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 skills/aws/agent-toolkit-for-aws/agents-optimizenpx skills add aws/agent-toolkit-for-aws --skill agents-optimizegit clone --depth 1 https://github.com/aws/agent-toolkit-for-awsWrote 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/agent-toolkit-for-aws/agents-optimize)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/agents-optimize"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/agents-optimize.svg" alt="Measured on agentmods" 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 | $0.00165 | $0.00867 |
| Opus 5 | $0.00082 | $0.00434 |
| Sonnet 5 | $0.00033 | $0.00173 |
| Haiku 4.5 | $0.00016 | $0.00087 |
Grade A, and why
agents-optimize 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 4d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
optimize
Measure and improve your AgentCore agent's quality through evaluation, monitoring, and observability.
When to use
- You want to know if your agent is giving good answers
- You want to set up continuous quality monitoring in production
- You want to add a quality gate to your CI/CD pipeline
- You want to understand agent behavior through logs, metrics, and traces
- You want to set up CloudWatch dashboards or X-Ray tracing
Do NOT use for:
- Debugging a specific broken agent (wrong answers, errors) → use
agents-debug - Production security hardening (IAM, auth) → use
agents-harden
Input
$ARGUMENTS can be:
- An eval goal: "add a quality gate", "set up monitoring"
- An observability goal: "set up CloudWatch dashboard", "understand my traces"
- A specific evaluator: "llm-as-a-judge", "code-based"
- Empty — the skill will guide based on project context
Process
Step 0: Verify CLI version
Run agentcore --version. This skill requires v0.9.0 or later.
Step 1: Read project context
Read agentcore/agentcore.json to understand existing evaluators, online eval configs, and agent setup.
If agentcore/agentcore.json is not found:
"This skill requires an AgentCore project. Use
agents-get-startedto create one."
Step 2: Determine the workflow
| Developer intent | Action |
|---|---|
| Measure quality, add evaluator, run eval, CI/CD gate, online monitoring | Load references/evals.md and follow its workflow |
| Set up observability, CloudWatch, X-Ray, logs, metrics, dashboards | Load references/observability.md and follow its workflow |
| Understand or reduce AgentCore costs | Load references/cost.md |
| Both — "I want to understand and improve my agent" | Start with observability setup, then add evals |
Step 3: Follow the loaded reference
The reference file contains the full procedure. Follow it step by step.
Cross-references
- After setting up evals, suggest
agents-hardenfor production readiness - If eval results reveal agent issues, suggest
agents-debugfor root cause analysis - If the developer needs to add capabilities first, suggest
agents-build
What ships with it
3 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.
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.
- 4d ago First seen · 92 lines · 165 tokens per session scan A 91c53dbdd334
agents-optimize is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,518 stars, last pushed today), licensed Apache-2.0. It adds 165 tokens to every session and 867 once invoked, about $0.0008 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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