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系统化投研能力中枢。覆盖宏观/行业/政策/个股/投资可行性/风险/投资规划七类研究,内置9条研究红线、深度研究5步法、7专题框架(每类型含专属独门分析方法)、6研究模板(分析引导器结构:独门分析方法层+材料分析示范)、8重点行业一手源清单,以及多源数据采集脚本(SEC EDGAR/HKEX/AKShare/FRED/政策门户等)。Use for: 任何投资研究、行业分析、公司研究、个股深度研究、政策解读、可行性评估、风险分析、投资规划,以及研究所需的真实一手数据采集。纯研究,不做盯盘/交易信号/交易执行。

ClawHub Agent Skills author: Garming v1.0.8 MIT-0 42 files body ≈ 1 031 tokens Open the sourceclawhub.ai analyzed 30 h ago

系统化投研能力中枢。覆盖宏观/行业/政策/个股/投资可行性/风险/投资规划七类研究,内置9条研究红线、深度研究5步法、7专题框架(每类型含专属独门分析方法)、6研究模板(分析引导器结构:独门分析方法层+材料分析示范)、8重点行业一手源清单,以及多源数据采集脚本(SEC…

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

ProcedureSoftware developmentResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
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

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: 42. 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 (bash) that frontmatter does not declare
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1031 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
    • -32 of 18 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 255: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (2 code blocks)
    • +1License stated

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

    External checks

    ClawHub: clean
    This is a disclosed investment-research skill that gathers public financial and policy data, writes local research artifacts, and does not show evidence of hidden exfiltration, destructive behavior, or trade execution.
    LLM: benign (high) · VirusTotal: · 10 Jun 2026