SKILLEMALL.ai

AC buyback-screen

筛选A股(沪深北交所)最近发布回购公告(回购计划/预案)的上市公司,并按市盈率(PE-TTM)、 市净率、总市值、回购金额、用途、进度等条件过滤,输出清单表格。数据来自东方财富数据中心。 当用户提到"回购公告/回购预案/回购进展/筛选回购/回购名单/哪些公司最近回购"、 "回购+市盈率/PE/估值过滤"、"注销式回购"、"大额回购"、或"最近有哪些公司回购了" 等需求时,务必使用此技能,即使没有明确说"skill"。也适用于把回购公司按 PE≤X 进一步筛选、 按回购金额/用途/进度筛选、导出回购清单 CSV/TSV 等场景。

ClawHub Agent Skills author: lucky-dreamer v1.0.0 MIT-0 3 files body ≈ 604 tokens Open the sourceclawhub.ai analyzed 3 d ago

筛选A股(沪深北交所)最近发布回购公告(回购计划/预案)的上市公司,并按市盈率(PE-TTM)、 市净率、总市值、回购金额、用途、进度等条件过滤,输出清单表格。数据来自东方财富数据中心。 当用户提到"回购公告/回购预案/回购进展/筛选回购/回购名单/哪些公司最近回购"、…

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

ProcedureData and analyticsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
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: 3. 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. 24 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 266: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 24 items
  • +4Has examples (3 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: clean
This skill is a coherent A-share buyback screener that uses public market-data APIs and writes a local TSV result file.
LLM: benign (high) · VirusTotal: · 8 Aug 2026