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prompt-engineering-patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

68

1.16x
Quality

55%

Does it follow best practices?

Impact

85%

1.16x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./tests/ext_conformance/artifacts/agents-wshobson/llm-application-dev/skills/prompt-engineering-patterns/SKILL.md

The canonical home for this skill is prompt-engineering-patterns in wshobson/agents

SKILL.md
Quality
Evals
Security

Quality

Content

46%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 delivers strong, concrete code examples but is bloated with prompt-engineering fundamentals Claude already knows, and it completely ignores its own bundle — inlining reference-level detail in SKILL.md while the references/, scripts/, and assets/ files go unmentioned. It reads more like a tutorial than an actionable skill overview. Restructuring around the existing bundle files would fix both the conciseness and progressive-disclosure problems at once.

Suggestions

Replace the inlined Core Capabilities and Key Patterns sections with brief one-line pointers to the existing bundle files (e.g., '**Chain-of-thought**: See [references/chain-of-thought.md](references/chain-of-thought.md)', '**A/B testing harness': See [scripts/optimize-prompt.py](scripts/optimize-prompt.py)), keeping only the Quick Start and one or two compact patterns inline.

Cut sections that restate knowledge Claude already has — Best Practices ('Be Specific', 'Show, Don't Tell'), Common Pitfalls, and the concept-level bullets under Core Capabilities — or fold the few genuinely non-obvious items into the reference files.

Add a short decision workflow (e.g., start with the simplest pattern level, add structure only if outputs fail, then run scripts/optimize-prompt.py to measure) so Claude knows which pattern to apply when, with a validate-and-retry loop.

DimensionReasoningScore

Conciseness

The ~480-line body spends large sections restating concepts Claude already knows (e.g., "Zero-shot CoT with 'Let's think step by step'", "Be Specific: Vague prompts produce inconsistent results", bullet lists defining few-shot learning and system-prompt basics), and this material duplicates the deeper reference files in the bundle. Padding is pervasive across Core Capabilities, Best Practices, and Common Pitfalls rather than occasional.

2 / 5

Actionability

The Quick Start and Patterns 1-6 provide concrete, mostly copy-paste-ready Python (Pydantic schemas, SemanticSimilarityExampleSelector, cache_control usage). Minor gaps keep it below a 5: top-level `await chain.ainvoke(...)` without an async wrapper, an untyped `llm` parameter in Pattern 5, and undocumented third-party deps like `langchain_voyageai`.

4 / 5

Workflow Clarity

This is a pattern catalog, not a sequenced process: there is no workflow telling Claude which pattern to apply in a given situation or how to iterate/test a prompt, and no validation checkpoints anywhere (even the 'Prompt Optimization' section is a bullet list, not a refine-measure loop). It is above a 2 because the content is well-organized topically and each pattern is self-contained.

3 / 5

Progressive Disclosure

The bundle contains references/ (chain-of-thought.md, few-shot-learning.md, prompt-optimization.md, prompt-templates.md, system-prompts.md), scripts/optimize-prompt.py, and assets/ (few-shot-examples.json, prompt-template-library.md), yet the body never references any of them — the Resources section links only to external URLs. Instead, ~300 lines of pattern detail that duplicates the reference files are inlined in SKILL.md, matching the anchor 'content that clearly belongs in separate files is inlined; or references are buried.'

2 / 5

Total

11

/

20

Passed

Description

65%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 has a solid explicit trigger clause and good natural-language keywords, but the capability statement is generic marketing language ('Master advanced prompt engineering techniques to maximize...performance, reliability, and controllability') rather than named actions. Naming two or three concrete capabilities would lift it from adequate to strong.

Suggestions

Replace the abstract first sentence with concrete named actions, e.g., 'Designs few-shot examples, chain-of-thought prompts, structured-output schemas, and reusable prompt templates for LLM applications.'

Add the missing natural trigger variations users actually say — 'tune or debug a prompt', 'system prompt design', 'structured outputs / JSON mode' — to the Use-when clause.

DimensionReasoningScore

Specificity

"Master advanced prompt engineering techniques" names the domain but describes no concrete actions — there is nothing like 'designs few-shot examples', 'builds structured-output schemas', or 'optimizes prompt templates'. This matches anchor 2 ('Names the domain but actions are minimal or generic'), not 3, which requires 1-2 concrete named actions.

2 / 5

Completeness

Both parts are present: an explicit "Use when optimizing prompts, improving LLM outputs, or designing production prompt templates" clause covers 'when', and the first sentence covers 'what'. It is not a 5 because the 'what' is abstract ('master advanced techniques to maximize performance') rather than concrete capabilities.

4 / 5

Trigger Term Quality

"optimizing prompts, improving LLM outputs, designing production prompt templates" are natural phrases a user would say, giving good keyword coverage. It stays below 5 because common variations are missing — e.g., 'prompt tuning', 'fix/debug my prompt', 'system prompt', 'few-shot examples', 'structured outputs'.

4 / 5

Distinctiveness Conflict Risk

Prompt engineering is a clear niche and triggers like 'optimizing prompts' and 'production prompt templates' are unlikely to fire for unrelated skills. Minor overlap risk remains with adjacent skills (e.g., API/SDK usage or model-selection guidance), keeping it below 5.

4 / 5

Total

14

/

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
Dicklesworthstone/pi_agent_rust
Reviewed

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