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llm-application-dev-prompt-optimize

You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati

44

Quality

46%

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

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

SKILL.md
Quality
Evals
Security

Quality

Content

47%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 well-structured and token-efficient but offers only abstract directives instead of concrete prompt-optimization guidance, and its one external reference points to a missing file. It reads as a template skeleton rather than an actionable skill body.

Suggestions

Replace the abstract Instructions with concrete, actionable prompt-optimization steps or worked examples (e.g., a before/after prompt rewrite, a checklist for adding chain-of-thought scaffolding, a template for model-specific tuning).

Either create resources/implementation-playbook.md with the promised detailed patterns and examples, or remove the references to it to avoid a broken pointer.

Add an explicit validation/verification checkpoint to the workflow (e.g., 'Test the optimized prompt against N cases; if outputs regress, iterate on the failing cases').

DimensionReasoningScore

Conciseness

The body is short and well-sectioned with no concept explanations Claude already knows, but the opening line duplicates the frontmatter description verbatim and the Context line's unsourced percentages ('40%', '30%', '50-80%') are minor padding, so it is efficient rather than perfectly lean.

4 / 5

Actionability

The Instructions are high-level hints ('Clarify goals, constraints, and required inputs', 'Apply relevant best practices and validate outcomes') with no concrete prompt-optimization techniques, examples, or executable steps; the only concrete artifact is a pointer to a playbook file that does not exist.

2 / 5

Workflow Clarity

A rough four-bullet sequence exists but the steps are generic process directives not specific to prompt optimization, validation is mentioned only abstractly ('validate outcomes', 'verification') with no concrete checkpoints, and there are no feedback loops.

2 / 5

Progressive Disclosure

Clear section headers organize the content and the single reference is well-signaled in both Instructions and Resources at one level deep; the gap is that the referenced resources/implementation-playbook.md does not exist, so navigation is not fully intact.

4 / 5

Total

12

/

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 the domain and a few advanced techniques but is truncated, uses second-person voice, and lacks any 'Use when...' trigger clause, capping completeness. It reads more as a persona statement than a trigger-rich capability description.

Suggestions

Add an explicit 'Use when...' clause with concrete trigger phrases users would naturally say (e.g., 'Use when improving, rewriting, or debugging LLM prompts, or when designing system prompts and few-shot examples').

Reframe in third person and lead with concrete actions (e.g., 'Optimizes LLM prompts via constitutional AI, chain-of-thought structuring, and model-specific tuning') instead of the persona line 'You are an expert prompt engineer'.

Fix the truncated 'optimizati' and add natural synonyms/extensions ('system prompt', 'few-shot', 'prompt template', 'prompt engineering').

DimensionReasoningScore

Specificity

The only concrete action is the generic 'crafting effective prompts for LLMs'; the named techniques (constitutional AI, chain-of-thought reasoning, model-specific optimization) are approaches/buzzwords rather than actions, and second-person voice ('You are an expert prompt engineer') reduces the score by 1 from a base of 3.

2 / 5

Completeness

It clearly states what the skill does but has no 'Use when...' clause or equivalent explicit trigger guidance; per the guideline a missing trigger clause caps completeness at 3, matching the 'clear what but when missing' anchor.

3 / 5

Trigger Term Quality

Relevant keywords exist ('prompt engineer', 'prompts for LLMs', 'prompt optimization') but common natural user phrasings ('improve my prompt', 'write a better prompt', 'system prompt', 'few-shot') and synonyms are missing, matching the 'some relevant keywords but missing variations' anchor.

3 / 5

Distinctiveness Conflict Risk

Prompt optimization is a recognizable niche, but the broad 'crafting effective prompts for LLMs' framing overlaps with general LLM-usage and claude-api-style skills, fitting 'somewhat specific but could still overlap with similar skills.'

3 / 5

Total

11

/

20

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

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
Reviewed

Table of Contents

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