AWS Startups is an official AWS repository containing plugins, skills, tools, and other resources for people building startup products on Amazon Web Services. Its add-ons support startup-focused architecture, migration, and development work on AWS.
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 awslabs/startups --skill agentcore-patternsgit clone --depth 1 https://github.com/awslabs/startupsWrote 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/startups/agentcore-patterns)<a href="https://agentmods.dev/skills/awslabs/startups/agentcore-patterns"><img src="https://agentmods.dev/badge/skills/awslabs/startups/agentcore-patterns/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/startups/agentcore-patterns"><img src="https://agentmods.dev/badge/skills/awslabs/startups/agentcore-patterns.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 | $0.00198 | $0.00806 |
| Opus 5.5 | $0.00079 | $0.00322 |
| Sonnet 5.5 | $0.00040 | $0.00161 |
| Haiku 4.5 | $0.00020 | $0.00081 |
Grade A, and why
agentcore-patterns 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentCore Patterns
Apply these patterns to agents that act inside an engineering pipeline rather than serving end users. Upstream aws-agents owns the build, deploy, and hardening mechanics. What it does not cover is the judgment layer, which is where these agents actually fail.
Treat one constraint as primary: nobody is available to babysit the agent. A judge that is wrong, slow, or noisy will not be tuned over a quarter by a platform team. It gets ignored, and then it is worse than nothing, because it occupies the slot where review attention used to be.
Design for that outcome. Measure stability before granting an agent authority to block, report a stance on every axis so silence is never ambiguous, and compute in code anything the judgment depends on.
Reference files
references/git-code-reviewer-agent.md: Read before letting any agent's output carry weight in someone else's work. End-to-end wiring for a pull-request reviewer, from the credential identity that decides whether a verdict can be recorded at all, through reading the pull request as data, the trigger that reaches a credentialed runtime, what the agent should remember between runs, and the judgment design that decides whether anyone keeps reading the output. Written as field notes, including the failures.
Upstream skills to defer to
Do not restate the mechanics these own. Invoke them directly:
Skill("aws-agents:agents-build"): Agent construction, tools, prompts, memory, and multi-agent composition.Skill("aws-agents:agents-deploy"): Container contract, deployment, versioning, rollback, and deploy-failure diagnosis.Skill("aws-agents:agents-harden"): IAM scoping, inbound auth, secret handling, session lifecycle, and quotas.Skill("aws-agents:agents-debug"): Traces, logs, and diagnosis of a deployed agent.Skill("aws-agents:agents-optimize"): Evaluators, online monitoring, CI/CD quality gates, observability, and latency and token-cost tuning. Read this first if the goal is to measure an agent's quality. It owns the evaluator and quality-gate machinery; this skill covers only the narrower case where the agent is the gate, and the question is whether its verdicts are stable enough to carry authority.Skill("aws-core:amazon-bedrock"): Model invocation, prompt caching, and throttling diagnosis.Skill("aws-core:aws-ai-ml"): Model selection and inference-cost comparison.
What ships with it
1 file 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.
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 · 40 lines · 198 tokens per session scan A 27021bddeb3d
agentcore-patterns is a skill published in the GitHub repository awslabs/startups (24 stars, last pushed 12d ago), licensed Apache-2.0. It adds 198 tokens to every session and 806 once invoked, about $0.0008 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 2026-09-02.
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