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BD financial-advisor

Professional financial analysis and investment advisory skill with institutional-grade valuation capabilities. Use when users want to analyze stocks, funds, ETFs, compare investments, get market reviews, perform DCF valuation, comparable company analysis, financial statement modeling, competitive landscape analysis, macro-economic analysis, geopolitical impact assessment, or receive investment recommendations. Provides data collection, quantitative analysis, risk assessment, valuation modeling, macro-economic context, global event intelligence, news intelligence, and HTML report generation with real-time data.

ClawHub Agent Skills author: frankxia v1.0.0 MIT-0 30 files body ≈ 2 452 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerFinancetype 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
88/100
safety, quality, tests
Safety 60%
92
Quality 40%
82
Run on models
none yet
Process rating
D
46/100
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

What is at stake

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

Obfuscation 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 files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.

For the author

Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.

How to improve

    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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • medium Obfuscation obf-base64-blob SKILL.md.bak.md:13
      Long base64-looking blob
      eyJp…0k0
    • low Secrets in code secret-high-entropy-token scripts/calculate_valuation.py:375
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      'ps': safe_float(info.get('pric…ths'), 0),
      quoted
    • low Secrets in code secret-high-entropy-token scripts/fetch_stock_data.py:627
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      '市销率': info.get('pric…ths'),
      quoted
    • low Secrets in code secret-high-entropy-token scripts/fetch_stock_data.py:674
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      '市销率': info.get('pric…ths'),
      quoted

    Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "cn_name"
    • note frontmatter-key unknown frontmatter key "cn_description"

    Process rating: all ten parameters 46/100

    • 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
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 100Steps. 125 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2452 tokens
    • 100Running it twice. No mutating operations
    • low 12 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
    • -225 emoji in the instructions: noise for the model
    • -32 of 13 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 617: enough signal without eating the budget
    • +4Structure: 43 headings
    • +3Step-by-step instructions: 125 items
    • +4Has examples (11 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: suspicious
    This financial-analysis skill mostly matches its purpose, but it needs Review because it includes an unexplained encrypted backup file, automatic dependency installation, broad network collection, and high-impact investment recommendations.
    LLM: suspicious (high) · 28 May 2026