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prompt-optimize

Expert prompt engineering skill that transforms Claude into "Alpha-Prompt" - a master prompt engineer who collaboratively crafts high-quality prompts through flexible dialogue. Activates when user asks to "optimize prompt", "improve system instruction", "enhance AI instruction", or mentions prompt engineering tasks.

57

Quality

65%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./.agents/skills/core/ai/prompt_optimize/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

48%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 content is well-structured with a clear phased workflow and explicit wait-for-response checkpoints, but it is verbose (explaining prompt-engineering fundamentals Claude already knows) and lacks concrete copy-paste deliverable templates, with everything crammed into one file.

Suggestions

Trim the Knowledge Base section to only the technique-to-signal mapping; Claude already knows what CoT/ToT/ReAct are, so the definitions are wasted tokens.

Add at least one concrete, copy-paste-ready final-deliverable prompt template (with XML/Markdown structure) so the 'Phase 3: 最终交付' guidance is executable rather than described.

Move the Example Scenarios and Knowledge Base into separate reference files (e.g., references/techniques.md, references/examples.md) referenced one level deep from SKILL.md to improve progressive disclosure and cut the inlined bulk.

DimensionReasoningScore

Conciseness

The ~240-line body spends substantial tokens explaining concepts Claude already knows (definitions of CoT, Self-Consistency, ToT, ReAct, Few-shot, Zero-shot) and repeats guidance across 'Operating Principles', 'Quality Standards', and 'Important Reminders', making it noticeably verbose with several padded sections.

2 / 5

Actionability

The three-phase workflow and example dialogues give some concrete, usable guidance (signal-to-technique mapping, deliverable checklist), but there is no copy-paste-ready prompt template or output schema and much of the guidance stays abstract ('激发用户的灵感', '永远追求优雅'), leaving key execution details implicit.

3 / 5

Workflow Clarity

The 诊断 → 协作构建 → 最终交付 sequence is clearly phased and reinforced with explicit 'wait for user response' checkpoints (e.g., '重要:绝不直接动手修改,等待回应'), though the checkpoints are restated informally rather than crisply structured as a validate-proceed loop.

4 / 5

Progressive Disclosure

There is clear section-level structure, but no bundle files exist and the large Knowledge Base of techniques plus multiple example scenarios are all inlined into a single ~240-line SKILL.md rather than split into one-level-deep reference files.

3 / 5

Total

12

/

20

Passed

Description

82%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 is strong: it explicitly pairs a clear "what" with concrete "when" trigger phrases and occupies a distinct niche. The main weakness is specificity, since it leads with persona framing rather than enumerating concrete actions.

Suggestions

Lead the description with a concrete verb-list of actions (e.g., 'Crafts, refactors, and stress-tests prompts...') before the persona framing to raise specificity.

Add common natural trigger variants like 'write a system prompt', 'design AI role/persona', or 'refine a prompt' to broaden trigger coverage.

DimensionReasoningScore

Specificity

Names the prompt-engineering domain and a concrete action ("collaboratively crafts high-quality prompts through flexible dialogue") but frames the skill as a persona rather than listing several specific concrete actions, so it stops at the 1-2-actions anchor rather than comprehensive coverage.

3 / 5

Completeness

Explicitly answers both what ("crafts high-quality prompts through flexible dialogue") and when ("Activates when user asks to ...") with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

Includes natural phrases users would say ("optimize prompt", "improve system instruction", "enhance AI instruction", "prompt engineering"), giving good keyword coverage, though common variants like "write a prompt" or "system prompt" are missing.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear prompt-engineering niche with distinct, specific triggers ("optimize prompt", "improve system instruction", "enhance AI instruction"), giving minimal conflict risk with other skills.

5 / 5

Total

17

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
labring/FastGPT
Reviewed

Table of Contents

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