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agent-advisor

Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model pricing.

68

Quality

86%

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SKILL.md
Quality
Evals
Security

Quality

Content

75%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 well-engineered orchestrator body: it delegates detail to phase and decision-ref files, specifies state, routing, and gates in checkable terms, and handles resumability and failure fallbacks explicitly. The main costs are baroque gate-condition prose, a missing scoring-engine invocation at this level, and a Files table that under-indexes the actual bundle.

Suggestions

Add the missing bundle entries to the Files table (references/diagram/, references/handoff/, references/models/, references/output-templates/, references/phases/model-recommend/, report-shell.md, report-help-banner.md) so the index matches the actual bundle.

State (or explicitly point to where the phase files state) the uv/python command that runs scripts/scoring.py, so the deterministic engine is invocable from the overview alone.

Rewrite the Gate 2 semantics paragraph as a short bulleted decision list (condition -> resolution) to make the nested in_progress/not_applicable/engine_absent cases scannable instead of one long sentence.

DimensionReasoningScore

Conciseness

Dense orchestration contract with no explanations of concepts Claude already knows (terse idioms like "read-merge-write", gate protocol "HANDOFF_OK / GATE_FAIL"). Not 5 because the inline Contextual-offers policy and the single-sentence Gate 2 conditions could be tightened; not 3 because there is no filler, only contract detail.

4 / 5

Actionability

Gives exact file paths, a concrete state-file JSON example, an explicit prerequisite check ("uv --version"), per-entry-point routing, and machine-checkable gate conditions. Not 5 because the scoring engine's actual invocation command is never stated at this level (delegated to phase files), and a few conditions remain prose-only.

4 / 5

Workflow Clarity

The backbone sequence is named, the phase gate has checkable preconditions ("phases.clarify == completed AND phases.confirm == completed"), and failure/resume handling is explicit (fallback POC from design.json on Stage 2 failure, persisting poc as in_progress before load). Not 5 because the Gate 2 paragraph packs many nested conditions into near-unparseable prose, burdening sequence comprehension.

4 / 5

Progressive Disclosure

The body is a genuine overview layer: detailed content lives in references/phases/*, references/decision-refs/*, references/runtimes/*.json and the vendored INTERPRETER.md, all indexed in a Files table with purposes and clearly signaled loads ("Load it first", "Load ... whenever target maturity is selected"). Not 5 because the Files table omits existing bundle content (references/diagram/, references/handoff/, references/models/, references/output-templates/, references/phases/model-recommend/, report-shell.md, report-help-banner.md), leaving minor navigation gaps.

4 / 5

Total

16

/

20

Passed

Description

95%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.

A strong description: concrete actions, comprehensive natural-language triggers, explicit scope boundaries with named redirect targets, and third-person voice. The only weakness is that secondary outputs of the flow (cost estimation, the generated recommendation document) are not named, leaving slightly less than full coverage of what the skill does.

DimensionReasoningScore

Specificity

"pick a runtime, plan a migration for existing workloads, and build an executable POC" plus "add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS" name several concrete actions covering each skill branch. Not 5 because supporting outputs (cost estimate, recommendation doc) go unmentioned; not 3 because coverage well exceeds 1-2 actions.

4 / 5

Completeness

Explicit "what" up front ("Entry point for AI-agent work on AWS: pick a runtime, plan a migration..., and build an executable POC") followed by an explicit "Triggers on:" clause with concrete phrases and a "Not for:" exclusion list. Clearly answers both what and when.

5 / 5

Trigger Term Quality

Triggers are natural user phrasings with synonyms and comparisons: "which runtime for my agent", "AgentCore vs ECS vs EKS vs Lambda", "deploy an AI agent on AWS", "I have an agent idea what do I build", "move/migrate my agents to AWS", "Temporal Cloud vs self-hosted". Matches the comprehensive anchor including common variations.

5 / 5

Distinctiveness Conflict Risk

Clear niche (AI agents on AWS) with distinct triggers, and it actively reduces conflict by carving out non-agent systems and naming the sibling skills they redirect to (gcp-to-aws / heroku-to-aws / llm-to-bedrock) plus exclusions like "per-model pricing".

5 / 5

Total

19

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 1 missing, 2 suspicious

Warning

referenced_paths_exist

Referenced path issues: 20 deeper-than-1-level

Warning

Total

14

/

16

Passed

Repository

aws/agent-toolkit-for-aws

Path

plugins/aws-startup-advisor/skills/agent-advisor/SKILL.md

Reviewed

Table of Contents

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