SKILLEMALL.ai

AD kline-shortterm-checklist

A 股短线(日/尾盘)选股与买点核查清单技能。当用户想用技术分析纪律筛选 A 股短线标的、评估某只股票是否符合「96 原则(九不买+六不卖)/ 下午 2:30 选股法(八层筛选)/ 底部 K 线形态」、或为自选股做纪律化买前/持有核查时使用。覆盖 9 类不买、6 类不卖、12 种底部形态、八层 2:30 筛选、上升趋势研判与心态纪律,并能调用腾讯财经 API 自动拉取涨跌幅/量比/换手率/流通市值等客观指标做初筛,连接 Wind 金融数据 MCP 时还可把九不买

ClawHub Agent Skills author: HANDM-735 v1.0.5 MIT-0 13 files body ≈ 4 448 tokens Open the sourceclawhub.ai analyzed 3 d ago

A 股短线(日/尾盘)选股与买点核查清单技能。当用户想用技术分析纪律筛选 A 股短线标的、评估某只股票是否符合「96 原则(九不买+六不卖)/ 下午 2:30 选股法(八层筛选)/ 底部 K 线形态」、或为自选股做纪律化买前/持有核查时使用。覆盖 9 类不买、6 类不卖、12 种底部形态、八层 2:30…

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

TemplateAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
D
42/100
Unfinished process
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: 13. 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")
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 42/100

  • 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
  • 30Running it twice. 10 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4448 tokens
  • 100Steps. 55 steps
  • 100Consistency. Name and required fields are in place

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
  • -2localhost URLs: will not work for another user
  • -251 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 233: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 55 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 8 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
This stock-analysis skill is mostly purpose-aligned, but it needs Review because its local workbench can run file-writing scripts through an unauthenticated localhost endpoint and fetches market data with certificate checks disabled.
LLM: suspicious (high) · 21 Aug 2026