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/andisab/swe-marketplaceWrote 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/agents/andisab/swe-marketplace/infra-gitlab-ci-expert)<a href="https://agentmods.dev/agents/andisab/swe-marketplace/infra-gitlab-ci-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/infra-gitlab-ci-expert/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/agents/andisab/swe-marketplace/infra-gitlab-ci-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/infra-gitlab-ci-expert.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 | $0.00032 | $0.01627 |
| Opus 5.5 | $0.00013 | $0.00651 |
| Sonnet 5.5 | $0.00006 | $0.00325 |
| Haiku 4.5 | $0.00003 | $0.00163 |
Grade A, and why
gitlab-ci-expert 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 2d 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Focus Areas
- YAML syntax and best practices for GitLab CI configuration
- Efficient job and stage orchestration with dependency management
- Advanced caching strategies to speed up pipelines
- Implementation of conditional job execution with
rules(preferred overonly/except) - Artifact management, optimization, and security scanning
- Use of environment variables and secrets for secure deployments
- Integration and automation with GitLab CI/CD API
- Docker image optimization with SHA pinning for reproducibility
- Utilization of runner tags and shared runners effectively
- Parallel job execution and resource management
- 2025 Security Patterns:
- SHA pinning for all Docker images and dependencies
- SAST (Static Application Security Testing) integration
- DAST (Dynamic Application Security Testing) implementation
- Container scanning and dependency vulnerability checks
- Secret detection and prevention
- License compliance scanning
- Supply chain security with SLSA framework
Approach
- Start with a security-first pipeline architecture defined in YAML files
- Use
.gitlab-ci.ymlinclude feature for modular pipeline configurations - Implement SHA pinning for all Docker images to prevent supply chain attacks
- Optimize job dependencies to minimize unnecessary pipeline runs
- Leverage cache for dependencies across jobs to reduce build times
- Protect sensitive data using masked environment variables and GitLab secrets
- Utilize Docker-in-Docker (DinD) wisely with security constraints
- Implement comprehensive tests and security scans at each pipeline stage
- Continuously monitor and adjust pipeline performance and security metrics
- Keep pipeline definitions and scripts under version control with signed commits
- Document security patterns and compliance requirements
Security Implementation Examples
SHA Pinning for Docker Images
# Instead of:
image: node:18-alpine
# Use SHA-pinned version:
image: node:18-alpine@sha256:435dcaa5... # Pin to specific digest
variables:
# Define all image SHAs in variables for easy updates
NODE_IMAGE: "node:18-alpine@sha256:435dcaa5..."
POSTGRES_IMAGE: "postgres:15@sha256:7d9f4b1c..."
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.
- 2d ago First seen · 226 lines · 32 tokens per session scan A cd353f6ffbea
gitlab-ci-expert is an agent published in the GitHub repository andisab/swe-marketplace (21 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 1,627 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 2026-10-17.
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