SKILLEMALL.ai

AC midas-skill

Midas Skill — Turn Your Repeated Orders Into Gold. A self-learning wealth extraction engine that takes the mundane, repetitive information streams of your daily life — office small talk, casual photos, browsing history, group chats, complaints, autopilot purchases — and distills them into actionable money signals, demand gaps, arbitrage windows, and monetization paths. You don't need a Bloomberg terminal. You need to stop ignoring the wealth signals you already encounter every day. Midas also maintains pre-built "wealth operating systems" of famous figures (Musk, Buffett, Thiel, etc.) and pattern-matches YOUR signals against THEIR playbooks to generate personalized strategies. Nuwa distills how people think. Midas distills how to turn your noise into money. Trigger words: "Midas", "turn this into gold", "what money signal is here", "deal analysis", "noise to gold", "mine this", "golden touch", "点石成金", "what am I missing", "Midas mode"

ClawHub Agent Skills author: realteamprinz v1.0.0 MIT-0 26 files body ≈ 3 341 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

ProcedureInfrastructureCustomer supporttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Consistency w 8
40
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: 26. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "trigger_words"

    Process rating: all ten parameters 59/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (midas-skill) differs from the folder (midas)
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 100Steps. 73 steps
    • 100Execution cost. Instruction body is 3341 tokens
    • 100Progress reporting. Reports progress

    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)
    • +3Description length 953: 120–800 characters recommended
    • -44 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 73 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: suspicious
    This skill is not executable malware, but it asks users to mine highly sensitive personal and workplace data over time with insufficient upfront privacy controls.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026