SKILLEMALL.ai

AB trading-signal-analysis

Trading Signal Analysis: Ingest OHLCV datasets for any stock or cryptocurrency and generate. Use when an agent needs trading signal analysis, generate buy and sell trading signals from technical indicators, backtest trading strategies against historical ohlcv price data, analyze maximum adverse excursion and maximum favorable excursion for trade risk, calculate win rate and expectancy for systematic trading strategies, analyze signals, candles, symbol through AgentPMT-hosted remote tool calls.

ClawHub Agent Skills author: AgentPMT v1.0.0 MIT-0 3 files body ≈ 3 581 tokens Open the sourceclawhub.ai analyzed 2 d ago

Trading Signal Analysis: Ingest OHLCV datasets for any stock or cryptocurrency and generate.

As a process B 70/100 · Nearly there — weak spots: inputs and preconditions, running it twice

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
B
70/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Tools and files w 18
60
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 70/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 82 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3581 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 13 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 498: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 82 items
    • +3Output format is stated explicitly
    • +4Has examples (10 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: clean
    This skill is a disclosed AgentPMT remote trading-analysis integration, with no executable code or hidden local behavior, but users should be careful about sending proprietary market data.
    LLM: benign (high) · VirusTotal: · 24 Jun 2026