AC prompt-engineering-expert
AI回答总是不够好？问题大概率出在你的提问方式。不是给你模板死记硬背，而是教你Role-Task-Format-CoT四大核心维度的设计原理，让你针对任何模型、任何场景都能写出高质量prompt。覆盖GPT/Claude/Gemini/国产大模型，掌握底层原理一通百通。附Prompt诊断修复表，12种常见问题一键定位。 触发词：提示词优化、写提示词、prompt工程、怎么问AI、让AI更好的回答、角色扮演prompt、Few-shot示例、思维链CoT、prompt模板、AI指令设计、提示词技巧、让AI更听话、system prompt、角色设定、AI回答不好、AI输出质量、prompt技巧、chain of thought、高质量prompt、prompt框架、结构化提问、AI控制输出格式、让AI输出JSON、prompt engineering、提示词工程师、prompt调试、AI调教、调教AI、prompt设计模式、怎么让AI输出稳定、高级prompt技巧、让AI听话的秘诀、AI输出不稳定、CoT提示词、few-shot写法、structured prompting 排除：代码生成（用编程技能）、长文本创作（用写作技能）、纯聊天
AI回答总是不够好？问题大概率出在你的提问方式。不是给你模板死记硬背，而是教你Role-Task-Format-CoT四大核心维度的设计原理，让你针对任何模型、任何场景都能写出高质量prompt。覆盖GPT/Claude/Gemini/国产大模型，掌握底层原理一通百通。附Prompt诊断修复表，12种常见问题一键定位…
As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 57/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (prompt-engineering-expert) differs from the folder (mimo-prompt-engineering-expert)
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 21 steps
- 100Execution cost. Instruction body is 419 tokens
- 100Running it twice. No mutating operations
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +1No license
- +2Single-language instructions
- +3Description length 525: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 21 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.