bedrock
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
Install with Tessl CLI
npx tessl i github:itsmostafa/aws-agent-skills --skill bedrockOverall
score
75%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
82%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a solid description that clearly identifies the AWS Bedrock domain and provides explicit trigger guidance with the 'Use when...' clause. The trigger terms are relevant and natural for the target audience. The main weakness is moderate specificity in actions and some potential overlap with generic AI/ML skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (AWS Bedrock) and lists some actions (invoking models, building AI applications, creating embeddings, configuring access, implementing RAG), but these are somewhat high-level rather than concrete specific operations like API calls or configuration steps. | 2 / 3 |
Completeness | Clearly answers both what (AWS Bedrock foundation models for generative AI) and when (explicit 'Use when...' clause listing five distinct trigger scenarios: invoking models, building AI apps, embeddings, model access, RAG patterns). | 3 / 3 |
Trigger Term Quality | Good coverage of natural terms users would say: 'foundation models', 'generative AI', 'embeddings', 'RAG patterns', 'model access', 'AI applications'. These are terms developers working with Bedrock would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | While 'AWS Bedrock' is specific, terms like 'generative AI', 'embeddings', and 'AI applications' could overlap with other AI/ML skills. The Bedrock-specific terms help but 'foundation models' and 'RAG patterns' are generic AI concepts. | 2 / 3 |
Total | 10 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable AWS Bedrock skill with excellent code examples that are immediately executable. The main weaknesses are verbosity (includes content Claude already knows like model descriptions), lack of explicit setup/validation workflows, and a monolithic structure that could benefit from splitting into focused sub-files.
Suggestions
Remove or significantly condense the 'Core Concepts' section - Claude knows what foundation models are and doesn't need model provider descriptions
Add an explicit setup workflow with validation: 1) Enable model access 2) Verify with list command 3) Test invocation 4) Confirm IAM permissions
Split into multiple files: keep SKILL.md as quick-start overview, move CLI reference to CLI.md, troubleshooting to TROUBLESHOOTING.md, and detailed patterns to PATTERNS.md
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is generally efficient but includes some unnecessary content like the Table of Contents (Claude can navigate without it), explanatory text about what foundation models are, and the model listing section which Claude likely knows. The core patterns are well-written but could be tighter. | 2 / 3 |
Actionability | Excellent executable code examples throughout - boto3 patterns are complete and copy-paste ready, CLI commands are specific with proper flags and jq parsing, and the IAM policy JSON is directly usable. All code is production-quality, not pseudocode. | 3 / 3 |
Workflow Clarity | Individual operations are clear, but the skill lacks explicit validation checkpoints for multi-step workflows. The troubleshooting section provides good error recovery patterns, but there's no clear workflow for setting up Bedrock access (enable model -> verify access -> test invocation -> production use). | 2 / 3 |
Progressive Disclosure | The content is well-organized with clear sections, but it's a monolithic document (~300 lines) that could benefit from splitting. CLI reference tables, troubleshooting, and detailed code patterns could be separate files. References section is good but points only to external docs, not internal skill files. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
75%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 12 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata' field is not a dictionary | Warning |
license_field | 'license' field is missing | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 12 / 16 Passed | |
- Reviewed
Table of Contents
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