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AC x2strategy

ALAGENT X2Strategy: any research input (PDF paper, Markdown draft, DOCX report, text notes, or keyword search) → structured strategy specification → executable Backtrader code → backtest → diagnosis report. Two core capabilities: (1) paper2spec extracts multi-strategy specs from any document via 5-layer LLM extraction, and (2) spec2code generates validated Backtrader code, runs backtests, and compares against paper metrics. Use this skill whenever the user wants to analyze a quant paper, extract trading strategies, generate strategy code, run a backtest, search for papers, or go end-to-end from any input to executable results. Also triggers on: "look at this paper", "what strategies does this use", "implement this strategy", "search for momentum papers", "turn this into code", or any request about quantitative finance research → implementation. Even if the user doesn't mention "strategy" explicitly — if they provide a finance paper or research document, use this skill.

ClawHub Agent Skills author: ALAGENT-HKU v0.1.1 MIT-0 59 files · 1 script body ≈ 2 804 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

AnalyzerWordSoftware developmentData and analyticstype 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
92/100
safety, quality, tests
Safety 60%
95
Quality 40%
87
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Exfiltration read-dotenv README_CN.md:147
      Reads a .env file
      cp .env.example .env          # 填入 API Key（推荐 DEEPSEEK_API_KEY）
    • low Exfiltration read-dotenv README_CN.md:322
      Reads a .env file
      cp .env.example .env  # 填入 API Key
    • low Exfiltration read-dotenv README.md:147
      Reads a .env file
      cp .env.example .env          # add your API key (DEEPSEEK_API_KEY recommended)
    • low Exfiltration read-dotenv README.md:323
      Reads a .env file
      cp .env.example .env  # add API key
    • low Dangerous commands cmd-background-process scripts/run_full_tests.sh:6
      Starts a background / autostarted process (code comment; test fixture / example file)
      #   nohup bash scripts/run_full_tests.sh &
      commentfixture

    Files scanned: 48. 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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 31 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2804 tokens
    • 100Running it twice. No mutating operations
    • 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)
    • +3Description length 983: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -32 of 7 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 31 items
    • +4Has examples (11 code blocks)
    • +4Reference files are cited in the instructions (6 of 7)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    The skill has a coherent finance-research purpose, but it needs review because it can store credentials and documents, send document text to external LLM providers, run generated trading/backtest code, and includes test tooling that reads credentials from an unrelated hard-coded path.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026