SKILLEMALL.ai

BF unified-asset-advisor

统一大类资产配置分析技能(融合版)。基于宏观经济分析和申万一级行业趋势,生成中国大类资产配置建议报告。数据源采用 AKShare(底层对接国家统计局/东方财富等权威源,非黑盒)。输出三格式:HTML(可视化卡片+产业链图)、Markdown(完整分析文档)、Excel(8 Sheet结构化数据)。覆盖宏观经济→资产周期→行业筛选→产业链分析→品种操作(A股/港股通/QDII/ETF/债券基金/商品基金)→期货期权策略(含置信度评级+止损参考)。触发词:资产配置、宏观分析、大类资产、行业配置、投资建议、还有哪些资产值得关注。当用户手动选择此 skill 或使用这些触发词时使用。

ClawHub Agent Skills author: bianchunhui v1.0.1 MIT-0 9 files body ≈ 4 679 tokens Open the sourceclawhub.ai analyzed 2 d ago

统一大类资产配置分析技能(融合版)。基于宏观经济分析和申万一级行业趋势,生成中国大类资产配置建议报告。数据源采用 AKShare(底层对接国家统计局/东方财富等权威源,非黑盒)。输出三格式:HTML(可视化卡片+产业链图)、Markdown(完整分析文档)、Excel(8…

As a process F 39/100 · Will not run — References files that are not bundled: scripts/fetch_macro.py, references/asset_cycle_logic.md, assets/report_template.html

ProcedureSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
57
Run on models
none yet
Process rating
F
39/100
Will not run
References files that are not bundled: scripts/fetch_macro.py, references/asset_cycle_logic.md, assets/report_template.html
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
For the model run — optional
  • 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 description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: scripts/fetch_macro.py
  • warning missing-ref reference to a missing file: references/asset_cycle_logic.md
  • warning missing-ref reference to a missing file: assets/report_template.html
  • warning missing-ref reference to a missing file: assets/report_template.md
  • warning missing-ref reference to a missing file: references/sw_industry_list.md
  • warning missing-ref reference to a missing file: references/futures_options_guide.md
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: scripts/fetch_macro.py, references/asset_cycle_logic.md, assets/report_template.html
  • 0Tools and files. 6 referenced file(s) missing: scripts/fetch_macro.py, references/asset_cycle_logic.md, assets/report_template.html
  • 0Result and completion. Does not say what the result is
  • 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
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 4679 tokens
  • 100Steps. 58 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • low 14 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)
  • +3Output format is not stated: the model decides each time
  • -222 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 291: enough signal without eating the budget
  • +4Structure: 57 headings
  • +3Step-by-step instructions: 58 items
  • +4Has examples (30 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.

External checks

ClawHub: clean
This is a disclosed financial-analysis skill that gathers public market data and generates reports, with notable investment-risk cautions but no evidence of hidden, destructive, or credential-seeking behavior.
LLM: benign (medium) · VirusTotal: · 13 Jun 2026