A security review performed before coding begins. It examines a task description, identifies the technologies involved, and assesses risks such as unsafe authentication, payments, personal data, file uploads, or database changes.

Use it to review an upcoming feature or change for security risks. It is useful for tasks involving web frameworks, databases, authentication, cloud infrastructure, APIs, or sensitive data.

It helps reveal security concerns before implementation makes them harder to fix. If no task is provided, it asks for a description of what is being built.

How Srajangpt1/ai-security-crew configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to Srajangpt1/ai-security-crew. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/Srajangpt1/ai-security-crew/main/.claude/commands/sec-review.md

Clone the repo

git clone --depth 1 https://github.com/Srajangpt1/ai-security-crew

Made for: Claude Code.

Or install mcp-security-review, the plugin that ships this one along with the rest of its 3 skills, 3 commands, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/commands/srajangpt1/ai-security-crew/sec-review/github.svg)](https://agentmods.dev/commands/srajangpt1/ai-security-crew/sec-review)

Your own site

<a href="https://agentmods.dev/commands/srajangpt1/ai-security-crew/sec-review"><img src="https://agentmods.dev/badge/commands/srajangpt1/ai-security-crew/sec-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/commands/srajangpt1/ai-security-crew/sec-review"><img src="https://agentmods.dev/badge/commands/srajangpt1/ai-security-crew/sec-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,192 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00000	$0.01192
Opus 5	$0.00000	$0.00596
Sonnet 5	$0.00000	$0.00238
Haiku 4.5	$0.00000	$0.00119

Measured 10d ago against content hash 74a2487eb31b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

sec-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/commands/sec-review.md · 147 lines

How it starts

The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Perform a pre-coding security review for the following task:

$ARGUMENTS

Instructions

Analyze the task description above and produce a structured security assessment. If no task is provided, ask the user to describe what they are building and optionally their tech stack.

Step 1 — Identify Technologies

Detect technologies from the description. Look for:

Languages: Python, JavaScript, TypeScript, Java, Go, Ruby, PHP, Rust, C#
Frameworks: Django, FastAPI, Flask, Express, Next.js, Spring, Rails, Laravel
Databases: PostgreSQL, MySQL, MongoDB, Redis, SQLite, DynamoDB
Auth: JWT, OAuth2, SAML, session-based, API keys
Infrastructure: AWS, GCP, Azure, Docker, Kubernetes
Other: GraphQL, REST API, gRPC, WebSockets, message queues

Step 2 — Assess Risk Level

Determine risk level based on what the task involves:

Critical — Any of: payments/financial transactions, healthcare/PHI, authentication/auth system, cryptographic key management, admin functionality, privilege escalation, multi-tenant data isolation

High — Any of: PII collection/storage, file uploads, external API integrations, session management, password handling, OAuth flows, database schema changes, rate limiting

Medium — Any of: user-generated content, search functionality, data exports, email/notification systems, third-party SDKs, internal APIs

Low — Static content, read-only public data, internal tooling with no sensitive data

Step 3 — Identify Security Categories

Select all applicable categories from this list:

authentication — login, registration, password reset, MFA
authorization — access control, roles, permissions, IDOR
data_validation — input validation, sanitization, output encoding
cryptography — encryption, hashing, key management, TLS
api_security — endpoints, rate limiting, CORS, versioning
web_security — XSS, CSRF, clickjacking, CSP, SRI
database — SQL injection, ORM, connection security, migrations
secrets_management — credentials, env vars, vaults, rotation
error_handling — information disclosure, stack traces, error codes
logging — audit trails, sensitive data in logs, monitoring
cloud_security — IAM, S3 permissions, VPC, security groups
supply_chain_security — dependencies, lockfiles, package integrity

Read the full file on GitHub · 147 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

10d ago First seen · 147 lines · 0 tokens per session scan A 74a2487eb31b

Subscribe to this mod's changes

sec-review is a command published in the GitHub repository Srajangpt1/ai-security-crew (68 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,192 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Security

ai-security-crew: Command for Claude Code

Borrowing it

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

Instructions

Step 1 — Identify Technologies

Step 2 — Assess Risk Level

Step 3 — Identify Security Categories

What this file has done since we first saw it

Other commands, from other repositories

checklist

clarify

specify

analyze

converge

implement