9): Stored XSS, CSRF, limited IDOR, information disclosure
  • Low (0.1-3.9): Missing security headers, minor info disclosure, verbose errors

Auth Flow Checklist

  • Passwords: argon2id or bcrypt (cost >= 10)
  • JWT: 15-min access tokens, 7-day refresh tokens rotated on use
  • Rate limiting: 5 attempts / 15 min on auth endpoints
  • Sessions invalidated on password change
  • OAuth state parameter validated, scoped API keys
  • MFA enforced for admin accounts
  • Password reset tokens are single-use and time-limited

Scanning Tools

  • SAST: semgrep --config=auto (all stacks), bandit (Python), gosec (Go), eslint-plugin-security (Node)
  • Dependencies: npm audit / pip-audit / govulncheck / trivy fs .
  • Containers: trivy image

Mandatory Constraints

  • Never test production without explicit written authorization
  • Never exploit beyond proof-of-concept demonstration
  • Always sequence automated scanning before manual review

Remediation Priority

  1. Actively exploitable + critical data — immediately
  2. Auth/authz bypass — 24 hours
  3. Injection — 48 hours
  4. Data exposure / critical CVEs — 1 week
  5. Config hardening — 2 weeks
  6. Defense-in-depth — next sprint

Related Skills

  • code-review — chain when findings require code-level fixes and review
  • architecture-design — chain when findings reveal architectural security flaws
  • soc2-prep — chain when review is part of compliance preparation

Examples

Example prompt: "Review the security of our user authentication system. We use JWT with Express."

Good output snippet:

# Security Review: JWT Authentication System

## Executive Summary
Risk posture: **Critical**. Hardcoded JWT secret and non-expiring tokens.

## Findings
### Critical (CVSS 9.8)
- **[SEC-1] Hardcoded JWT secret** — auth/config.js:3 — Secret is
  "supersecret123". Attacker can forge any token.
  **Fix:** Move to env var, generate with `openssl rand -base64 64`.

### Critical (CVSS 9.1)
- **[SEC-2] Tokens never expire** — auth/jwt.js:12 — No `expiresIn`.
  **Fix:** Set `expiresIn: '15m'`, implement refresh token rotation.

Frequently asked questions

What does the Security Review AI skill do?

When the user needs a security assessment — threat modeling, vulnerability review, auth flow audit, dependency scanning, or says "is this secure", "review for vulnerabilities", "threat model", "security audit", "pen test prep".

Why use Security Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shawnpang/startup-founder-skills/tree/main/skills/security-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Security Review?

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 Review?

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

Is the Security Review AI skill free?

Yes. It is published on GitHub by shawnpang 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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