SKILLEMALL.ai

AC crypto-self-learning

Self-learning system for crypto trading. Logs trades with full context (indicators, market conditions), analyzes patterns of wins/losses, and auto-updates trading rules. Use to log trades, analyze performance, identify what works/fails, and continuously improve trading accuracy.

ClawHub Agent Skills author: totaleasy v1.0.0 7 files body ≈ 1 006 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 7. 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 58/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1006 tokens
    • 100Progress reporting. Reports progress
    • 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

    • +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
    • -214 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 279: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (10 code blocks)
    • +3All 4 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: clean
    This skill is a local crypto trade journal and rules generator; it stores trading data and can update an agent memory file, but those behaviors are disclosed and aligned with its purpose.
    LLM: benign (high) · VirusTotal: benign · 10 Sept 2026