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llm-application-dev-ai-assistant

Design conversational assistants with context management, response generation, and LLM integration. Use when building chatbots or planning assistant application architecture.

Quality

60%

Does it follow best practices?

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Passed

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tessl review fix ./skills/llm-application-dev-ai-assistant/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

25%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: unordered meta-instructions with no concrete, executable guidance, and its sole substance reference points to a nonexistent file. Section structure exists, but there is nothing actionable in the body and nothing to navigate to in the bundle.

Suggestions

Replace vague bullets like "Apply relevant best practices and validate outcomes" with concrete, executable guidance — e.g., a specific assistant architecture pattern, a starter snippet for the conversation loop, or named libraries and integration steps.

Add a sequenced workflow with explicit validation checkpoints (e.g., 1. clarify goals and constraints → 2. design context/memory strategy → 3. implement response generation → 4. test conversation flows) instead of unordered meta-instructions.

Fix or remove the dangling `resources/implementation-playbook.md` reference — either ship the playbook in the bundle or inline the key patterns, since the file does not exist.

DimensionReasoningScore

Conciseness

The body is short in absolute terms, but several sections add no information Claude doesn't already have: the persona paragraph ("You are an AI assistant development expert specializing in..."), the "Use this skill when" bullets ("Working on ai assistant development tasks or workflows"), and the generic "Context" section restate the description. Fits anchor 3 ("mostly efficient but includes some unnecessary explanation or could be tightened") more than anchor 2's "several padded sections" given the low total token count.

3 / 5

Actionability

Bullets like "Apply relevant best practices and validate outcomes" and "Provide actionable steps and verification" are meta-instructions applicable to any task, with no concrete commands, code, or specific steps — matching anchor 1 ("entirely vague or abstract; no concrete code or commands"). The only concrete pointer ("open resources/implementation-playbook.md") targets a file that does not exist in the bundle, so it provides no executable guidance either.

1 / 5

Workflow Clarity

A rough sequence is implied (clarify goals → apply best practices → provide steps), but each step is poorly defined and there are no validation checkpoints, matching anchor 2 ("rough sequence present but many gaps; steps poorly defined; validation absent"). It is above anchor 1 because some ordering exists, and below anchor 3 because the steps are generic rather than listed as a concrete sequence.

2 / 5

Progressive Disclosure

The body defers all substance to `resources/implementation-playbook.md`, but no bundle files exist (no references/, scripts/, assets/, or resources/ directories), so the single one-level reference is dangling. Scoring against the actual bundle structure, the disclosure structure collapses into an empty shell pointing at a phantom file — closer to anchor 2's broken minimal structure than anchor 3, where references at least resolve.

2 / 5

Total

8

/

20

Passed

Description

75%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 clearly answers both what and when, with an explicit "Use when building chatbots or planning assistant application architecture" trigger clause and natural keywords. It is weakened by a somewhat generic capability list and moderate overlap risk with broader LLM application development skills.

Suggestions

Replace generic capability phrasing like "LLM integration" with more concrete actions (e.g., "implements conversation state management, tool/function calling, and streaming responses").

Broaden trigger coverage with common synonyms such as "AI assistant", "virtual assistant", or "dialogue system" to catch more natural user phrasings.

DimensionReasoningScore

Specificity

"Design conversational assistants with context management, response generation, and LLM integration" names the domain and several capability areas, but "design" is the only action verb and "LLM integration" is generic buzzword-adjacent phrasing. It sits above anchor 2 (bare domain naming) but below anchor 4, which expects several distinct concrete actions like "extracts text, fills forms, converts pages".

3 / 5

Completeness

It explicitly answers both what ("Design conversational assistants with context management, response generation, and LLM integration") and when ("Use when building chatbots or planning assistant application architecture") with concrete trigger phrases. The 'when' clause is explicit and specific, so anchor 4 ("'when' could be more explicit or specific") does not fit; anchor 5 does.

5 / 5

Trigger Term Quality

"chatbots", "conversational assistants", and "assistant application architecture" are natural phrases a user would say when needing this skill. A few common variations are missing (e.g., "AI assistant", "virtual assistant", "bot", "dialogue system"), matching anchor 4's "good keyword coverage; a few natural terms missing" rather than anchor 5's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

The chatbot/assistant development niche is somewhat specific, but broad framing like "LLM integration" and "conversational assistants" overlaps with general LLM-application-development skills (the frontmatter name itself suggests an llm-application-dev skill family). Fits anchor 3 ("could still overlap with similar skills") better than anchor 4's "mostly distinct".

3 / 5

Total

15

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

rmyndharis/antigravity-skills

Path

skills/llm-application-dev-ai-assistant/SKILL.md

Reviewed

Table of Contents

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