ai-engineering-toolkit

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

35%

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

The content reads as a well-organized overview of six workflows but stops at description: it offers no executable code or commands, and keeps all detail inline without bundle references. Workflow sequencing is implied but lacks explicit validation checkpoints.

Suggestions

Add concrete, executable guidance per skill (scoring rubric tables, decision-tree snippets, or command/example blocks) instead of prose summaries.

Introduce explicit validation/feedback steps for the risky workflows (e.g. prompt-eval re-scoring, agent-safety-guard per-phase confirmation, RAG metric thresholds) to raise workflow clarity.

Move each workflow's detailed methodology into one-level-deep reference files under references/ and link to them from SKILL.md for proper progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient with organized per-skill sections, but it carries promotional padding ('turn your AI coding assistant into a senior AI engineering partner', 'every workflow produces consistent, reproducible results') and some unnecessary context that could be tightened.	2 / 3
Actionability	Each skill is described at a high level ('Scores prompts across 8 dimensions ... with weighted aggregation') with no executable code, commands, or copy-paste guidance, so it describes rather than instructs.	1 / 3
Workflow Clarity	Sequences are implied (the RAG decision tree, the 5-phase coaching flow, the best-practices ordering) and the safety skill notes per-phase confirmation, but most multi-step processes lack explicit validation checkpoints or error-recovery feedback loops.	2 / 3
Progressive Disclosure	The single SKILL.md is reasonably sectioned per skill, but no bundle files (references/scripts/assets) exist and detailed methodology for six distinct workflows is kept inline rather than split into one-level-deep references.	2 / 3
	Total	7 / 12 Passed

Description

60%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and enumerates concrete capabilities, but it lacks an explicit use-when trigger clause and leans on promotional phrasing rather than natural user keywords. It is clearly distinguishable yet broad enough to risk overlap with narrower skills.

Suggestions

Add an explicit 'Use when ...' clause listing the natural situations that should trigger this skill (e.g. 'Use when evaluating LLM prompts, designing a RAG pipeline, or auditing an agent for prompt-injection risks').

Trim marketing language ('production-ready', '8-dimension scoring', '65-point checklist') in favor of plain trigger terms a user would actually say.

Narrow or restate the umbrella category so the six bundled workflows do not overlap with single-purpose skills.

Dimension	Reasoning	Score
Specificity	The description lists six concrete, named capabilities ('prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching'), matching the multiple-specific-actions anchor.	3 / 3
Completeness	It clearly answers 'what' via the six enumerated workflows but provides no explicit 'Use when...' or equivalent trigger guidance for when Claude should invoke the skill, which the guidelines cap at 2.	2 / 3
Trigger Term Quality	It contains some relevant natural terms ('prompt evaluation', 'RAG', 'security audit') but mixes in marketing-fluff jargon ('8-dimension scoring', '65-point checklist', 'production-ready') and omits common user variations, so coverage is partial rather than comprehensive.	2 / 3
Distinctiveness Conflict Risk	The named sub-skills carve out a recognizable niche, but bundling six disparate workflows under the broad 'AI engineering' label means it could overlap with several separate single-purpose skills.	2 / 3
	Total	9 / 12 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: sickn33/antigravity-awesome-skills
Commit: b69437e

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.