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AA stockaskill

Multi-market intelligent stock selection for A-share/HK/US stocks and ETFs. Use when user asks about stock analysis, market scanning, portfolio construction, factor screening, quantitative strategy signals, fund screening, backtesting, or investment diagnosis. Triggers on stock codes (600519, AAPL, 0700.HK), Chinese stock names (贵州茅台), fund codes (510300), and queries with keywords like "分析", "选股", "scan", "portfolio", "backtest", "因子", "评分", "BUY/SELL/HOLD", or "诊断".

ClawHub Agent Skills author: axjing v0.1.0 MIT-0 13 files body ≈ 1 771 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Multi-market intelligent stock selection for A-share/HK/US stocks and ETFs.

As a process A 81/100 · Runs to the end — weak spots: progress reporting

ProcedureSoftware developmentData 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
A
99/100
safety, quality, tests
Safety 60%
100
Quality 40%
97
Run on models
none yet
Process rating
A
81/100
Runs to the end
Progress reporting w 2
0
Failures and branches w 10
50
Result and completion w 14
60
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 · 0

    ✓ No critical or high findings

    Files scanned: 13. 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 81/100

    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 28 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1771 tokens
    • 100Running it twice. No mutating operations
    • low 10 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 472: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 28 items
    • +3Output format is stated explicitly
    • +4Has examples (13 code blocks)
    • +4Reference files are cited in the instructions (11 of 11)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 97.

    External checks

    ClawHub: clean
    This is a disclosed stock-research skill that uses public market data, local caching, and report files, with some methodology and scoping caveats users should review.
    LLM: benign (high) · VirusTotal: · 30 Jun 2026