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prompt-engineer

Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)

42

Quality

42%

Does it follow best practices?

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/prompt-engineer/SKILL.md

The canonical home for this skill is prompt-engineer in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

31%Scale 1-5

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

This skill is severely over-engineered for its purpose. It extensively explains prompting frameworks and detection heuristics that Claude already understands, resulting in extreme verbosity. While the framework mapping table and examples provide some actionable structure, the workflow has a missing step (Step 2) and lacks proper error recovery loops. The entire content is inlined with no progressive disclosure despite being well over 200 lines.

Suggestions

Reduce content by 60-70%: remove the framework rationale column, detection patterns section, and explanations of what each framework does—Claude already knows these frameworks. Focus on the mapping table and 1-2 concise examples.

Fix the missing Step 2 in the workflow (it jumps from Step 1 to Step 3) and add a feedback loop: if quality checks fail, specify what corrective action to take before presenting output.

Split the framework reference table and examples into separate bundle files (e.g., FRAMEWORKS.md, EXAMPLES.md) and reference them from the main SKILL.md to improve progressive disclosure.

Remove the 'Notes' section explaining platform-agnosticism—this is unnecessary meta-commentary that doesn't help Claude execute the skill.

DimensionReasoningScore

Conciseness

Extremely verbose. Extensively explains prompting frameworks Claude already knows, includes lengthy framework mapping tables, detection patterns for simple vs complex tasks, and multiple full-length examples that bloat the content. The skill could be reduced to ~30% of its size without losing actionable information. It also explains basic concepts like what 'ambiguous tasks' are and provides rationale columns in tables that Claude doesn't need.

1 / 5

Actionability

The skill provides a framework mapping table and examples of input/output, which is somewhat concrete. However, the core guidance is more descriptive than executable—it tells Claude to 'analyze intent' and 'select frameworks' but the actual transformation logic is implicit. The examples help but are more illustrative than providing a reusable pattern. There's no executable code since this is an instruction-only skill, but the instructions remain at a moderate level of specificity.

3 / 5

Workflow Clarity

Steps are listed (Analyze, Select, Generate) but Step 2 is missing from the numbered sequence (jumps from Step 1 to Step 3), which is a significant gap. The quality checks in 4.6 provide a validation checklist, but there's no feedback loop for when the generated prompt fails quality checks—it just says 'verify' without specifying what to do on failure. The workflow is present but has structural errors and missing validation loops.

3 / 5

Progressive Disclosure

The content is a monolithic wall of text with no bundle files or external references. The lengthy framework mapping table, multiple full examples, and detailed rules are all inlined when they could be split into separate reference files (e.g., FRAMEWORKS.md, EXAMPLES.md). For a skill this long (~200+ lines), the lack of any content splitting is a significant organizational weakness.

2 / 5

Total

9

/

20

Passed

Description

53%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 identifies a clear niche (prompt optimization) and lists specific frameworks, which aids distinctiveness. However, it lacks a 'Use when...' clause, misses natural user trigger phrases like 'improve my prompt' or 'prompt engineering', and doesn't elaborate on what the transformation actually produces or how it benefits the user.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks to improve, rewrite, or optimize a prompt, or mentions prompt engineering.'

Include natural trigger terms and synonyms users would say: 'improve my prompt', 'rewrite prompt', 'prompt engineering', 'better prompt', 'prompt refinement'.

Briefly describe the output or benefit, e.g., 'Produces structured, more effective prompts with clear instructions, context, and constraints.'

DimensionReasoningScore

Specificity

The description names the domain (prompt optimization) and one concrete action (transforms prompts), but doesn't elaborate on what the transformation entails beyond listing framework acronyms. It lacks detail on outputs or specific capabilities beyond naming frameworks.

3 / 5

Completeness

The 'what' is reasonably clear (transforms prompts using specific frameworks), but there is no explicit 'when' clause. There's no 'Use when...' guidance, which per the rubric caps completeness at 3.

3 / 5

Trigger Term Quality

Includes 'prompts' and 'optimized prompts' which are relevant, and lists specific framework names (RTF, RISEN, Chain of Thought, etc.) that some users might reference. However, it misses natural phrases users would say like 'improve my prompt', 'rewrite prompt', 'prompt engineering', 'better prompt', or 'prompt refinement'.

3 / 5

Distinctiveness Conflict Risk

The specific focus on prompt optimization using named frameworks creates a fairly distinct niche. The listing of specific framework acronyms (RTF, RISEN, RODES, etc.) helps differentiate it from general writing or editing skills, though there could be minor overlap with general writing improvement skills.

4 / 5

Total

13

/

20

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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