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BC 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 Claude Code author: ALAGENT-HKU v0.1.1 MIT-0 59 files · 32 scripts body ≈ 2 804 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

ALAGENT X2Strategy: any research input (PDF paper, Markdown draft, DOCX report, text notes, or keyword search) → structured strategy specification →…

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

ProcedureWordSoftware developmentData and analyticsAI 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
B
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. 1 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
    This quant-research skill is coherent, but it should be reviewed because it stores API keys in project files, probes an unrelated secret path in tests, and runs generated trading code directly without a sandbox.
    LLM: suspicious (high) · VirusTotal: · 16 Sept 2026