AF chinese-literacy-detection
面向3-12岁儿童的汉字认字量科学检测对话技能。通过对话交互，基于2500高频汉字（覆盖98.5%语料）的6层分层抽样策略与动态熔断机制，精准估算识字量。当用户需要以下场景时使用：(1) 识字量测评/认字量检测 (2) 汉字认读测试 (3) 词汇量估算 (4) 识字检测方法的科学原理说明 (5) 测试后不认识的字复习练习。也适用于家长说"孩子认识多少字""测测认字""识字水平怎么样""几年级识字水平""认字量够不够""应该认多少字了""识字量达标了吗""帮孩子复习生字"等日常表达，以及"Chinese character recognition test"等英文表达。即使用户没有明确提到"测试"，只要涉及评估或提升孩子的汉字认读能力，都应使用此技能。
As a process F 35/100 · Will not run — References files that are not bundled: assets/top_2500_chars_with_words.json
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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: 10. 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") - warning
missing-refreference to a missing file: assets/top_2500_chars_with_words.json
Process rating: all ten parameters 35/100
- 0Tools and files. 1 referenced file(s) missing: assets/top_2500_chars_with_words.json
- 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
- 100Steps. 24 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1677 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
- +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 8 example trigger phrases
- +3Description length 329: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 24 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.