Borrowing it
Nothing to install: this file belongs to guillermoscript/lms-front. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/guillermoscript/lms-front/master/.agents/skills/security-review/SKILL.mdgit clone --depth 1 https://github.com/guillermoscript/lms-frontWrote 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.
[](https://agentmods.dev/skills/guillermoscript/lms-front/security-review)<a href="https://agentmods.dev/skills/guillermoscript/lms-front/security-review"><img src="https://agentmods.dev/badge/skills/guillermoscript/lms-front/security-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.
<a href="https://agentmods.dev/skills/guillermoscript/lms-front/security-review"><img src="https://agentmods.dev/badge/skills/guillermoscript/lms-front/security-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00061 | $0.02839 |
| Opus 5.5 | $0.00024 | $0.01136 |
| Sonnet 5.5 | $0.00012 | $0.00568 |
| Haiku 4.5 | $0.00006 | $0.00284 |
Grade A, and why
security-review scanned grade A with 2 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(f"{settings.SEER_AUTOFIX_URL}{path}") Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
child_process.exec(user) # Node.js This is a copy
100% identical to security-review — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Review Skill
Identify exploitable security vulnerabilities in code. Report only HIGH CONFIDENCE findings—clear vulnerable patterns with attacker-controlled input.
Scope: Research vs. Reporting
CRITICAL DISTINCTION:
- Report on: Only the specific file, diff, or code provided by the user
- Research: The ENTIRE codebase to build confidence before reporting
Before flagging any issue, you MUST research the codebase to understand:
- Where does this input actually come from? (Trace data flow)
- Is there validation/sanitization elsewhere?
- How is this configured? (Check settings, config files, middleware)
- What framework protections exist?
Do NOT report issues based solely on pattern matching. Investigate first, then report only what you're confident is exploitable.
Confidence Levels
| Level | Criteria | Action |
|---|---|---|
| HIGH | Vulnerable pattern + attacker-controlled input confirmed | Report with severity |
| MEDIUM | Vulnerable pattern, input source unclear | Note as "Needs verification" |
| LOW | Theoretical, best practice, defense-in-depth | Do not report |
Do Not Flag
General Rules
- Test files (unless explicitly reviewing test security)
- Dead code, commented code, documentation strings
- Patterns using constants or server-controlled configuration
- Code paths that require prior authentication to reach (note the auth requirement instead)
Server-Controlled Values (NOT Attacker-Controlled)
These are configured by operators, not controlled by attackers:
| Source | Example | Why It's Safe |
|---|---|---|
| Django settings | settings.API_URL, settings.ALLOWED_HOSTS |
Set via config/env at deployment |
| Environment variables | os.environ.get('DATABASE_URL') |
Deployment configuration |
| Config files | config.yaml, app.config['KEY'] |
Server-side files |
| Framework constants | django.conf.settings.* |
Not user-modifiable |
| Hardcoded values | BASE_URL = "https://api.internal" |
Compile-time constants |
What ships with it
21 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- infrastructure/docker.md 9.2 KB
- languages/javascript.md 9.9 KB
- languages/python.md 9.3 KB
- LICENSE 884 B
- references/api-security.md 12 KB
- references/authentication.md 8.9 KB
- references/authorization.md 9.8 KB
- references/business-logic.md 12 KB
- references/cryptography.md 8.5 KB
- references/csrf.md 9.7 KB
- references/data-protection.md 9.3 KB
- references/deserialization.md 10.0 KB
- references/error-handling.md 11 KB
- references/file-security.md 12 KB
- references/injection.md 6.7 KB
- references/logging.md 12 KB
- references/misconfiguration.md 10 KB
- references/modern-threats.md 13 KB
- references/ssrf.md 11 KB
- references/supply-chain.md 8.9 KB
- references/xss.md 7.4 KB
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.
- 1mo ago First seen · 313 lines · 61 tokens per session scan A 6d36fa39ada8
security-review is a skill published in the GitHub repository guillermoscript/lms-front (40 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 2,839 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). It is 100% identical to security-review, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
review-animations
Reviews animation and motion code against a high craft bar derived from Emil Kowalski's design engineering philosophy. Default to flagging; approval is earned.
ship-feature
End-to-end feature delivery — implement a feature, verify builds/tests pass, open a fully-filled draft PR, then automatically push every feedback round and review iteration to the same PR. Use when the user asks to implement or build a feature, fix-and-ship a change, or iterate on an existing feature PR.
cross-model-review
A second-opinion code review that asks a different AI model to independently inspect a change, then compares both reviews. It focuses on logic, security, consistency, and omissions.
review-system-design
Review a system design someone else provided - screenshot, drawio, Mermaid, slides, doc, or IaC - by extracting it into a confirmed fact sheet, then scoring it on the nine axes.
review-ticket
Review a ticket or PR through focused specialist lenses: scope, architecture, security, tests, AC coverage, and PR metadata.
common-feedback-reporter
Pre-write audit for skill violations: checks planned code against loaded skill anti-patterns before any file write. Use when writing Flutter/Dart/TS code or editing SKILL.md files with active project skills. Load as composite; on auto-fixed violation, also load +common/common-learning-log.