SKILLEMALL.ai

BC opc-technical-due-diligence

技术尽调初筛工具,用于投资前的技术可行性评估、团队背景核查、专利验证。适用于科技创新项目的真实性核查,识别虚假技术和夸大宣传。核心能力:技术可行性评估、团队背景核查、专利备案验证、商业模式分析。

ClawHub Agent Skills author: golngod v1.0.1 MIT-0 15 files body ≈ 1 477 tokens Open the sourceclawhub.ai analyzed 12 h ago

技术尽调初筛工具,用于投资前的技术可行性评估、团队背景核查、专利验证。适用于科技创新项目的真实性核查,识别虚假技术和夸大宣传。核心能力:技术可行性评估、团队背景核查、专利备案验证、商业模式分析。

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

Integrationtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
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
This is a copy of a skill from another catalog; the rating counts the canonical one: opc-technical-due-diligence (ClawHub)

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: 15. 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. Tools declared in frontmatter
  • 100Steps. 49 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1477 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 97: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -36 of 6 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 49 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 5)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
This appears to be a real due-diligence helper, but it needs review because it handles confidential deal data and credentials, stores reports, and includes verifier behavior that can make weak reports look trustworthy.
LLM: suspicious (high) · 28 May 2026