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 pekral/cursor-rules --skill security-threat-analysisgit clone --depth 1 https://github.com/pekral/cursor-rulesWrote 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/pekral/cursor-rules/security-threat-analysis)<a href="https://agentmods.dev/skills/pekral/cursor-rules/security-threat-analysis"><img src="https://agentmods.dev/badge/skills/pekral/cursor-rules/security-threat-analysis/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/pekral/cursor-rules/security-threat-analysis"><img src="https://agentmods.dev/badge/skills/pekral/cursor-rules/security-threat-analysis.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 | $0.00058 | $0.01958 |
| Opus 5.5 | $0.00023 | $0.00783 |
| Sonnet 5.5 | $0.00012 | $0.00392 |
| Haiku 4.5 | $0.00006 | $0.00196 |
Grade B, and why
security-threat-analysis scanned grade B with 1 finding 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.
Cloud metadata endpointmediumServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
- Fetch the referenced URL with `WebFetch` (or the available MCP equivalent). If the source is a CVE/GHSA identifier without a URL, resolve it against `https://nvd.nist.gov/vuln/detail/<CVE>` or `https://github.com/advis Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Constraints
- Apply
@rules/php/core-standards.mdc - Apply
@rules/php/dependency-selection.mdc— when the remediation playbook proposes adopting a new Composer package (e.g. a hardened replacement for a vulnerable library, or a security helper not previously installed), run the Activity gate + Compatibility gate from that rule and embed the selection note in the playbook step. Pin upgrades of an already-installed package fall under@skills/composer-update/SKILL.mddiscovery and do not need the full selection process. - Apply
@rules/security/backend.md - Apply
@rules/security/frontend.md - Apply
@rules/security/mobile.md - Apply
@rules/reports/general.mdc. When the remediation report is published as a GitHub PR comment (technical channel — the PR is the codebase tracker), it stays in canonical English per the rule's Exception — technical CR findings on the GitHub PR. When it is published as a comment on the originating tracker issue / JIRA ticket (non-technical channel), it follows the language of the source assignment. CVE / GHSA identifiers, CWE / OWASP labels, package names, and code identifiers stay verbatim regardless of the surrounding prose language. - Never include exploit payloads in a form ready for live attack; always redact secrets, PII, and identifying tokens
- Do not modify code in this skill — it produces a report only
- Do not duplicate
@skills/security-review/SKILL.md; that skill audits the whole project, this skill analyzes one referenced external threat
Use when
- The user provides a URL or identifier (CVE-…, GHSA-…, advisory link, blog post, write-up) describing a specific security threat
- The user wants a remediation report that an AI agent can act on without re-deriving the attack
- The user asks to "analyze this CVE / advisory / vulnerability" in the context of the current project
Inputs the agent must collect
Before generating the report, capture:
- Source — the URL or identifier of the threat
- Affected component (claimed) — package, framework, protocol, or pattern named in the source
- Project context — language/framework of the current repository (read from
composer.json,package.json, lockfiles) - Scope — whether the user wants threat analysis only, or analysis plus a fix plan tailored to the current project
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 · 125 lines · 58 tokens per session scan B ebf37046805c
security-threat-analysis is a skill published in the GitHub repository pekral/cursor-rules (11 stars, last pushed 19d ago), licensed MIT. It adds 58 tokens to every session and 1,958 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (cloud metadata endpoint). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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