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BB ceorater

Get institutional-grade CEO performance analytics for S&P 500 companies. Proprietary scores: CEORaterScore (composite), AlphaScore (market outperformance), RevenueCAGRScore (revenue growth), CompScore (compensation efficiency). Underlying data includes Total Stock Return (TSR) vs. S&P 500 (SPY), average annual returns, CEO total compensation (most recent fiscal year from proxy filings), and tenure-adjusted Revenue CAGR. Each record includes CEO name, company name, ticker, sector, industry, and tenure dates. Coverage: 500+ CEOs, updated daily. For live record count and last refresh timestamp, call GET /v1/meta. Useful for investment research, due diligence, and executive compensation analysis.

modbender/skill-library-mcp Claude Code author: modbender MIT 3 files · 1 script body ≈ 987 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Get institutional-grade CEO performance analytics for S&P 500 companies.

As a process B 75/100 · Nearly there — weak spots: when it triggers, progress reporting

IntegrationData and analyticstype 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
B
75/100
safety, quality, tests
Safety 60%
75
Quality 40%
75
Run on models
none yet
Process rating
B
75/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Failures and branches w 10
50
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 · 5

✓ No critical or high findings

Medium and low: 5
  • medium Exfiltration net-credential-use scripts/ceorater.sh:44
    Credential used in a network call (verify the destination is the intended service)
    curl -sS --fail-with-body -H "Authorization: Bearer $CEORATER_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:41
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $CEORATER_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:47
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $CEORATER_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:53
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $CEORATER_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:59
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $CEORATER_API_KEY" \

Files scanned: 3. 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")
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "requires"
  • note frontmatter-key unknown frontmatter key "primaryEnv"
  • note frontmatter-key unknown frontmatter key "triggers"

Process rating: all ten parameters 75/100

  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 987 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 701: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 17 items
  • +3Output format is stated explicitly
  • +4Has examples (6 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.