sample-oh-my-aidlcops is a plugin marketplace for Claude Code and Kiro that packages practices for managing the AWS AI-Driven Development Lifecycle, including design correctness and agent safety. It is for teams using agents to develop and operate AWS systems with approval checkpoints. Its catalogue contains the marketplace's plugins, skills, agents, and commands.
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/aws-samples/sample-oh-my-aidlcopsWrote 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/commands/aws-samples/sample-oh-my-aidlcops/oma-platform-review)<a href="https://agentmods.dev/commands/aws-samples/sample-oh-my-aidlcops/oma-platform-review"><img src="https://agentmods.dev/badge/commands/aws-samples/sample-oh-my-aidlcops/oma-platform-review/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/commands/aws-samples/sample-oh-my-aidlcops/oma-platform-review"><img src="https://agentmods.dev/badge/commands/aws-samples/sample-oh-my-aidlcops/oma-platform-review.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 | $0.00066 | $0.01038 |
| Opus 5 | $0.00033 | $0.00519 |
| Sonnet 5 | $0.00013 | $0.00208 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
oma-platform-review 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 9d 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.
The source is not reproduced here
Licensed MIT-0
The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 9d ago First seen · 102 lines · 66 tokens per session scan A 122a2a2b3e9c
oma-platform-review is a command published in the GitHub repository aws-samples/sample-oh-my-aidlcops (19 stars, last pushed 2d ago), licensed MIT-0. It adds 66 tokens to every session and 1,038 once invoked, about $0.0003 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.
Other commands, from other repositories
aws-kb
Read-only AWS knowledge lookup — query docs, CLI reference, SOPs, comparisons, onboarding pointers, and next-step recommendations from the kb MCP server. Stateless per turn; never writes spec artefacts.
aws
Run the claude-aws-architect orchestrator on a feature prompt — vibe-classifies depth and either answers directly or fans out L3 specialists in parallel and merges their outputs.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.