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amazon-bedrock

Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching, quota health checks and throttling diagnosis, cost attribution, migrating between Claude model generations, chunking strategies, API selection (Converse vs InvokeModel), and model selection. Also covers AgentCore Payments setup (x402, microtransactions, Payment Manager, Coinbase CDP, Stripe Privy, 402 Payment Required, paid endpoint). NOT for custom model training, Rekognition, or Comprehend.

72

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Medium

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The SKILL.md body is a well-organized overview that leans on decision tables, executable CLI commands, and one-level-deep reference files, with strong progressive disclosure and good actionability. The main improvement lever is tightening the repeated maxTokens guidance across sections.

Suggestions

Consolidate the maxTokens/quota warning: state it once in Critical Warnings and reference it from the Converse, Troubleshooting, and quota examples instead of restating it verbatim in each.

Inline at least one complete validate-then-proceed feedback loop (e.g., for the Harness create→poll→invoke flow) so the body demonstrates the validation pattern, not just names the steps.

Move the 'Examples — mapping user intent to workflows' list toward the decision-table format used in 'Which Bedrock Capability Do You Need?' to reduce prose tokens and improve scannability.

DimensionReasoningScore

Conciseness

The body is largely lean and assumes Claude's competence — terse tables, runnable commands, and pointed warnings — but the maxTokens caution is repeated across Critical Warnings, Converse API, Troubleshooting, and multiple examples, adding some redundancy that could be trimmed without losing the safety signal.

4 / 5

Actionability

Concrete, copy-paste-ready AWS CLI commands appear throughout ('aws bedrock-runtime converse --model-id us.anthropic.claude-sonnet-4-6 ...', 'aws service-quotas get-service-quota ...') with a real retry-config snippet, and detailed code is correctly routed to reference files; minor gaps exist because most full procedures live in references rather than inline.

4 / 5

Workflow Clarity

Multi-step processes are explicitly sequenced and routed to reference procedures with 'execute step by step' / MUST constraints, and key checkpoints are named (poll get-harness until READY, run prepare-agent after changes, confirm before destructive ops); full validation feedback loops mostly live in the referenced procedures rather than inline, keeping this just below a 5.

4 / 5

Progressive Disclosure

A clear overview with a TOC and goal→reference decision tables routes each task to a single one-level-deep reference file, all 22 referenced files verified to exist, with detail appropriately split out and easy to navigate.

5 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is a strong, third-person trigger statement that concretely enumerates capabilities, gives explicit 'Use when'/'ALSO USE for' trigger guidance, and scopes out unrelated AWS services. It is comprehensive on both what and when with low conflict risk.

DimensionReasoningScore

Specificity

Lists multiple concrete, distinct capabilities — 'model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore' plus 'prompt caching, quota health checks... cost attribution, migrating between Claude model generations, chunking strategies, API selection' — giving comprehensive concrete-action coverage rather than vague language.

5 / 5

Completeness

Explicitly answers both 'what' ('Builds generative AI applications on Amazon Bedrock. Covers...') and 'when' with two concrete trigger clauses ('Use when...' and 'ALSO USE for...'), plus an explicit out-of-scope boundary.

5 / 5

Trigger Term Quality

Includes natural user phrasings and synonyms ('invoking models', 'prompt caching', 'troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException)', 'choosing models (Claude, Llama, Nova, Titan)', 'migrating between Claude model generations'), covering terms a user would actually say.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear Amazon Bedrock niche and includes an explicit negative boundary ('NOT for custom model training, Rekognition, or Comprehend'), minimizing the chance of triggering for the wrong skill.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
aws/agent-toolkit-for-aws
Reviewed

Table of Contents

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