Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add UnitOneAI/SecuritySkills --skill secure-code-reviewgit clone --depth 1 https://github.com/UnitOneAI/SecuritySkillsWrote 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/unitoneai/securityskills/secure-code-review)<a href="https://agentmods.dev/skills/unitoneai/securityskills/secure-code-review"><img src="https://agentmods.dev/badge/skills/unitoneai/securityskills/secure-code-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/unitoneai/securityskills/secure-code-review"><img src="https://agentmods.dev/badge/skills/unitoneai/securityskills/secure-code-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 | $0.00074 | $0.06546 |
| Opus 5.5 | $0.00030 | $0.02618 |
| Sonnet 5.5 | $0.00015 | $0.01309 |
| Haiku 4.5 | $0.00007 | $0.00655 |
Grade A, and why
secure-code-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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 579 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Secure Code Review
A structured, repeatable process for performing security-focused code review grounded in OWASP Application Security Verification Standard (ASVS) 4.0.3 and the CWE Top 25 Most Dangerous Software Weaknesses (2024 edition). This skill produces findings with traceable control IDs, severity ratings, and actionable remediation guidance.
Step 1: Scope and Language Identification
If a target is provided via arguments, focus the review on: $ARGUMENTS
Before examining any code, establish the review boundary.
- Identify the languages and frameworks present in the changeset (Python, JavaScript/TypeScript, Go, Java, etc.).
- Catalog the modules under review -- list every file path and its primary responsibility (route handler, data model, utility, middleware, configuration).
- Determine trust boundaries -- mark where user-controlled data enters the system (HTTP parameters, headers, file uploads, message queues, environment variables).
- Note dependencies -- third-party libraries that handle security-sensitive operations (auth libraries, ORM layers, crypto packages, templating engines).
- Map ASVS sections to scope -- based on what the code does, select which ASVS chapters (V1 through V14) are applicable to this review.
Gate: Do not proceed until the language, trust boundaries, and applicable ASVS sections are documented. This prevents scope creep and ensures coverage.
Step 2: Input Validation and Injection Review
ASVS Reference: V5 -- Validation, Sanitization and Encoding CWE Coverage: CWE-79 (XSS), CWE-89 (SQL Injection), CWE-78 (OS Command Injection), CWE-22 (Path Traversal), CWE-77 (Command Injection), CWE-20 (Improper Input Validation)
2.1 Controls to Verify
| ASVS Control | Description |
|---|---|
| V5.1.1 | Input validation is applied on a trusted service layer, not solely client-side |
| V5.1.3 | All input is validated against an allowlist of permitted characters or patterns |
| V5.2.1 | All HTML form output is properly encoded to prevent reflected XSS |
| V5.2.2 | Unstructured data is sanitized to enforce safety and allowed characters |
| V5.3.1 | Output encoding is relevant for the interpreter context (HTML, JS, URL, CSS, SQL) |
| V5.3.4 | Data selection or database queries use parameterized queries or ORM |
| V5.3.7 | The application protects against LDAP injection |
| V5.3.8 | The application protects against OS command injection |
| V5.5.1 | Serialized objects use integrity checks or encryption to prevent hostile object creation |
What ships with it
1 file 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.
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 · 579 lines · 74 tokens per session scan A f18fb31b59ac
secure-code-review is a skill published in the GitHub repository UnitOneAI/SecuritySkills (89 stars, last pushed 4mo ago), licensed MIT. It adds 74 tokens to every session and 6,546 once invoked, about $0.0003 per session on Opus 5.5. 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.
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