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Get Started Free →面向基金投顾与客户服务领域的账户复盘任务Skill，围绕「账户复盘助手」场景提供信息抽取、结构化分析与结果输出。
.claude/skills/aifinlab-account-review/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-16 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-23 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 48% | 0% |
bashpip install -r requirements.txt
bashpython scripts/main.py --input sample.txt --output-json result.json --output-md report.md
json{ "skill": "账户复盘助手", "domain": "投顾与客户服务", "scene": "账户复盘", "input_summary": { "fund_code": "000000", "fund_name": "示例基金", "period": "2024Q4", "data_coverage": "净值/持仓/披露/市场" }, "key_findings": [ "关键结论1", "关键结论2" ], "data_quality": { "has_text": true, "text_length": 1200 }, "limitations": [ "仅基于输入信息形成初步判断" ] }
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-08 | pass→pass | 12,072 | 14,358 | +19% | 1 | 1 | 0% | 2,032 | 3,273 | +61% | 0 | 0 | — |
case-09 | pass→pass | 12,022 | 9,798 | -18% | 1 | 1 | 0% | 1,820 | 2,513 | +38% | 0 | 0 | — |
case-10 | pass→pass | 12,039 | 10,595 | -12% | 1 | 1 | 0% | 1,625 | 2,246 | +38% | 0 | 0 | — |
case-16 | fail→pass | 14,724 | 20,626 | +40% | 1 | 1 | 0% | 2,455 | 3,630 | +48% | 0 | 0 | — |
case-23 | fail→pass | 13,654 | 6,045 | -56% | 1 | 1 | 0% | 1,807 | 1,749 | -3% | 0 | 0 | — |
case-01 | fail→pass | 20,246 | 31,270 | +54% | 1 | 1 | 0% | 2,734 | 4,081 | +49% | 0 | 0 | — |
case-02 | fail→pass | 31,954 | 27,888 | -13% | 1 | 1 | 0% | 3,824 | 4,646 | +21% | 0 | 0 | — |
case-03 | pass→pass | 21,383 | 24,899 | +16% | 1 | 1 | 0% | 3,262 | 4,089 | +25% | 0 | 0 | — |
case-04 | pass→pass | 14,554 | 14,514 | -0% | 1 | 1 | 0% | 2,118 | 2,805 | +32% | 0 | 0 | — |
case-05 | fail→fail | 20,166 | 10,989 | -46% | 1 | 1 | 0% | 2,672 | 2,319 | -13% | 0 | 0 | — |
case-06 | pass→pass | 3,390 | 14,151 | +317% | 1 | 1 | 0% | 419 | 2,905 | +593% | 0 | 0 | — |
case-07 | fail→fail | 33,612 | 26,023 | -23% | 1 | 1 | 0% | 5,493 | 5,348 | -3% | 0 | 0 | — |
case-11 | fail→fail | 16,458 | 7,841 | -52% | 1 | 1 | 0% | 2,931 | 2,151 | -27% | 0 | 0 | — |
case-12 | fail→pass | 14,064 | 13,478 | -4% | 1 | 1 | 0% | 1,879 | 2,774 | +48% | 0 | 0 | — |
case-13 | pass→pass | 11,441 | 12,700 | +11% | 1 | 1 | 0% | 1,583 | 2,430 | +54% | 0 | 0 | — |
case-14 | fail→pass | 20,265 | 22,033 | +9% | 1 | 1 | 0% | 2,756 | 3,943 | +43% | 0 | 0 | — |
case-15 | fail→pass | 12,533 | 11,198 | -11% | 1 | 1 | 0% | 1,629 | 2,261 | +39% | 0 | 0 | — |
case-17 | fail→pass | 10,736 | 9,551 | -11% | 1 | 1 | 0% | 1,449 | 2,106 | +45% | 0 | 0 | — |
case-18 | fail→pass | 12,968 | 15,830 | +22% | 1 | 1 | 0% | 1,964 | 3,077 | +57% | 0 | 0 | — |
case-19 | fail→fail | 13,470 | 16,306 | +21% | 1 | 1 | 0% | 2,179 | 3,165 | +45% | 0 | 0 | — |
case-20 | pass→pass | 13,503 | 10,424 | -23% | 1 | 1 | 0% | 1,552 | 2,603 | +68% | 0 | 0 | — |
case-21 | fail→pass | 12,797 | 10,261 | -20% | 1 | 1 | 0% | 1,886 | 2,241 | +19% | 0 | 0 | — |
case-22 | pass→fail | 13,082 | 8,287 | -37% | 1 | 1 | 0% | 1,977 | 2,055 | +4% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +39 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.