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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. Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching setup and debugging, quota health checks and throttling diagnosis, cost attribution and tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. NOT for custom model training, Rekognition, or Comprehend.

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

85%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, highly actionable routing skill: nearly every section ends in an executable command or a clearly signaled, verified reference file, and validation/abort checkpoints plus an extensive error-recovery catalog make the workflows safe to follow. The main weakness is redundancy — the same maxTokens/throttling warning and routing intent repeated across up to five sections, plus a Table of Contents and an Examples section that duplicate existing structure — which inflates the token budget without adding information.

Suggestions

State the unset-maxTokens/ThrottlingException warning once in Critical Warnings and link to it from the other locations (Converse section, ThrottlingException entry, quota-health workflow) — it is currently repeated ~5 times across the body.

Merge the 'Examples — mapping user intent to workflows' section into the 'Which Bedrock Capability Do You Need?' routing table; Examples 1 and 7 are near-duplicates of each other and of the ThrottlingException troubleshooting entry.

Delete the Table of Contents (it duplicates the section headers already scannable in the file) and trim generically-known AWS security advice (IAM roles vs users, CloudTrail) from Security Considerations, keeping only Bedrock-specific items like the PII logging gap and confused-deputy conditions.

DimensionReasoningScore

Conciseness

The body is dense and mostly high-value, but repetition goes beyond 'minor': the maxTokens/ThrottlingException warning is restated ~5 times (Critical Warnings, the Converse section, Examples 1 and 7, and the ThrottlingException entry), Examples 1 and 7 are near-duplicates, the Examples section largely restates the 'Which Bedrock Capability Do You Need?' routing table, the PII/CloudWatch guidance appears in both Critical Warnings and Security Considerations, and the Table of Contents duplicates the section headers. This fits 'mostly efficient but could be tightened' better than anchor 4's 'minor instances'.

3 / 5

Actionability

Fully executable guidance throughout: copy-paste-ready CLI with a real model ID ('aws bedrock-runtime converse --model-id us.anthropic.claude-sonnet-4-6 … --inference-config '{"maxTokens":1024}''), complete JSON payloads for retrieve-and-generate, quota and CloudWatch commands, adaptive retry config ('Config(retries={"max_attempts": 5, "mode": "adaptive"})'), and exact error signatures with specific fixes. Placeholders like <kb-id> are appropriate parameterization, not gaps, so this is anchor 5 rather than 4.

5 / 5

Workflow Clarity

Clear sequences with explicit validation and feedback loops: the 'Verify Dependencies' MUST-constraints act as a pre-flight checkpoint, general constraints mandate step-explanation and abort/confirm semantics (including confirmation before destructive operations), the invoke workflow is a checkbox step list, and the Troubleshooting catalog plus retry-classification table and prompt-cache debug checklist provide error-recovery loops. This matches anchor 5 (validation steps, feedback loops, checklists).

5 / 5

Progressive Disclosure

The body is a routing overview pointing to 19 one-level-deep reference files, all of which exist on disk and are clearly signaled from two routing tables ('Which Bedrock Capability Do You Need?', 'AgentCore Services') plus per-workflow MUST-read pointers. The inline Troubleshooting section functions as an always-loaded triage layer rather than misplaced detail, so content is appropriately split with easy navigation — anchor 5.

5 / 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: third-person voice, concrete capability list, explicit 'Use when' triggers covering the full workflow surface (invocation, RAG, agents, guardrails, caching, quotas, cost, migration), and clear exclusions. Its only weakness is mild redundancy — 'model selection' and 'guardrail capabilities' restate triggers already named earlier — which slightly pads length without hurting trigger quality.

DimensionReasoningScore

Specificity

Lists multiple concrete actions with comprehensive coverage: 'model invocation (Converse API, InvokeModel)', 'RAG with Knowledge Bases', 'prompt caching setup and debugging', 'quota health checks and throttling diagnosis', 'cost attribution and tracking', 'migrating between Claude model generations'. The only flaw is mild repetition ('choosing models'/'model selection'), which is redundancy rather than a coverage gap, so it does not drop to anchor 4.

5 / 5

Completeness

Explicitly answers both: what ('Builds generative AI applications on Amazon Bedrock. Covers model invocation… Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore') and when ('Use when invoking models… ALSO USE for… NOT for custom model training, Rekognition, or Comprehend'), with concrete trigger phrases and explicit exclusions. Clearly matches the top anchor.

5 / 5

Trigger Term Quality

Comprehensive natural-term coverage including synonyms and literal strings users would paste: 'troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException)', 'RAG', 'prompt caching', 'throttling diagnosis', 'chunking strategies', 'API selection (Converse vs InvokeModel)', plus model names (Claude, Llama, Nova, Titan). Not anchor 4 — no common phrasing is missing.

5 / 5

Distinctiveness Conflict Risk

Clear niche (Amazon Bedrock) with distinct triggers including specific exception codes, and an explicit NOT-for clause (Rekognition, Comprehend, custom model training) that disambiguates it from neighboring AWS AI skills. Minimal conflict risk — fits anchor 5 better than 4.

5 / 5

Total

20

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

13

/

16

Passed

Repository

mreferre/aws-agent-toolkit-skills

Path

.kiro/skills/amazon-bedrock/SKILL.md

Reviewed

Table of Contents

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