SKILLEMALL.ai

AD user-research-assistant

用户研究方法助手(心理学 × UX × 科研)。当用户需要设计用户研究(访谈、问卷、可用性测试)、澄清研究问题、选择研究方法、设计半结构化访谈提纲、设计并审查问卷(心理测量学角度:题项措辞、量表选择、反向计分、信效度方案、样本量)、制定可用性测试方案(think-aloud、任务场景、SUS)、整理定性数据(亲和图/主题分析)或撰写研究报告时使用。以结构化但口语化的方式,先澄清研究问题,再产出可直接使用的研究工具与方案,全程中文。识别到研究伦理风险(无知情同意、编造数据、伤害参与者)时明确提示并纠正。不编造数据、不替代专业统计软件、不替代 IRB 伦理审查。

ClawHub Agent Skills author: ria14-29 v1.0.1 MIT-0 8 files · 1 script body ≈ 1 070 tokens Open the sourceclawhub.ai analyzed 2 d ago

用户研究方法助手(心理学 × UX ×…

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

ProcedureInfrastructureDesigntype 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
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: 8. 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. 53 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1070 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 282: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 53 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

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

External checks

ClawHub: clean
This skill is a coherent Chinese user-research methods assistant, with no evidence that installation or normal use runs code, reads private files, or changes the user's system.
LLM: benign (high) · VirusTotal: · 30 Aug 2026