SKILLEMALL.ai

AB sim-trading-mvp

Run a paper-trading / simulated investing workflow with explicit style selection, fixed risk rules, three decision windows per trading day, optional cron setup, persistent account and trade logs, and a post-market daily recap. Use when the user wants a simulated US stock trading account, asks the agent to act like an investor, maintain a model portfolio, make buy/sell/hold decisions, schedule trading decisions during market hours, or send a daily market/trading review. Especially use when the user cares about discipline, repeatability, and truthful reporting without fabricated data.

ClawHub Agent Skills author: QRG-cloud v0.2.1 MIT-0 9 files body ≈ 1 933 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process B 69/100 · Nearly there — weak spots: result and completion, inputs and preconditions

ProcedureInfrastructureData and analyticsAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
30
When it triggers w 12
70
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: 9. 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 69/100

    • 0Result and completion. Does not say what the result is
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 8 branches
    • 85Steps. 95 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1933 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 17 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 589: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 95 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (5 of 6)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    This appears to be a paper-trading automation skill, but it needs review because it can create persistent scheduled jobs and modify ongoing account/log state without sufficiently clear user controls.
    LLM: suspicious (medium) · VirusTotal: · 29 May 2026