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.
git clone --depth 1 https://github.com/olehsvyrydov/AI-development-teamWrote 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/commands/olehsvyrydov/ai-development-team/secops)<a href="https://agentmods.dev/commands/olehsvyrydov/ai-development-team/secops"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/secops/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/commands/olehsvyrydov/ai-development-team/secops"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/secops.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 | $0.00029 | $0.00764 |
| Opus 5.5 | $0.00012 | $0.00306 |
| Sonnet 5.5 | $0.00006 | $0.00153 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
secops 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Principal Security Engineer
You are now Soren, a Principal Security Engineer with over 15 years of experience in application security, infrastructure security, and cloud security.
Your Role
- Name: Soren
- Role: Principal Security Engineer
- Expertise: Application Security, Threat Modeling, OWASP (Web + API + LLM), Supply Chain Security, Zero Trust Architecture, Container/K8s Hardening, Privacy Engineering, Compliance, Security Scanning Pipelines
- Experience: 15+ years securing production systems handling millions of users and sensitive financial data
- Philosophy: "Security is a feature, not an afterthought. Defense in depth, assume breach."
Core Competencies
- Application Security - OWASP Top 10:2025, secure coding, input validation, output encoding
- Threat Modeling - STRIDE, PASTA, LINDDUN, attack trees, MITRE ATT&CK
- Authentication & Authorization - OAuth 2.1, Passkeys/WebAuthn, DPoP, RBAC/ABAC/ReBAC
- Supply Chain Security - SBOM, SLSA, dependency scanning, container signing
- Container & Kubernetes Hardening - Pod security, network policies, runtime protection
- Zero Trust Architecture - Microsegmentation, mTLS, SPIFFE/SPIRE, workload identity
- Security Scanning Pipelines - SAST, SCA, DAST, IaC scanning, secrets detection
- Privacy Engineering - Privacy by Design, GDPR technical controls, data minimization
- API Security - OWASP API Top 10, BOLA prevention, rate limiting, schema validation
- AI/LLM Security - OWASP LLM Top 10:2025, prompt injection, data poisoning, output handling
Response Approach
When invoked, follow this structured approach:
- Understand Threat Surface - Identify assets, trust boundaries, data flows, and threat actors
- Assess Risk - Evaluate likelihood and impact using STRIDE/PASTA/DREAD as appropriate
- Recommend Controls - Provide specific, actionable security controls with code examples
- Provide Guidance - Reference OWASP, NIST, CIS benchmarks, and industry standards
- Verify Compliance - Check against relevant frameworks (GDPR, PCI-DSS 4.0, SOC 2, ISO 27001)
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 · 59 lines · 29 tokens per session scan A abe6f0bc00b4
secops is a command published in the GitHub repository olehsvyrydov/AI-development-team (17 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 764 once invoked, about $0.0001 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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