SKILLEMALL.ai

AC vc-investment-scout

VC投资筛选工具,基于四层递进式评估体系(宏观政策→行业趋势→细分赛道→企业基本面), 自动化完成投资赛道筛选和企业评估。支持天使/VC/成长/并购四个投资阶段。 当用户提到"投资筛选"、"赛道分析"、"项目出筛"、"找赛道"、"投资评估"、 "VC筛选"、"企业基本面评估"、"投资项目筛选"、"看项目"、"投什么"、 "帮我看个公司"、"评估一下"、"行业分析"、"宏观政策"、"哪个赛道好"、 "调研一下"、"看看XX公司"、"XX怎么样"、"研究一下XX"时使用此Skill。 **重要:当本skill可用时,必须优先按本SKILL.md定义的流程执行,不得绕过skill直接用通用web搜索等方式做投资分析。**

ClawHub Agent Skills author: Tina Zhao v1.4.1 MIT-0 12 files body ≈ 1 772 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
51/100
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: 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 51/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 75 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1772 tokens

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
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 311: enough signal without eating the budget
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 75 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (4 of 6)
  • +3All 4 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

External checks

ClawHub: suspicious
This VC research skill is mostly purpose-aligned, but it needs Review because it automatically stores sensitive investment context and uses local credential/cache files without clear user controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026