SKILLEMALL.ai

AC sxt-ab-lead-analysis

私信通(私信AI) AB 实验留资效果分析全流程 playbook。从线上埋点表 CSV 出发, 完成「会话重建 → 留资信号标注(A/B/C+宽口径) → 组间对比与置信区间 → 行业 mix 分解/标准化/商家配对 → 漏斗分层 → OR 归因(规则版特征 + LLM 反向提取 rubrics 管道) → 在线报告」。当用户要"分析 AB 实验留资率差异""实验组对照组留资对比""归因哪些 AI 动作影响留资""跑 rubrics OR 归因""复跑留资分析报告"时使用。也适用于其他二值结局(如成单/加微)的会话级 AB 归因分析。

ClawHub Agent Skills author: Hao Zhou v1.0.0 MIT-0 6 files body ≈ 527 tokens Open the sourceclawhub.ai analyzed 3 d ago

私信通(私信AI) AB 实验留资效果分析全流程 playbook。从线上埋点表 CSV 出发, 完成「会话重建 → 留资信号标注(A/B/C+宽口径) → 组间对比与置信区间 → 行业 mix 分解/标准化/商家配对 → 漏斗分层 → OR 归因(规则版特征 + LLM 反向提取 rubrics 管道) →…

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

ProcedureAI and agentsData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
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: 6. 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. 16 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 527 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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 271: enough signal without eating the budget
  • +4Structure: 4 headings
  • +3Step-by-step instructions: 16 items
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: suspicious
The skill is a coherent AB-analysis playbook, but it handles private chat and contact-lead data and sends conversation content to a remote LLM without clear privacy, consent, or data-minimization controls.
LLM: suspicious (high) · 24 Jul 2026