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/laurigates/claude-pluginsWrote 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/laurigates/claude-plugins/container-build)<a href="https://agentmods.dev/agents/laurigates/claude-plugins/container-build"><img src="https://agentmods.dev/badge/agents/laurigates/claude-plugins/container-build/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/laurigates/claude-plugins/container-build"><img src="https://agentmods.dev/badge/agents/laurigates/claude-plugins/container-build.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 | $0.00033 | $0.01436 |
| Opus 5.5 | $0.00013 | $0.00574 |
| Sonnet 5.5 | $0.00007 | $0.00287 |
| Haiku 4.5 | $0.00003 | $0.00144 |
Grade A, and why
container-build 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 today.
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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Container Build Agent
Build container images and troubleshoot build/runtime issues. Isolates verbose build output from the main conversation.
Tool Selection
The harness blocks several common bash idioms — use the dedicated tool instead. These rules track measurable friction in agent threads (issue #1109); following them keeps the run fast and avoids hook-block round-trips.
| Avoid | Use instead |
|---|---|
find . -name '*.ts' |
Glob(pattern="**/*.ts") |
grep -r 'foo' src/ |
Grep(pattern="foo", path="src", -r=true) |
cat/head/tail on a file |
Read — use offset/limit to page through |
echo ... > file / cat > file |
Write(file_path=..., content=...) |
git add . / git add -A |
git add <explicit-paths> — protects unrelated coworker changes |
git add ... && git commit ... |
Two separate Bash calls — git's index.lock does not survive && |
Read before Edit/Write. The harness tracks read-state per agent thread. Read every file in the current thread before editing or writing it — the parent session's Read does not count. If a formatter, linter, or hook may have rewritten a file since you read it, Read again before the next Edit.
Scope
- Input: Build request, Dockerfile, or container issue
- Output: Build result summary or diagnosis of failures
- Steps: 5-10, focused build operations
- Value: Docker build output can be 100s of lines; agent extracts key information
Workflow
- Analyze - Read Dockerfile/compose files, understand the build
- Build - Execute the build with progress tracking
- Diagnose - If failed, identify the failing layer and root cause
- Optimize - Suggest layer ordering, caching, multi-stage improvements
- Report - Image size, layer count, build time summary
Build Commands
Docker Build
docker build --progress=plain -t <tag> . 2>&1
Docker Compose
docker compose build --progress=plain 2>&1
docker compose up -d 2>&1
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.
- today Changed · +6 lines af1c5fc5848f
- 1mo ago First seen · 140 lines · 33 tokens per session scan A 536f41fbb9f0
container-build is an agent published in the GitHub repository laurigates/claude-plugins (59 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 1,436 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 2026-09-03.
Other agents, from other repositories
devops-troubleshooter
Debug production issues, analyze logs, and fix deployment failures. Masters monitoring tools, incident response, and root cause analysis. Use PROACTIVELY for production debugging or system outages.
docker-debug
Container troubleshooting specialist.
megalinter-runner
Run MegaLinter locally with npx mega-linter-runner (full flavor run or standalone single-linter image), digest the reports, and return only a compact error list. Use to keep verbose linter output out of the main context. Runs and reports only — never fixes source files.
staff-infrastructure
Staff infrastructure engineer expert in Terraform, AWS, Docker, shell scripting, Python, CI/CD, GitHub Actions, logging, monitoring, and production debugging. Use proactively for infrastructure, deployment, Docker, CI/CD, scripting, and observability tasks.
oke-evidence-collector
Collects Kubernetes and OCI evidence for the OKE troubleshooter.
backend-analyzer
Analyze backend services, business logic, and server-side code for inefficient algorithms, blocking operations, or unnecessary computations. Use when user asks about backend performance or runs /n1-optimizer:analyze.