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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.

53

Quality

67%

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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 skill defines a coherent persona and a well-sequenced collaborative workflow with useful sample dialogues, but it is significantly padded with persona rhetoric and textbook definitions of prompting techniques Claude already knows. It also keeps all reference material inline and never shows a concrete example of its own final deliverable.

Suggestions

Cut the persona rhetoric (Final Mandate, '灵魂在于灵活性' passages) and the Knowledge Base definitions of CoT/Few-shot/ToT/ReAct — Claude already knows these techniques; keep only the skill-specific guidance on when to recommend each one.

Move the Knowledge Base Reference and Example Scenarios sections into a references/ file (e.g. references/techniques.md) linked one level deep from SKILL.md, slimming the main file to the workflow and behavioral rules.

Add one complete example of the Phase 3 final deliverable (design rationale + full copy-paste prompt) so the output format is demonstrated rather than just described.

DimensionReasoningScore

Conciseness

The ~240-line body contains substantial padding: the Knowledge Base Reference section re-explains techniques Claude already knows ("思维链 (CoT):展示分步推理过程", "Few-shot 提示:提供示例让 AI 模仿学习"), and the Final Mandate is pure rhetoric ("永远保持灵动", "永远追求优雅"). This is noticeably verbose with several sections that add no operational value, matching anchor 2 rather than 3 because the padding is pervasive, not incidental.

2 / 5

Actionability

There is concrete guidance — sample dialogues for each phase, a signal-to-technique mapping ("创意生成任务 → 思想树(ToT) + 自洽性"), and explicit behavioral rules ("绝不直接修改,等待回应") — but key details are missing: the final deliverable section lists requirements ("无状态设计", "清晰的结构") without ever showing an example output prompt, so Claude must improvise the actual artifact. This sits between anchor 3 and 4, below 4 because the core deliverable format is never concretely demonstrated.

3 / 5

Workflow Clarity

The three phases (诊断与探询 → 协作构建 → 最终交付) are clearly sequenced, with user-response checkpoints stated repeatedly ("永远等待关键决策点的回应") and quality-tier branching (初步想法/半成品/高质量成品). It is not a destructive or batch operation, so no validation cap applies, but there is no explicit check that a delivered prompt meets the listed quality standards, keeping it below anchor 5.

4 / 5

Progressive Disclosure

Section headers are clear and consistent, but the skill is a single monolithic file with no references/, scripts/, or assets/ bundle, and the Knowledge Base Reference and Example Scenarios sections are exactly the kind of detail content that belongs in separate one-level-deep reference files. This matches anchor 3: some structure present, but content that should be separate is inline.

3 / 5

Total

12

/

20

Passed

Description

70%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 solid description with an explicit activation clause and well-chosen natural trigger phrases, written in third person. Its main weakness is that the capability statement is padded with persona language ("Alpha-Prompt", "master prompt engineer") instead of enumerating the concrete actions the skill performs.

DimensionReasoningScore

Specificity

The description names the domain ("prompt engineering") and one to two concrete actions ("collaboratively crafts high-quality prompts through flexible dialogue"), but the rest is persona framing ("transforms Claude into 'Alpha-Prompt' - a master prompt engineer") rather than additional concrete capabilities. It matches anchor 3 (domain plus 1-2 actions, not comprehensive) rather than 4, which requires several distinct specific actions such as diagnosing, restructuring, or testing prompts.

3 / 5

Completeness

Both parts are present: the 'what' ("collaboratively crafts high-quality prompts through flexible dialogue") and an explicit 'when' clause ("Activates when user asks to...") with concrete trigger phrases. Not a 5 because the 'what' leans on persona over-claiming ("master prompt engineer") rather than plainly stating what the skill does, leaving it slightly less explicit than the anchor-5 example.

4 / 5

Trigger Term Quality

It quotes natural trigger phrases users would say — "optimize prompt", "improve system instruction", "enhance AI instruction", "prompt engineering tasks" — giving good keyword coverage. Not a 5 because common variations like "write/design a prompt", "fix my prompt", or "system prompt" phrasing are missing.

4 / 5

Distinctiveness Conflict Risk

The prompt-engineering niche has distinct, targeted triggers ("optimize prompt", "improve system instruction") that are unlikely to fire for unrelated skills. Minor overlap risk remains with general writing or Claude-API skills because "prompt engineering tasks" is a broad catch-all; not a 5 for that reason.

4 / 5

Total

15

/

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

labring/FastGPT

Path

.agents/skills/core/ai/prompt_optimize/SKILL.md

Reviewed

Table of Contents

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