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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.

Quality

64%

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

Quality

Content

50%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.

A well-organized overview tier with one genuinely executable Quick Start and a clean pointer to references/details.md, but it under-delivers on its own promise: most guidance stays descriptive, the iterative optimization workflow is never sequenced, and the majority of the bundle's reference, asset, and script files are orphaned with no navigation path to them.

Suggestions

Sequence the optimization workflow explicitly with validation checkpoints (e.g. 1. Establish baseline with N samples → 2. Draft variant → 3. Evaluate against metrics → 4. Keep only if it beats baseline → 5. Repeat), since 'Prompt Optimization' is a headline capability but no procedure is given.

Add a reference map so the rest of the bundle is discoverable: link references/few-shot-learning.md, chain-of-thought.md, prompt-optimization.md, prompt-templates.md, system-prompts.md, assets/few-shot-examples.json, assets/prompt-template-library.md, and scripts/optimize-prompt.py (with its invocation) from the relevant capability sections.

Cut or merge the generic 'Best Practices', 'Common Pitfalls', and 'Success Metrics' sections (or replace with skill-specific, non-obvious guidance) and drop the 'When to Use' section that duplicates the frontmatter description.

DimensionReasoningScore

Conciseness

The Quick Start and Core Capabilities index are tight, but ~half the body restates knowledge Claude already has: 'When to Use' duplicates the frontmatter description, and 'Best Practices' ('Show, Don't Tell: Examples are more effective than descriptions'), 'Common Pitfalls', and 'Success Metrics' are generic prompt-engineering textbook advice with no skill-specific content. This is 'mostly efficient with some unnecessary explanation' rather than anchor 2's heavily padded profile, since each such section is brief and the technical core is lean.

3 / 5

Actionability

The Quick Start is a complete, executable example (ChatPromptTemplate + with_structured_output + chain invocation), but it is the only executable content; the six capability sections ('Iterative refinement workflows', 'A/B testing prompt variations', 'Self-consistency techniques') are descriptive bullets with no commands, code, or concrete procedures. Anchor 3 ('some concrete guidance but incomplete, missing key details') fits better than 4 because the bulk of the body instructs nothing.

3 / 5

Workflow Clarity

There is a logical section progression (when-to-use → capabilities → example → details → practices → pitfalls → metrics) and the Quick Start code is unambiguous, but the skill's own core process — iterative optimization (baseline → variants → evaluate → refine) — is never sequenced into steps and has no validation checkpoints. No destructive/batch operations, so no hard cap; anchor 3 ('sequence present but checkpoints missing or implicit') is the best fit.

3 / 5

Progressive Disclosure

SKILL.md cleanly signals one reference ('Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.') and details.md is one level deep, but 7 of the 9 bundle files — references/chain-of-thought.md, few-shot-learning.md, prompt-optimization.md, prompt-templates.md, system-prompts.md, assets/few-shot-examples.json, assets/prompt-template-library.md, and scripts/optimize-prompt.py — are never mentioned in SKILL.md or in details.md, so much of the bundle is undiscoverable. That is 'some structure, references not clearly signaled' rather than anchor 4's 'most content appropriately placed'.

3 / 5

Total

12

/

20

Passed

Description

61%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.

A trigger-heavy description with excellent natural trigger phrases and clear niche identity, but it omits an explicit statement of what the skill actually does — the capability side is only implied through the 'use when' list. Adding a third-person capability sentence before the trigger clause would make it fully balanced.

Suggestions

Lead with a concrete capability sentence (e.g. 'Applies few-shot, chain-of-thought, self-consistency, structured-output, and template patterns to production prompts. Design system prompts...') before the 'use when' clause so the 'what' is explicit, not inferred from triggers.

Add trigger phrases for capabilities the body covers but the description omits, e.g. 'write a system prompt', 'design the system prompt', or 'make outputs parseable as JSON'.

DimensionReasoningScore

Specificity

The description names the domain ('prompt engineering patterns for production LLM applications') but its only stated action is the generic 'apply advanced prompt engineering patterns'; the concrete items ('optimize a prompt', 'design a prompt template', 'debug prompt issues') are framed as user trigger phrases rather than capabilities the skill performs. It sits above anchor 2 (domain + minimal actions) because the quoted phrases enumerate several concrete activities, but below anchor 4 because the skill's actual actions are never explicitly stated.

3 / 5

Completeness

The 'when' is explicit and rich ("This skill should be used when the user asks to..." plus eight concrete trigger phrases), but the 'what' is only weakly present via the vague clause 'apply advanced prompt engineering patterns' — no capability statement like 'extracts/fills/merges' in the good examples. This is a mirror of anchor 3 (one side explicit, the other weak), noticeably above anchor 2's contentless 'when'-only example.

3 / 5

Trigger Term Quality

Strong natural-phrase coverage with multiple synonyms users would actually say: 'optimize a prompt', 'improve prompt performance', 'write better prompts', 'debug prompt issues', 'use chain-of-thought', 'structured prompting', 'few-shot prompting'. It falls short of anchor 5 because a few natural terms for covered capabilities are missing (e.g. 'system prompt' work and prompt evaluation/refinement phrasings).

4 / 5

Distinctiveness Conflict Risk

'Prompt engineering' is a clear niche and the quoted triggers ('few-shot prompting', 'structured prompting', 'debug prompt issues') are distinctive to it, so it would rarely fire for unrelated skills. Minor overlap remains with closely related API/LLM-development skills whose users might also say 'structured outputs' or 'improve prompt performance', keeping it below anchor 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

wshobson/agents

Path

plugins/llm-application-dev/skills/prompt-engineering-patterns/SKILL.md

Reviewed

Table of Contents

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