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 frontal-labs/mcp-server --skill deployment-pipeline-designgit clone --depth 1 https://github.com/frontal-labs/mcp-serverWrote 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/frontal-labs/mcp-server/deployment-pipeline-design)<a href="https://agentmods.dev/skills/frontal-labs/mcp-server/deployment-pipeline-design"><img src="https://agentmods.dev/badge/skills/frontal-labs/mcp-server/deployment-pipeline-design/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/frontal-labs/mcp-server/deployment-pipeline-design"><img src="https://agentmods.dev/badge/skills/frontal-labs/mcp-server/deployment-pipeline-design.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 | $0.01089 |
| Opus 5.5 | $0.00023 | $0.00436 |
| Sonnet 5.5 | $0.00012 | $0.00218 |
| Haiku 4.5 | $0.00006 | $0.00109 |
Grade A, and why
deployment-pipeline-design 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 yesterday.
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.
This is a copy
100% identical to deployment-pipeline-design — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deployment Pipeline Design
Architecture patterns for multi-stage CI/CD pipelines with approval gates, deployment strategies, and environment promotion workflows.
Purpose
Design robust, secure deployment pipelines that balance speed with safety through proper stage organization, automated quality gates, and progressive delivery strategies. This skill covers both the structural design of pipeline architecture and the operational patterns for reliable production deployments.
Input / Output
What You Provide
- Application type: Language/runtime, containerized or bare-metal, monolith or microservices
- Deployment target: Kubernetes, ECS, VMs, serverless, or platform-as-a-service
- Environment topology: Number of environments (dev/staging/prod), region layout, air-gap requirements
- Rollout requirements: Acceptable downtime, rollback SLA, traffic splitting needs, canary vs blue-green preference
- Gate constraints: Approval teams, required test coverage thresholds, compliance scans (SAST, DAST, SCA)
- Monitoring stack: Prometheus, Datadog, CloudWatch, or other metrics sources used for automated promotion decisions
What This Skill Produces
- Pipeline configuration: Stage definitions, job dependencies, parallelism, and caching strategy
- Deployment strategy: Chosen rollout pattern with annotated configuration (canary weights, blue-green switchover, rolling parameters)
- Health check setup: Shallow vs deep readiness probes, post-deployment smoke test scripts
- Gate definitions: Automated metric thresholds and manual approval workflows
- Rollback plan: Automated rollback triggers and manual runbook steps
When to Use
- Design CI/CD architecture for a new service or platform migration
- Implement deployment gates between environments
- Configure multi-environment pipelines with mandatory security scanning
- Establish progressive delivery with canary or blue-green strategies
- Debug pipelines where stages succeed but production behavior is wrong
- Reduce mean time to recovery by automating rollback on metric degradation
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 107 lines · 58 tokens per session scan A 9a56bd9401c1
deployment-pipeline-design is a skill published in the GitHub repository frontal-labs/mcp-server (1 stars, last pushed yesterday), licensed Apache-2.0. It adds 58 tokens to every session and 1,089 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to deployment-pipeline-design, differing in 0 lines, and is treated as a copy.
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mcp-dependency-review
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baby-sit
Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.
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Watches a long-running command via tmux, lets sub-agent diagnose failures and propose a fix, and records the diagnosis to memory. Designed for overnight model fine-tunes, CI image builds, or repeated regression suites that may fail intermittently.
swarm-migrate
Cross-repo migration swarm — one coordinator + N parallel subagents (one per target repo) that apply the same transformation, open PRs, wait for CI, and report back to a shared JSON ledger. Coordinator handles topology, conflict auto-rebase, and stop-on-novel-failure. Use when bumping a shared dependency, rolling out…