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BD prompt-optimizer

分析原始提示，识别意图和差距，匹配ECC组件（技能/命令/代理/钩子），并输出一个可直接粘贴的优化提示。仅提供咨询角色——绝不自行执行任务。触发时机：当用户说“优化提示”、“改进我的提示”、“如何编写提示”、“帮我优化这个指令”或明确要求提高提示质量时。中文等效表达同样触发：“优化prompt”、“改进prompt”、“怎么写prompt”、“帮我优化这个指令”。不触发时机：当用户希望直接执行任务，或说“直接做”时。不触发时机：当用户说“优化代码”、“优化性能”、“optimize performance”、“optimize this code”时——这些是重构/性能优化任务，而非提示优化。

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 1 file body ≈ 2 330 tokens Open the sourcegithub.com↗ analyzed 25 h ago

分析原始提示，识别意图和差距，匹配ECC组件（技能/命令/代理/钩子），并输出一个可直接粘贴的优化提示。仅提供咨询角色——绝不自行执行任务。触发时机：当用户说“优化提示”、“改进我的提示”、“如何编写提示”、“帮我优化这个指令”或明确要求提高提示质量时。中文等效表达同样触发：“优化prompt”、“改进prompt”…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureDockerAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ECC

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "origin"

Process rating: all ten parameters 43/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 30Running it twice. 3 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 52 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2330 tokens

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 9 example trigger phrases
  • +3Description length 300: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 52 items
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.