SKILLEMALL.ai

AC resume-parser-claw

简历标准化解析虾 — 将非标简历(PDF/Word/图片/文本)解析为结构化候选人数据,并可写入飞书多维表格或导出 Excel/JSON。 **当以下情况时使用此 Skill**: (1) 用户上传或提供简历文件(PDF、Word、图片),要求提取候选人信息 (2) 需要批量解析简历并录入数据库或飞书多维表格 (3) 需要提取技能标签、工作年限、最高学历等结构化字段 (4) 需要对简历进行去重、质量评分或格式统一 (5) 用户提到"解析简历"、"简历录入"、"简历标准化"、"提取技能"、"简历筛选"、"候选人信息"、"简历数据库"、"批量简历"、"简历去重"、"人才数据"

ClawHub Agent Skills author: Ricky v1.0.0 MIT-0 5 files body ≈ 378 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

ProcedureData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
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: 5. 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. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 378 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 290: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (1 code blocks)
  • +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: 79.

External checks

ClawHub: suspicious
This resume-processing skill appears useful, but it handles sensitive applicant data and can export it to Feishu Bitable or files without enough disclosed consent, destination, or retention controls.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026