SKILLEMALL.ai

AC sector-anomaly

分析A股板块异动(放量、资金流入、涨停潮、个股联动),输出板块异动评分、强势/出货判断和操作建议。用于"看看哪些板块异动""分析XX板块是不是异动""今天资金流向哪些板块""哪些板块有异动信号"等场景。可接入本地 SQLite 行情库和 stock-pool-v2.json 板块映射。

ClawHub Agent Skills author: Wei Wu v1.0.0 MIT-0 5 files body ≈ 500 tokens Open the sourceclawhub.ai analyzed 2 d ago

分析A股板块异动(放量、资金流入、涨停潮、个股联动),输出板块异动评分、强势/出货判断和操作建议。用于"看看哪些板块异动""分析XX板块是不是异动""今天资金流向哪些板块""哪些板块有异动信号"等场景。可接入本地 SQLite 行情库和 stock-pool-v2.json 板块映射。

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

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
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: 5. 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. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 500 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

  • +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 4 example trigger phrases
  • +3Description length 143: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

External checks

ClawHub: clean
This skill coherently analyzes local A-share market sector data and does not show hidden, destructive, or unrelated behavior.
LLM: benign (high) · VirusTotal: · 9 Aug 2026