AB coding-prompt
AI coding prompt optimizer and coach. This skill should be used whenever the user is writing programming prompts or instructions to an AI during active coding sessions— including when starting new features, correcting AI's direction, reviewing code, or requesting tests. Trigger when: explicit request to optimize/improve/refine a prompt, the user activates this skill (激活编程提示词), or during coding tasks where instructions to AI are vague, missing constraints, missing acceptance criteria, or could benefit from prompt engineering best practices. Also trigger when the user says "更新技能" or "update skill" to evolve this skill's knowledge base. Do NOT trigger for non-coding prompts or general chat.
As a process B 73/100 · Nearly there — weak spots: inputs and preconditions, progress reporting
How to improve
- 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: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 3, column 14: description: AI coding prompt optimizer and coach. This skill should be used wh… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 73/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 55Failures and branches. 1 branches
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1684 tokens
- 100Running it twice. Mutating operations check current state
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)
- -212 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 696: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 20 items
- +3Output format is stated explicitly
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.