awslabs/agent-plugins is a collection of plugins, skills, instructions, an MCP server, a hook, and a setting that guide AI coding agents in architecting, deploying, and operating software on Amazon Web Services. It is for developers using coding agents such as Claude Code, Codex, and Cursor with AWS projects. The catalogue entries are the repository's own agent workflows and supporting components.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/awslabs/agent-pluginsnpx agentmods add skills/awslabs/agent-plugins/document-serviceWrote 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/awslabs/agent-plugins/document-service)<a href="https://agentmods.dev/skills/awslabs/agent-plugins/document-service"><img src="https://agentmods.dev/badge/skills/awslabs/agent-plugins/document-service/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/awslabs/agent-plugins/document-service"><img src="https://agentmods.dev/badge/skills/awslabs/agent-plugins/document-service.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00141 | $0.03536 |
| Opus 5.5 | $0.00056 | $0.01414 |
| Sonnet 5.5 | $0.00028 | $0.00707 |
| Haiku 4.5 | $0.00014 | $0.00354 |
Grade A, and why
document-service 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 1mo 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document Service
Analyze codebases to produce structured technical documentation and architecture diagrams with source-of-truth citations. Every finding links back to the exact file and line it was derived from. Optimized for AWS workloads but works with any codebase.
Core Principles
- Explain WHY, not just WHAT. The reader inherited this codebase and has zero context. Listing components is not enough — explain why the architecture is shaped this way. Search for code comments, TODOs, and commit messages that reveal design rationale. When no rationale exists, mark it
[RATIONALE UNKNOWN]. - Trace end-to-end flows. For every API endpoint or message handler, trace the complete request path from entry to response. Note every intermediate step, transformation, timeout, and failure point. This is the "if it breaks at 3am, where do I look?" analysis.
- Deep-dive complex logic. Identify the most complex or domain-specific code paths (ML pipelines, business rule engines, state machines, custom algorithms). Document HOW they work at the implementation level — the algorithm, key parameters, edge cases, and where production bugs will occur. Surface-level summaries of complex code provide no value over a naive AI prompt.
- Surface implicit knowledge. Look for hardcoded values, magic numbers, environment-dependent behavior, and undocumented assumptions. These are the tribal knowledge items that disappear when teams leave.
- Every claim must be traceable. Include
file:linecitations for every finding. See citation-format.md. Verify citations precisely — re-read the cited file and confirm the line number is within ±3 lines. Anchor with function/variable names. - Code is the source of truth. Document what actually exists in code, not what READMEs or wikis claim. Flag every discrepancy between documentation and reality.
- Mark unknowns and risks explicitly. Use
[UNKNOWN]for items not inferable from code,[RISK]for unhandled failure modes,[INFERRED]for educated guesses,[RATIONALE UNKNOWN]for unexplained architecture choices. Omitting markers undermines trust. - Verify quantitative claims. List directory entries programmatically and use exact counts.
What ships with it
8 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.
- references/business-context.md 2.7 KB
- references/citation-format.md 3.7 KB
- references/discovery-patterns.md 7.8 KB
- references/error-scenarios.md 1.7 KB
- references/exclusion-patterns.md 3.9 KB
- references/framework-patterns.md 14 KB
- references/recursive-analysis.md 3.9 KB
- references/technical-doc-template.md 7.7 KB
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.
- 1mo ago First seen · 216 lines · 141 tokens per session scan A 5a43cae02e5d
document-service is a skill published in the GitHub repository awslabs/agent-plugins (924 stars, last pushed 2d ago), licensed Apache-2.0. It adds 141 tokens to every session and 3,536 once invoked, about $0.0006 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 2026-08-30.
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