SKILLEMALL.ai

AC trading-agents

Multi-agent stock trading signal analysis framework with two-round debate mechanism. Triggered when users provide a stock ticker for investment analysis. Input a stock ticker, analyze through 7 SubAgents in 4 layers (Information Gathering → Opinion Formation → Two-Round Debate → Final Decision), output BUY/SELL/HOLD recommendation with rationale. Trigger phrases: "analyze this stock", "give me investment advice", "is this stock worth buying", "analyze XX stock", "stock investment analysis".

ClawHub Agent Skills author: 赖根 v2.4.2 MIT-0 12 files body ≈ 3 979 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

AnalyzerAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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: 12. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Multi-agent stock trading signal analysis framework with two-round… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
    • note frontmatter-key unknown frontmatter key "requires"

    Process rating: all ten parameters 59/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 40Consistency. Frontmatter name (trading-agents) differs from the folder (stock-trading-agents-light)
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web, python) 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. 112 steps
    • 100Execution cost. Instruction body is 3979 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 495: enough signal without eating the budget
    • +4Structure: 43 headings
    • +3Step-by-step instructions: 112 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (7 of 7)
    • +3All 2 scripts are documented
    • +1License stated

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

    External checks

    ClawHub: clean
    This is a disclosed stock-analysis skill that fetches market data, runs multi-agent analysis, and saves local reports, with no evidence of hidden trading, exfiltration, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026