SKILLEMALL.ai

AD worldcup-2026-analysis

2026世界杯/足球比赛量化分析与预测引擎。基于Elo评级+Dixon-Coles泊松+蒙特卡洛模拟,结合网页搜索获取最新赛果/伤病/赔率进行实时校准。提供单场胜平负预测、比分概率、小组出线概率、淘汰赛晋级概率、爆冷分析、赔率价值检测。当用户要求分析足球比赛、预测结果、查看晋级/出线概率、对比球队实力、检测赔率价值、查询世界杯赛程比分赔率、或提到足球/世界杯/欧冠/英超/五大联赛等赛事分析时使用。

ClawHub Agent Skills author: luciuszhao0101-dot v2.1.1 MIT-0 12 files body ≈ 1 156 tokens Open the sourceclawhub.ai analyzed 2 d ago

2026世界杯/足球比赛量化分析与预测引擎。基于Elo评级+Dixon-Coles泊松+蒙特卡洛模拟,结合网页搜索获取最新赛果/伤病/赔率进行实时校准。提供单场胜平负预测、比分概率、小组出线概率、淘汰赛晋级概率、爆冷分析、赔率价值检测。当用户要求分析足球比赛、预测结果、查看晋级/出线概率、对比球队实力、检测赔率价值、…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
D
46/100
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: 12. 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 46/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 48 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1156 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 200: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 48 items
  • +4Has examples (3 code blocks)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This football analysis skill is purpose-aligned and transparent, with local calibration and optional sports-data API use that fit its stated prediction workflow.
LLM: benign (high) · VirusTotal: · 12 Jun 2026