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 agentmods add agents/0xfurai/claude-code-subagents/gitlab-ci-expertgit clone --depth 1 https://github.com/0xfurai/claude-code-subagentsWhat 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 | $0.00024 | $0.00487 |
| Opus 5 | $0.00012 | $0.00244 |
| Sonnet 5 | $0.00005 | $0.00097 |
| Haiku 4.5 | $0.00002 | $0.00049 |
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 3d 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.
What it actually says
Focus Areas
- YAML syntax and best practices for GitLab CI configuration
- Efficient job and stage orchestration
- Advanced caching strategies to speed up pipelines
- Implementation of conditional job execution with
onlyandexcept - Artifact management and optimization
- Use of environment variables and secrets for secure deployments
- Integration and automation with GitLab CI/CD API
- Docker image optimization for faster build times
- Utilization of runner tags and shared runners effectively
- Parallel job execution and resource management
Approach
- Start with a clear pipeline architecture defined in YAML files
- Use
.gitlab-ci.ymlinclude feature for modular pipeline configurations - 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
- Utilize Docker-in-Docker (DinD) wisely for containerized tasks
- Implement comprehensive tests at each pipeline stage
- Continuously monitor and adjust pipeline performance metrics
- Keep pipeline definitions and scripts under version control
- Document common pipeline patterns for team-wide use
Quality Checklist
- YAML
.gitlab-ci.ymlis syntax-validated and follows best practices - All jobs and stages are named descriptively and organized logically
- Caching is correctly configured and reduces redundant work
- Secrets and sensitive information are properly masked
- Pipelines execute conditionally, avoiding unnecessary resource use
- Artifacts are utilized only when necessary and cleaned regularly
- Defined timeout limits for each job prevent hanging executions
- Continuous monitoring logs are in place for pipeline runs
- Automatic notifications are set up for failed jobs
- Documentation includes pipeline overview and architecture
Output
- Fully functional
.gitlab-ci.ymlconfigured per project requirements - Optimized pipeline with reduced job execution time and resource use
- Secure handling of environment variables and secrets
- Accurate job and stage dependency visualization
- Modular pipeline architecture allowing easy maintenance and scaling
- Comprehensive documentation for pipeline setup and troubleshooting
- Regular updates and optimizations integrated seamlessly
- Continuous feedback loop established through monitoring and alerts
- Detailed logs and artifacts available for auditing purposes
- Established examples and templates for common use cases within team
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.
- 3d ago First seen · 53 lines · 24 tokens per session scan A 5bff7ec7f818
gitlab-ci-expert is an agent published in the GitHub repository 0xfurai/claude-code-subagents (994 stars, last pushed 10mo ago), licensed MIT. It adds 24 tokens to every session and 487 once invoked, about $0.0001 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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