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prompt-engineering-patterns

This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot prompting", or wants to apply advanced prompt engineering patterns for production LLM applications.

64

Quality

75%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./plugins/llm-application-dev/skills/prompt-engineering-patterns/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%

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

The body has a solid executable Quick Start and a valid progressive-disclosure pointer, but it pads the overview with concept re-explanation and inline capability lists that duplicate dedicated reference files. Tightening the overview and linking the per-pattern references would lift the weaker dimensions.

Suggestions

Replace the six 'Core Capabilities' bullet-list subsections with concise one-line summaries that link to the corresponding reference files (e.g. chain-of-thought.md, few-shot-learning.md), removing concept re-explanation Claude already knows.

Add explicit navigation to each available reference file from the body rather than only details.md, so the per-pattern documentation is discoverable at the top level.

Convert the 'When to Use' and capability lists into a brief decision-oriented overview that directs Claude to the concrete worked examples and the optimize-prompt.py script.

DimensionReasoningScore

Conciseness

The Quick Start code is lean and useful, but the six 'Core Capabilities' subsections re-explain concepts Claude already knows (chain-of-thought, few-shot, structured outputs) as bullet lists, which the rubric penalizes as unnecessary explanation.

2 / 3

Actionability

The Quick Start provides fully executable Python, but large portions of the body (e.g. 'Example selection strategies', 'Step-by-step reasoning elicitation') describe rather than instruct, so guidance is concrete in one place and abstract elsewhere.

2 / 3

Workflow Clarity

As a patterns catalog rather than a fragile multi-step operation it needs no validation loop, but the body presents unordered sections rather than a sequenced process with checkpoints, so the sequence is implicit.

2 / 3

Progressive Disclosure

It correctly points to a real one-level-deep references/details.md, but the six inline 'Core Capabilities' bullet-list sections duplicate material that exists in separate reference files (chain-of-thought.md, few-shot-learning.md, etc.) without signaling them, so content that should be split is inline.

2 / 3

Total

8

/

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.

The description is strong: it states the skill's purpose concisely and supplies explicit, natural trigger phrases that clearly distinguish it from other skills. Both the 'what' and 'when' questions are answered directly.

DimensionReasoningScore

Specificity

Names the prompt-engineering domain and multiple concrete actions such as 'optimize a prompt', 'design a prompt template', and 'debug prompt issues', matching the rubric's 'lists multiple specific concrete actions' anchor.

3 / 3

Completeness

Explicitly answers both 'what' ('apply advanced prompt engineering patterns for production LLM applications') and 'when' via the quoted trigger phrases, satisfying the 'clearly answers both what AND when with explicit triggers' anchor.

3 / 3

Trigger Term Quality

Quotes natural user phrases like 'optimize a prompt', 'write better prompts', and 'debug prompt issues' that a user would realistically say, giving good coverage; not score 2 because common variations are well represented rather than partially missing.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (advanced prompt engineering patterns) with distinct triggers, making it unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
wshobson/agents
Reviewed

Table of Contents

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