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AC trading-agents

Orchestrate a swarm of specialized Claude subagents that simulate a professional trading firm to analyze stocks and produce trading decisions. Based on the TradingAgents framework (arXiv 2412.20138), this skill deploys analyst agents (fundamental, technical, sentiment, news), bull/bear research debaters, a trader, a risk manager, and a portfolio manager — all collaborating to produce a comprehensive trading recommendation. Use this skill whenever the user asks about stock analysis, trading decisions, market research for specific tickers, investment recommendations, portfolio decisions, or wants a multi-perspective analysis of any publicly traded security. Also trigger when the user mentions "trading agents", "multi-agent trading", "stock swarm", or wants an AI-driven trading desk analysis.

ClawHub Agent Skills author: huahang v0.0.2 MIT-0 15 files body ≈ 2 563 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, consistency, progress reporting

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
92
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token scripts/fetch_market_data.py:41
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "price_to_sales": info.get("pric…ths"),
      quoted

    Files scanned: 15. 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 61/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (trading-agents) differs from the folder (trading-agents-skill)
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 59 steps
    • 100Execution cost. Instruction body is 2563 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 800: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 59 items
    • +4Has examples (4 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    The skill appears purpose-built for stock research, but it should be reviewed because it broadly triggers a costly multi-agent workflow, writes multiple files, and runs local commands with user-derived ticker text without clear validation or overwrite safeguards.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026