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 shawnpang/startup-founder-skills --skill security-reviewgit clone --depth 1 https://github.com/shawnpang/startup-founder-skillsWrote 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/shawnpang/startup-founder-skills/security-review)<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/security-review"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/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/shawnpang/startup-founder-skills/security-review"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/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 | $0.00050 | $0.01750 |
| Opus 5 | $0.00025 | $0.00875 |
| Sonnet 5 | $0.00010 | $0.00350 |
| Haiku 4.5 | $0.00005 | $0.00175 |
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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- security-review — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Review
When to Use
- The user wants a security audit of their application, infrastructure, or specific feature
- They need a threat model before launching or a penetration test preparation review
- They have a dependency vulnerability alert and need remediation guidance
- They are handling sensitive data (PII, payment, health) and need verification
- Code audit, secrets detection, or compliance assessment is requested
Context Required
From startup-context: tech stack, deployment environment, compliance requirements, data types. Also ask:
- Scope — Full app, feature, auth system, single PR, infrastructure, or cloud environment
- Data types — PII, payment, health, credentials, or other sensitive data handled
- Compliance requirements — SOC 2, HIPAA, PCI-DSS, GDPR, ISO 27001
- Authorization — Confirm written authorization exists before any active testing
Workflow
Follow a five-phase methodology. Automated scanning precedes manual review. Authorization verification is mandatory before active testing.
- Scope definition — Establish attack surface boundaries. Identify all components, data flows, and trust boundaries. Confirm authorization. Define in-scope and out-of-scope.
- Automated scanning — Execute tooling before manual review:
- SAST:
semgrep --config=autoacross the codebase - Dependency audit:
npm audit/pip-audit/govulncheck/trivy fs . - Secrets detection: Scan for hardcoded credentials, API keys, tokens in source
- Container scanning:
trivy imagefor containerized deployments - Record all automated findings for validation in the next phase.
- SAST:
- Manual code review — Conduct contextual analysis that automated tools miss:
- Authentication and authorization flow tracing end-to-end
- Business logic vulnerabilities (price manipulation, race conditions, privilege escalation)
- Data flow analysis for sensitive information (where does PII enter, transit, and persist?)
- STRIDE threat modeling against each component and data flow
- Validation and classification — Test findings and assign severity:
- Validate automated findings to eliminate false positives
- Assign CVSS v3.1 scores; assess exploitability in context
- Classify by business impact, not just technical severity
- Reporting — Document vulnerabilities with precise locations, business impact, and corrective actions. Deliver a prioritized remediation roadmap.
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.
- 12d ago First seen · 152 lines · 50 tokens per session scan A b6c434d7ae90
security-review is a skill published in the GitHub repository shawnpang/startup-founder-skills (321 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 1,750 once invoked, about $0.0003 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 2026-08-30.
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