Security Review Pr logo

Security Review Pr

Organization
warpdotdev
security-review-pr

Audit a pull request diff for common security concerns (input validation, sanitization, authentication and authorization, secrets management, unsafe dependencies, and related risks) and fold findings into the same review.json produced by the base PR review. Use as a supplement to `review-pr` whenever a code PR is being reviewed.

Overview

Publisherwarpdotdev
Repositoryoz-for-oss
Skill namesecurity-review-pr
Stars
313
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 warpdotdev on GitHub. Read the source before you install it.

Installation

Install the Security Review Pr 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/warpdotdev/oz-for-oss/tree/main/.agents/skills/security-review-pr
  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/warpdotdev/oz-for-oss.git /tmp/oz-for-oss
mkdir -p .claude/skills
cp -r /tmp/oz-for-oss/.agents/skills/security-review-pr .claude/skills/security-review-pr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security Review Pr 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 Pr 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 Pr 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 PR Skill

Audit the current pull request for security concerns and fold any findings into the same review.json produced by the base review-pr skill.

Goal

Provide a focused security pass on top of the general PR review. This is a supplement to review-pr, not a separate output. Findings must be merged into the single combined review.json so reviewers receive one cohesive review.

Inputs

  • The working directory is the PR branch checkout.
  • The workflow usually provides an annotated diff in pr_diff.txt.
  • The workflow usually provides the PR description in pr_description.md.
  • Focus on the files and lines changed by this PR.
  • Default behavior: do not post comments or reviews to GitHub directly.

When to apply this skill

  • Apply on code PRs whenever review-pr is applied.
  • Do not apply on spec-only PRs handled by review-spec.
  • Skip the skill entirely when no changed file introduces code or configuration that touches the concerns below; it is better to stay silent than to manufacture findings.
  • Do not duplicate findings the base review-pr pass will already raise. If the base review would naturally catch an issue, leave it there rather than re-reporting it from the security pass.

Security concerns to audit

Evaluate each changed hunk against the following concerns. Treat the list as a checklist, not a ceiling — flag other clearly security-relevant issues when they appear.

Input validation and untrusted data

  • User-supplied or network-supplied input used without validation, length limits, or type checks.
  • Deserialization of untrusted data (e.g. pickle, yaml.load, eval, Function, JSON.parse feeding into a command).
  • Path traversal risk: user input concatenated into filesystem paths without normalization or an allowlist.
  • SSRF risk: user-controlled URLs passed to outbound HTTP clients without scheme or host restrictions.
  • Unbounded resource use driven by untrusted input (memory, loops, regex with catastrophic backtracking).

Output encoding and sanitization

  • Untrusted data interpolated into SQL, shell commands, HTML, Markdown, log lines, or URLs without the right encoding or parameterization.
  • Use of shell=True, string concatenation into exec/spawn, or raw SQL strings.
  • Rendering user-supplied Markdown or HTML into a UI without sanitization.

Authentication and authorization

  • Missing or weakened authentication checks on new endpoints, RPC handlers, or CLI commands.
  • Authorization checks that trust client-supplied identifiers instead of the authenticated principal.
  • Permission checks removed or made more permissive without clear justification.

Secrets management

  • Hardcoded credentials, API keys, private keys, or tokens committed in source.
  • Secrets read from insecure locations (world-readable files, logs, environment dumps printed to stdout).
  • Secrets passed via command-line arguments where they would leak into process listings or shell history.
  • Secrets written to logs, error messages, analytics events, or serialized payloads.
  • New secrets added to .env, fixtures, or test data that are real rather than clearly synthetic placeholders.

Cryptography and randomness

  • Use of weak or deprecated primitives (MD5, SHA1 for security purposes, ECB mode, RC4).
  • Hand-rolled crypto when a vetted library is available.
  • Non-cryptographic randomness (random.random, Math.random) used for tokens, IDs, or security decisions.
  • Missing integrity or authenticity checks when decrypting or verifying tokens.

Dependencies and supply chain

  • New dependencies from unknown registries or forks without a clear rationale.
  • Pinning loosened in a way that allows untrusted upgrades (e.g. * or very broad ranges on a sensitive package).
  • Fetching scripts over HTTP or piping curl to a shell inside build or CI steps.

Data handling and privacy

  • Logging or echoing personally identifiable information, auth tokens, session IDs, or request bodies that may contain them.
  • New telemetry or analytics events that capture sensitive data without redaction.
  • Expanding the scope of stored data beyond what the feature requires.

Configuration and defaults

  • New feature flags or configuration options that default to insecure values (e.g. TLS disabled, auth optional).
  • CORS, cookies, or headers weakened in scope without an explicit justification.
  • Permissive file modes on newly created files that contain sensitive material.

Process

  1. Read pr_description.md and pr_diff.txt to understand the scope and intent of the change.
  2. For each changed hunk, consider which of the concerns above could plausibly apply given the surrounding code in the checkout.
  3. Prefer evidence-based findings tied to specific changed lines. If a concern only applies to untouched code, describe it in the review summary instead of as an inline comment.
  4. Do not flag purely stylistic or non-security issues here — those belong in the base review-pr pass.
  5. Do not repeat findings already covered by the base review; if the base pass would naturally catch it, leave it there.

Outputs

  • Do not create a separate report file.
  • Fold security findings into the same review.json produced by review-pr.
  • Prefix every security finding's comment body with a [SECURITY] tag after the severity label so reviewers can tell the source of the concern. For example:
    • 🚨 [CRITICAL] [SECURITY] SQL injection: ...
    • ⚠️ [IMPORTANT] [SECURITY] Secret written to logs: ...
    • 💡 [SUGGESTION] [SECURITY] Prefer parameterized query: ...
  • In the review summary, add a dedicated ## Security subsection when there are security findings. List the most important security concerns there in addition to any inline comments. If there are no security findings, do not add the subsection.
  • Count security findings toward the existing Found: X critical, Y important, Z suggestions tally in the summary. Do not add a separate security counter.
  • Upgrade the overall verdict if a security finding materially demands it. A critical security finding should generally result in Request changes.

Severity mapping

  • 🚨 [CRITICAL] for issues that are likely exploitable in production (e.g. shell injection with attacker-controlled input, committed live secret, missing auth on a destructive endpoint).
  • ⚠️ [IMPORTANT] for plausible security weaknesses that should be fixed before merge (e.g. missing input validation on an internal-only endpoint, weak hashing for non-password data, overly broad CORS).
  • 💡 [SUGGESTION] for defense-in-depth improvements that are clearly worthwhile but not immediate risks.
  • 🧹 [NIT] is rarely appropriate for security findings; use only when the comment includes a concrete suggestion block and the issue is genuinely cosmetic.

Inline comment requirements

  • Follow the same diff-line rules as review-pr: inline comments must target lines that exist in this PR's diff.
  • Keep comments concise, direct, and actionable. Explain the threat, then the fix.
  • When proposing code changes, use the same suggestion block format as review-pr.

Boundaries

  • Do not run dynamic scans, fetch remote advisories, or call external security APIs.
  • Do not speculate about vulnerabilities that cannot be tied to the diff or the checked-out files.
  • Do not gate the PR on theoretical risks; prefer 💡 [SUGGESTION] when the risk is low or the fix is optional.
  • Do not post to GitHub directly. Your only output is the merged review.json from the base review pass.

Frequently asked questions

What does the Security Review Pr AI skill do?

Audit a pull request diff for common security concerns (input validation, sanitization, authentication and authorization, secrets management, unsafe dependencies, and related risks) and fold findings into the same review.json produced by the base PR review. Use as a supplement to `review-pr` whenever a code PR is being reviewed.

Why use Security Review Pr on TypingMind?

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

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

Which AI models can use Security Review Pr?

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 Pr?

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

Is the Security Review Pr AI skill free?

Yes. It is published on GitHub by warpdotdev 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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