SKILLEMALL.ai

BD fund-cio-copilot

产业基金 CIO Copilot —— 面向产业基金 GP / 投资总监 / 投委会的投资决策辅助 Agent。覆盖 Screening(BP 初筛)→ Evaluation(立项分析)→ IC(投委会)→ Portfolio(投后管理/生命周期闭环)→ Radar(地方国资投资周报/产业雷达)全流程,内置五层分析引擎、Decision Memory 机构记忆与四件套对象族(Decision/Monitoring/Outcome/Learning)。辅助决策,不做投资决定。触发词:筛 BP / 初筛 / 立项分析 / 投委 / IC / 上会 / 投后 / 跟踪 / 里程碑 / 复盘 / 基金决策 / 产业基金 / 周报 / 产业雷达。

ClawHub Agent Skills author: perrykono-debug v2.6.2 MIT-0 14 files body ≈ 4 242 tokens Open the sourceclawhub.ai analyzed 2 d ago

产业基金 CIO Copilot —— 面向产业基金 GP / 投资总监 / 投委会的投资决策辅助 Agent。覆盖 Screening(BP 初筛)→ Evaluation(立项分析)→ IC(投委会)→ Portfolio(投后管理/生命周期闭环)→…

As a process D 45/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
45/100
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: 14. 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 45/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4242 tokens
  • 100Steps. 97 steps
  • 100Consistency. Name and required fields are in place
  • low 17 top-level sections: this looks like several domains in one skill

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
  • -234 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 322: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 97 items
  • +4Has examples (1 code blocks)

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

External checks

ClawHub: suspicious
The skill is not malware, but it should be reviewed because it can automatically retain sensitive investment records and create external Tencent Docs.
LLM: suspicious (high) · VirusTotal: · 5 Sept 2026