llm-application-dev-ai-assistant

You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natur

Quality

43%

Does it follow best practices?

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Securityby

Passed

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Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/llm-application-dev-ai-assistant/SKILL.md

The canonical home for this skill is llm-application-dev-ai-assistant in rmyndharis/antigravity-skills

Quality

Content

42%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.

The body is a generic template: tautological trigger sections, an opening that duplicates the description, abstract instructions, and a broken reference to a missing playbook file. It is structured but lacks any concrete, executable technical guidance.

Suggestions

Replace the abstract Instructions with concrete, executable steps for AI-assistant development (e.g., defining intents, designing context/memory, wiring tool-calling, configuring guardrails) with real code or config examples.

Create the referenced 'resources/implementation-playbook.md' (and resources/ directory) or remove the broken reference so progressive disclosure points to real material.

Delete the tautological 'Do not use this skill when' bullets and the duplicated opening paragraph to tighten conciseness.

Dimension	Reasoning	Score
Conciseness	The body is short, but the opening paragraph duplicates the description, the 'Use this skill when'/'Do not use this skill when' bullets are tautological restatements of the skill name, and the Context section restates the description — efficient enough but padded with non-informative content that could be tightened.	3 / 5
Actionability	Instructions are high-level hints with no concrete guidance ('Clarify goals, constraints, and required inputs', 'Apply relevant best practices and validate outcomes') — no code, commands, schemas, or specific steps to execute.	2 / 5
Workflow Clarity	A rough sequence is implied (clarify → apply best practices → validate → provide steps + verification) and validation is mentioned, but checkpoints are implicit and the steps are generic rather than a concrete multi-step workflow.	3 / 5
Progressive Disclosure	Sections are well-organized and a reference is signaled, but the only referenced file ('resources/implementation-playbook.md') does not exist — no resources/ directory is present — so the reference is broken, undercutting navigation.	3 / 5
	Total	11 / 20 Passed

Description

45%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.

The description names its domain and lists a few capabilities but is generic, uses second-person voice, lacks any 'Use when...' trigger clause, and is truncated mid-word. It conveys a clear 'what' but no explicit 'when', landing it in the middle of the scale.

Suggestions

Add an explicit 'Use when...' clause with concrete trigger phrases (e.g., 'Use when building chatbots, conversational agents, or AI assistants, or when the user mentions dialog systems, NLU, or intent routing').

Rewrite in third person and replace generic verbs ('Design comprehensive solutions') with specific actions (e.g., 'Define intents, design context/memory handling, wire tool-calling, configure guardrails').

Fix the truncated description so it is a complete, well-formed sentence.

Dimension	Reasoning	Score
Specificity	The domain is named and a few actions appear ('creating intelligent conversational interfaces, chatbots', 'Design comprehensive AI assistant solutions'), but the actions are generic and the text is truncated mid-word ('with natur'); second-person voice ('You are an AI assistant development expert') triggers the -1 specificity penalty, dropping it from a 3.	2 / 5
Completeness	It states clearly 'what' the skill does (design AI assistant solutions), but the 'when' is entirely absent with no 'Use when...' or equivalent trigger guidance, which caps completeness at 3 per the rubric.	3 / 5
Trigger Term Quality	Some relevant natural keywords appear ('AI assistant', 'chatbots', 'conversational interfaces', 'AI-powered applications'), but common variations, synonyms, and concrete trigger phrases are missing and no 'Use when...' clause enumerates them.	3 / 5
Distinctiveness Conflict Risk	'AI assistant development' is a somewhat specific niche, but the broad phrasing could overlap with general application development, prompt-engineering, and integration skills.	3 / 5
	Total	11 / 20 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: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills-claude/skills/llm-application-dev-ai-assistant/SKILL.md
Commit: 57c135b

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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