AB multilingual-learning-sprint
Multilingual Learning Sprint is an adaptive language-learning coach for rapid, interest-based plans across English, Japanese, Chinese, Spanish, French, Korean, German, Arabic, and other languages. Use when a user wants to assess language ability, choose enjoyable content, build a 7/14/30-day crash-course plan, practice speaking, writing, reading, or listening, run spaced-repetition quizzes, schedule periodic review tests, or expose the learning service through Alipay AI Pay and JD ClawTip A2A payment. It emphasizes placement diagnostics, CEFR-style scoring, content personalization, targeted lesson design, measurable reinforcement, Alipay HTTP 402 pay-per-use, and ClawTip/X402 order metadata.
Multilingual Learning Sprint is an adaptive language-learning coach for rapid, interest-based plans across English, Japanese, Chinese, Spanish, French…
As a process B 69/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, running it twice
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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 69/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 51 steps
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2655 tokens
- low 10 top-level sections: this looks like several domains in one skill
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)
- -31 of 3 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 700: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 51 items
- +3Output format is stated explicitly
- +4Has examples (8 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.