SKILLEMALL.ai

AC etf-intent-gate

ETF/行业投研平台的网关级前置意图识别与安全兜底Skill。执行于用户输入之后、业务Agent集群分发之前:规则引擎过滤非法字符/prompt注入/违规话术,LLM意图识别输出结构化JSON(7种intent_type),query标准化改写(把"可以买吗"改写为投研分析指令),Agent裁剪调度与异常降级。Use when building an ETF research platform gateway, adding pre-dispatch intent classification and safety guardrails before fan-out to multiple research agents.

ClawHub Agent Skills author: nothingstop v1.0.2 MIT-0 16 files body ≈ 1 517 tokens Open the sourceclawhub.ai analyzed 3 d ago

ETF/行业投研平台的网关级前置意图识别与安全兜底Skill。执行于用户输入之后、业务Agent集群分发之前:规则引擎过滤非法字符/prompt注入/违规话术,LLM意图识别输出结构化JSON(7种intenttype),query标准化改写(把"可以买吗"改写为投研分析指令),Agent裁剪调度与异常降级。Use…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
89
Run on models
none yet
Process rating
C
53/100
Has gaps
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 Exfiltration read-dotenv SKILL.md:149
      Reads a .env file (documentation of a security skill)
      cp .env.example .env
      security skill

    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 53/100

    • 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
    • 100Tools and files. No external tools needed
    • 100Steps. 25 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1517 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
    • +2Single-language instructions
    • +3Description length 315: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: suspicious
    The skill largely matches its ETF intent-gateway purpose, but it exposes raw financial user queries in logs and task context without clear privacy controls.
    LLM: suspicious (medium) · VirusTotal: · 31 Aug 2026