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ai-engineer

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

68

1.17x
Quality

60%

Does it follow best practices?

Impact

73%

1.17x

Average score across 3 eval scenarios

SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./skills/ai-engineer/SKILL.md

The canonical home for this skill is jbvc/ai-engineer

SKILL.md
Quality
Evals
Security

Quality

Content

20%

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

The body reads as a persona/CV profile — extensive capability inventories and knowledge restatement with no executable guidance, validation steps, or file-based detail split. It is organized by section headers but is otherwise a verbose monolith.

Suggestions

Replace the Knowledge Base and catalogued capability lists with a lean overview, moving long model/library inventories into a separate reference file that SKILL.md links to one level deep.

Add at least one concrete, copy-paste-ready example (e.g. a minimal RAG or agent snippet with real library calls) so the skill instructs rather than describes.

Tighten the Instructions/Response Approach into a short workflow with explicit validation checkpoints (e.g. verify retrieval recall, run evals before rollout) and drop hard-coded version numbers that will age.

DimensionReasoningScore

Conciseness

The 162-line body is dominated by restatement of domain knowledge Claude already has (a "Knowledge Base" listing "Latest LLM developments... GPT-4o, Claude 4.5, Llama 3.2" and catalogues of named models/libraries), and embeds time-sensitive version numbers, matching the verbose/padded anchor rather than the level-2 mostly-efficient one.

1 / 3

Actionability

There is no executable code, command, or concrete example anywhere; "Instructions" ("Clarify use cases... Design the AI architecture...") and "Response Approach" only describe in the abstract, matching the describes-rather-than-instructs level-1 anchor rather than the pseudocode level 2.

1 / 3

Workflow Clarity

Sequenced steps exist (the four "Instructions" and eight "Response Approach" items) but they are abstract with no validation checkpoints or error-recovery feedback loops, fitting the steps-present-but-checkpoints-missing level-2 anchor; not the fully-gapped level 1 since ordering is explicit.

2 / 3

Progressive Disclosure

No bundle files exist and none are referenced; the body is a single monolithic file with large capability/reference lists kept inline that would be better split out, matching the level-2 "content that should be separate is inline" anchor rather than the one-level-deep-references level 3.

2 / 3

Total

6

/

12

Passed

Description

100%

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 concise, well-constructed description that states concrete capabilities in third person and pairs them with explicit, natural trigger terms. It cleanly answers both what the skill does and when to use it.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "Build production-ready LLM applications, advanced RAG systems, and intelligent agents" plus "Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations" — matching the multiple-specific-actions anchor rather than the single-action level 2.

3 / 3

Completeness

Explicitly answers both what (the build/implement clauses) and when ("Use PROACTIVELY for LLM features, chatbots, AI agents..."), meeting the explicit-trigger requirement rather than the when-only-implied level 2.

3 / 3

Trigger Term Quality

"Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications" covers natural terms a user would actually say, and the verbs are third-person ("Build", "Implements"), so no voice penalty; not the sparse level-2 "Works with PDF files".

3 / 3

Distinctiveness Conflict Risk

Triggers are AI-specific (RAG, LLM applications, AI agents, chatbots, AI-powered applications), giving a clear niche unlikely to fire for non-AI coding skills; above the still-overlapping level-2 anchor.

3 / 3

Total

12

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
rmyndharis/antigravity-skills
Reviewed

Table of Contents

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