SKILLEMALL.ai
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AF company-research

Multi-source company research tool that generates structured due-diligence reports. Use when the user asks to research, look up, or investigate a company — including questions about shareholders, legal representative, registered capital, equity structure, beneficial owner, funding history, investors, valuation, lawsuits, court judgments, enforcement records, blacklist / dishonest debtor status, administrative penalties, operating anomalies, trademarks, patents, government procurement / bidding, recruitment profile, negative news, competitors, or industry position. Also triggers on: "帮我查一下XX公司", "XX公司背景", "XX的股东是谁", "XX有没有诉讼/被执行/失信", "XX融了多少钱", "XX股权结构", "尽调", "公司调研", "公司背景调查", "is X company reliable", "due diligence on X", "background check on X company".

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 2 157 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Multi-source company research tool that generates structured due-diligence reports.

As a process F 42/100 · Will not run — References files that are not bundled: URL

GeneratorAI and agentsData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
F
42/100
Will not run
References files that are not bundled: URL
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: URL

Process rating: all ten parameters 42/100

Will not run. References files that are not bundled: URL
  • 0Tools and files. 1 referenced file(s) missing: URL
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 40Consistency. Frontmatter name (company-research) differs from the folder (company-search)
  • 50Failures and branches. 0 branches, has a failure section
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 56 steps
  • 100Execution cost. Instruction body is 2157 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

  • +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 10 example trigger phrases
  • +3Description length 765: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 56 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.