AF score-analyzer
Analyze student score data from Excel files and generate professional analysis reports. Use when the user provides an Excel score sheet (.xlsx), asks to analyze student scores, test results, exam data, or grade data. The Agent performs all analysis directly using Python scripts (pandas, matplotlib) and its own intelligence for report writing—no external LLM API needed during execution. Supports data cleaning, statistical analysis, chart generation (score distribution, class comparison, radar, trends, boxplot, heatmap, deviation, top-bottom), narrative report writing, and ZIP package output. Triggers: "analyze scores", "score report", "exam analysis", "成绩分析", "成绩报告", "学生成绩", "score sheet", "upload excel for analysis", "analyze test results", "成绩统计", "前10名", "成绩对比", "班级成绩".
Analyze student score data from Excel files and generate professional analysis reports.
As a process F 38/100 · Will not run — References files that are not bundled: references/analysis_prompt.md
How to improve
- 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: 0. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: references/analysis_prompt.md
Process rating: all ten parameters 38/100
- 0Tools and files. 1 referenced file(s) missing: references/analysis_prompt.md
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 5 mutating operations with no state check
- 40Consistency. Frontmatter name (score-analyzer) differs from the folder (score-analyzer-skill)
- 85Steps. 23 steps, 1 vague phrases
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 2179 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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 6 example trigger phrases
- +3Description length 782: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 23 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.