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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). Applies when invoking models, setting up Knowledge Bases, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing Bedrock models (Claude, Llama, Nova, Titan). Also for KB migration to Managed, prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations, chunking strategies, and Bedrock model selection. Covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend. For SageMaker model discovery, customization, or endpoints, use aws-ai-ml.

76

Quality

95%

Does it follow best practices?

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SecuritybySnyk

Medium

Suggest reviewing before use

The canonical home for this skill is amazon-bedrock in aws/agent-toolkit-for-aws

SKILL.md
Quality
Evals
Security

Quality

Content

88%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.

A strong routing-style SKILL.md for a very broad domain: executable commands with real model IDs, explicit validation checkpoints in every spelled-out workflow, and a verified, clearly signaled reference bundle. The main costs are repetition of the maxTokens warning, one hard-to-parse Harness paragraph, and two-level reference nesting in the migration procedures.

Suggestions

Consolidate the maxTokens warning to the Critical Warnings section and reference it with a one-line pointer from the Converse, Examples, and ThrottlingException sections instead of restating the full rationale each time.

Break the long Harness paragraph into a short numbered checklist or table (create-harness inputs → poll get-harness until READY → invoke with runtimeSessionId/messages → read streamed events → agentcore CLI) so each requirement is scannable instead of buried in a run-on sentence.

Flatten the two-level reference nesting for the KB and agent migration guides — either inline a manifest of the phase files (discovery, plan, implement, etc.) in SKILL.md's migration sections or hoist the phase files to a single index — so navigation stays one level deep.

DimensionReasoningScore

Conciseness

The body is largely efficient — routing tables, terse troubleshooting entries, and pointer sections — but the maxTokens warning is repeated roughly six times (Critical Warnings, Converse section, Examples 1 and 7, ThrottlingException), the Harness paragraph packs five requirements into one dense run-on, and a few spots over-explain known concepts ('as IAM changes are eventually consistent'). This fits the 'efficient with minor trimmable over-explanation' anchor rather than the lean 5, but sits noticeably above the 3.

4 / 5

Actionability

Guidance is copy-paste ready: 'aws bedrock-runtime converse --model-id us.anthropic.claude-sonnet-4-6 --messages … --inference-config '{"maxTokens":1024}'', 'aws bedrock list-foundation-models --region us-east-1', 'Config(retries={"max_attempts": 5, "mode": "adaptive"})', exact error signatures, and specific constraints like 'runtimeSessionId (≥33 chars)'. Common cases (invoke, throttling, RAG setup, agent deploy) are each covered by a concrete example.

5 / 5

Workflow Clarity

Workflows are clearly sequenced with explicit validation: a Verify Dependencies gate (CLI presence, version, region access), the checkboxed invoke workflow (verify model access before invoking), the KB creation sequence ending in 'verify with Retrieve', Harness deployment with polling 'get-harness until status READY', and mandatory confirmation before destructive operations. The error-retry classification table and per-error troubleshooting entries provide feedback loops for recovery, satisfying the top anchor's checklists-and-validation criteria.

5 / 5

Progressive Disclosure

The body is a well-signaled routing hub: a Table of Contents, capability tables mapping goals to references, and all 27 referenced reference files verified to exist on disk. However, the migration guides chain two levels deep (SKILL.md → migrate-knowledge-base-to-managed.md → references/bedrock-kb-migration/*.md with 9 phase files, and similarly for the AgentCore-harness migration with 5 files), which is a minor organization gap against the one-level-deep ideal.

4 / 5

Total

18

/

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.

An exemplary description: dense, third-person, and packed with concrete capabilities, natural trigger phrases (including synonyms and verbatim error names), and explicit negative boundaries plus sibling-skill routing. Every clause earns its place despite the length.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop)', plus payments, prompt caching, cost tracking, and migrations — with no meaningful coverage gaps. It uses consistent third-person voice ('Builds', 'Covers', 'Applies'), so no voice penalty applies.

5 / 5

Completeness

It explicitly answers both 'what' ('Builds generative AI applications on Amazon Bedrock. Covers model invocation…') and 'when' ('Applies when invoking models… Also for KB migration to Managed, prompt caching, quota and throttling diagnosis…') with concrete trigger phrases, matching the top anchor exactly.

5 / 5

Trigger Term Quality

Trigger terms are comprehensive and include synonyms users would naturally say: 'migrating/porting/converting a Bedrock Agent', 'troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException)', 'prompt caching', 'chunking strategies', and model family names (Claude, Llama, Nova, Titan). Exact exception names match what users paste verbatim.

5 / 5

Distinctiveness Conflict Risk

It carves a clear Bedrock-specific niche and actively prevents mis-triggering with explicit boundaries: 'NOT for custom model training, Rekognition, or Comprehend. For SageMaker model discovery, customization, or endpoints, use aws-ai-ml.' Conflict risk with adjacent AWS skills is minimal.

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

aws/agent-toolkit-for-aws

Path

plugins/aws-core/skills/amazon-bedrock/SKILL.md

Reviewed

Table of Contents

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