Well-Architected Skills & Steering for AI Coding Agents is a collection of playbooks that teaches coding agents to apply the AWS Well-Architected Framework while software is being developed. Developers use it to receive local, contextual architecture guidance across supported AI coding tools without requiring AWS credentials or API calls. The catalogue add-ons are the skills, commands, agents, and instructions that deliver these playbooks to coding agents.
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 aws-samples/sample-well-architected-skills-and-steering --skill aws-well-architected-framework-reviewgit clone --depth 1 https://github.com/aws-samples/sample-well-architected-skills-and-steeringWrote 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/aws-samples/sample-well-architected-skills-and-steering/aws-well-architected-framework-review)<a href="https://agentmods.dev/skills/aws-samples/sample-well-architected-skills-and-steering/aws-well-architected-framework-review"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-well-architected-skills-and-steering/aws-well-architected-framework-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/skills/aws-samples/sample-well-architected-skills-and-steering/aws-well-architected-framework-review"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-well-architected-skills-and-steering/aws-well-architected-framework-review.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 | $0.00050 | $0.10404 |
| Opus 5 | $0.00025 | $0.05202 |
| Sonnet 5 | $0.00010 | $0.02081 |
| Haiku 4.5 | $0.00005 | $0.01040 |
Grade A, and why
aws-well-architected-framework-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 13d 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 ships with it
60 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.
- DESIGN.md 6.6 KB
- evals/evals.json 11 KB
- evals/triggering.json 2.7 KB
- metadata-devops-agent.json 1.7 KB
- metadata.json 2.5 KB
- references/lenses/agentic-ai/AGENTCOST01.md 32 KB
- references/lenses/agentic-ai/AGENTCOST02.md 34 KB
- references/lenses/agentic-ai/AGENTCOST03.md 22 KB
- references/lenses/agentic-ai/AGENTCOST04.md 23 KB
- references/lenses/agentic-ai/AGENTCOST05.md 31 KB
- references/lenses/agentic-ai/AGENTCOST06.md 21 KB
- references/lenses/agentic-ai/AGENTCOST07.md 25 KB
- references/lenses/agentic-ai/AGENTOPS01.md 26 KB
- references/lenses/agentic-ai/AGENTOPS02.md 32 KB
- references/lenses/agentic-ai/AGENTOPS03.md 31 KB
- references/lenses/agentic-ai/AGENTOPS04.md 21 KB
- references/lenses/agentic-ai/AGENTOPS05.md 32 KB
- references/lenses/agentic-ai/AGENTOPS06.md 22 KB
- references/lenses/agentic-ai/AGENTOPS07.md 26 KB
- references/lenses/agentic-ai/AGENTPERF01.md 40 KB
- references/lenses/agentic-ai/AGENTPERF02.md 58 KB
- references/lenses/agentic-ai/AGENTPERF03.md 56 KB
- references/lenses/agentic-ai/AGENTPERF04.md 17 KB
- references/lenses/agentic-ai/AGENTPERF05.md 30 KB
- references/lenses/agentic-ai/AGENTPERF06.md 19 KB
- references/lenses/agentic-ai/AGENTPERF07.md 12 KB
- references/lenses/agentic-ai/AGENTREL01.md 29 KB
- references/lenses/agentic-ai/AGENTREL02.md 29 KB
- references/lenses/agentic-ai/AGENTREL03.md 23 KB
- references/lenses/agentic-ai/AGENTREL04.md 24 KB
- references/lenses/agentic-ai/AGENTREL05.md 18 KB
- references/lenses/agentic-ai/AGENTREL06.md 27 KB
- references/lenses/agentic-ai/AGENTREL07.md 17 KB
- references/lenses/agentic-ai/AGENTREL08.md 23 KB
- references/lenses/agentic-ai/AGENTSEC01.md 27 KB
- references/lenses/agentic-ai/AGENTSEC02.md 30 KB
- references/lenses/agentic-ai/AGENTSEC03.md 36 KB
- references/lenses/agentic-ai/AGENTSEC04.md 23 KB
- references/lenses/agentic-ai/AGENTSEC05.md 22 KB
- references/lenses/agentic-ai/AGENTSEC06.md 32 KB
- references/lenses/agentic-ai/AGENTSEC07.md 37 KB
- references/lenses/agentic-ai/AGENTSEC08.md 17 KB
- references/lenses/agentic-ai/AGENTSEC09.md 36 KB
- references/lenses/agentic-ai/AGENTSUS01.md 40 KB
- references/lenses/agentic-ai/AGENTSUS02.md 28 KB
- references/lenses/agentic-ai/AGENTSUS03.md 29 KB
- references/lenses/connected-mobility/cost-optimization.md 43 KB
- references/lenses/connected-mobility/operational-excellence.md 26 KB
- references/lenses/connected-mobility/performance-efficiency.md 42 KB
- references/lenses/connected-mobility/reliability.md 49 KB
- references/lenses/connected-mobility/security.md 94 KB
- references/lenses/connected-mobility/sustainability.md 18 KB
- references/lenses/container-build/cost-optimization.md 15 KB
- references/lenses/container-build/operational-excellence.md 13 KB
- references/lenses/container-build/performance-efficiency.md 9.9 KB
- references/lenses/container-build/reliability.md 11 KB
- references/lenses/container-build/security.md 11 KB
- references/lenses/container-build/sustainability.md 7.8 KB
- references/lenses/data-analytics/cost-optimization.md 25 KB
- references/lenses/data-analytics/operational-excellence.md 14 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.
- 13d ago First seen · 691 lines · 50 tokens per session scan A 1912b839a7af
aws-well-architected-framework-review is a skill published in the GitHub repository aws-samples/sample-well-architected-skills-and-steering (258 stars, last pushed 16d ago), licensed MIT-0. It adds 50 tokens to every session and 10,404 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.
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