SKILLEMALL.ai

AC china-macro-climate

中国宏观景气分析 — 从增长、通胀、汇率、利率、信用五维度综合打分, 判断经济周期阶段(美林时钟),输出大类资产配置建议。 触发场景:(1) 宏观分析/宏观景气/经济周期判断 (2) 大类资产配置建议 (3) 美林时钟/投资时钟分析 (4) 中国经济五维度诊断 (5) 汇率/利率/信用/通胀/增长综合评估。

ClawHub Agent Skills author: StevenGE791 v1.0.0 MIT-0 6 files body ≈ 604 tokens Open the sourceclawhub.ai analyzed 2 d ago

中国宏观景气分析 — 从增长、通胀、汇率、利率、信用五维度综合打分, 判断经济周期阶段(美林时钟),输出大类资产配置建议。 触发场景:(1) 宏观分析/宏观景气/经济周期判断 (2) 大类资产配置建议 (3) 美林时钟/投资时钟分析 (4) 中国经济五维度诊断 (5) 汇率/利率/信用/通胀/增长综合评估。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureResearchSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.
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: 6. 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")

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 40 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 604 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

  • +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
  • +1No license
  • +2Single-language instructions
  • +3Description length 154: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
This is a transparent China macro-analysis skill that gives market-cycle and asset-allocation guidance, with no hidden access or automation found.
LLM: benign (high) · VirusTotal: · 28 May 2026