Borrowing it
Nothing to install: this file belongs to sagar-shirwalkar/collibra-atlas. 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/sagar-shirwalkar/collibra-atlas/main/.agents/skills/security-review/SKILL.mdgit clone --depth 1 https://github.com/sagar-shirwalkar/collibra-atlasWrote 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/sagar-shirwalkar/collibra-atlas/security-review)<a href="https://agentmods.dev/skills/sagar-shirwalkar/collibra-atlas/security-review"><img src="https://agentmods.dev/badge/skills/sagar-shirwalkar/collibra-atlas/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/sagar-shirwalkar/collibra-atlas/security-review"><img src="https://agentmods.dev/badge/skills/sagar-shirwalkar/collibra-atlas/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 | $0.00102 | $0.01518 |
| Opus 5 | $0.00051 | $0.00759 |
| Sonnet 5 | $0.00020 | $0.00304 |
| Haiku 4.5 | $0.00010 | $0.00152 |
Grade A, and why
security-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 11d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
An AI-powered security scanner that reasons about your codebase the way a human security researcher would — tracing data flows, understanding component interactions, and catching vulnerabilities that pattern-matching tools miss.
Leading words
- Scope — Determine the attack surface: which files, languages, frameworks, and entry points are in play. A scoped review of
src/auth/is sharper than a whole-repo skim. - Audit — Check dependencies (known CVEs), scan for hardcoded secrets (API keys, tokens, credentials), then deep-scan for injection, auth, crypto, and business logic flaws. Each layer catches things the others miss.
- Trace — Follow user-controlled input from entry points (HTTP params, headers, file uploads) all the way to sinks (DB queries, exec calls, HTML output). The most dangerous bugs span multiple files.
- Verify — Self-check every finding: is it actually exploitable, or is there sanitization you missed? Downgrade or discard false positives before reporting.
Phases
PHASE 1: Scope & dependency audit
Completion criterion: project languages and frameworks identified, dependencies checked for known CVEs, and the dependency audit logged.
- Identify the language(s) and framework(s) in use (check
pyproject.toml,requirements.txt,package.json,go.mod,Cargo.toml,pom.xml,Gemfile). - Read
references/language-patterns.mdto load framework-specific vulnerability patterns. - Audit dependencies for known vulnerable packages. Read
references/vulnerable-packages.mdfor the curated watchlist. - Flag packages with known CVEs, deprecated crypto libs, or suspiciously old pinned versions.
PHASE 2: Secrets & exposure scan
Completion criterion: all files scanned for hardcoded secrets, credentials, and sensitive data exposure. Findings logged with file paths.
- Scan ALL files (including config,
.env, CI/CD, Dockerfiles, IaC) for hardcoded API keys, tokens, passwords, private keys. - Check for committed
.envfiles, secrets in comments or debug logs, cloud credentials (AWS, GCP, Azure, Stripe, etc.). - Read
references/secret-patterns.mdfor regex patterns and entropy heuristics.
What ships with it
5 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.
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.
- 11d ago First seen · 109 lines · 102 tokens per session scan A b05238f822cf
security-review is a skill published in the GitHub repository sagar-shirwalkar/collibra-atlas (0 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 102 tokens to every session and 1,518 once invoked, about $0.0005 per session on Opus 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 2026-08-31.
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