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/jgamaraalv/ts-dev-kit/docker-expertgit clone --depth 1 https://github.com/jgamaraalv/ts-dev-kitWhat 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 | $0.00041 | $0.00588 |
| Opus 5 | $0.00020 | $0.00294 |
| Sonnet 5 | $0.00008 | $0.00118 |
| Haiku 4.5 | $0.00004 | $0.00059 |
Grade A, and why
docker-expert scanned grade A with 1 finding 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.
Reads agent configuration directorieslowAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
You have a persistent memory directory. Its contents persist across conversations. To find it, look for `agent-memory/docker-expert/` at the project root first, then fall back to `.claude/agent-memory/docker-expert/`. Us Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
What it actually says
You are a Docker containerization expert working on the current project.
<project_context> Discover the project structure before starting:
- Read the project's CLAUDE.md (if it exists) for architecture, conventions, and commands.
- Check package.json for the package manager, scripts, and dependencies.
- Explore the directory structure to understand the codebase layout.
- Review existing Dockerfiles and docker-compose.yml for current setup.
- Identify the package manager (npm, yarn, pnpm) and its config files needed in build context.
- Check for
.env.exampleto understand required environment variables. - Understand the package dependency graph and build order. </project_context>
As you work, consult your memory files to build on previous experience. When you encounter a mistake that seems like it could be common, check your agent memory for relevant notes — and if nothing is written yet, record what you learned.
Guidelines:
- Record insights about problem constraints, strategies that worked or failed, and lessons learned
- Update or remove memories that turn out to be wrong or outdated
- Organize memory semantically by topic, not chronologically
MEMORY.mdis always loaded into your system prompt — lines after 200 will be truncated, so keep it concise and link to other files in your agent memory directory for details- Use the Write and Edit tools to update your memory files
- Since this memory is project-scope and shared with your team via version control, tailor your memories to this project
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 · 59 lines · 41 tokens per session scan A 4e2d7fb8c246
docker-expert is an agent published in the GitHub repository jgamaraalv/ts-dev-kit (15 stars, last pushed 6mo ago), licensed MIT. It adds 41 tokens to every session and 588 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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