SKILLEMALL.ai
GitHub ↗ RU Sign in

AD finance-data

Fetch professional stock market data from Yahoo Finance (yfinance) and SEC EDGAR. Use when: user asks about stock prices, market data, company financials, earnings, analyst recommendations, SEC filings (10-K, 10-Q, 8-K), insider transactions, options chains, dividend history, company profiles, XBRL financial concepts, or any equity research task. Supports US stocks, Chinese A-shares (e.g. 600519.SS), and international markets.

ClawHub Agent Skills author: 1shadow1 v1.0.0 MIT-0 5 files body ≈ 2 132 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

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

AnalyzerInfrastructuretype 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
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
87
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

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token scripts/yfinance_query.py:68
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "trailingPE", "forwardPE", "priceToBook", "pric…ths",
      quoted

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    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. 5 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2132 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 430: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 5 items
    • +4Has examples (24 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    This finance skill mostly matches its purpose, but its SEC filing reader is too broad and can fetch non-SEC URLs, including local file URLs.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026