AC xiapi-heatmap-analysis
分析 A 股板块热力图，识别领涨、上升、反转行业。触发词：板块热力图、行业轮动、热门板块、领涨板块、板块分析、热力图。适用场景：分析板块热力图、识别热门行业、判断行业轮动。不适用场景：个股分析、指数分析、债券分析。
As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
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
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 58/100
- 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
- 100Tools and files. No external tools needed
- 100Steps. 76 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1736 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)
- +3Description length 107: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -242 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +4Structure: 19 headings
- +3Step-by-step instructions: 76 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.
External checks
ClawHub: clean
This skill is a focused market heatmap analysis guide that uses DaxiAPI data and shows no hidden, destructive, or unrelated behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026