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FC nautilus-trader

NautilusTrader algorithmic trading platform for strategy development and live trading. Use when building trading strategies, backtesting, or deploying to Hyperliquid.

Not recommendedcritical or high security findings · low grade F
ClawHub Agent Skills author: Dan Repaci v0.1.0 16 files body ≈ 2 872 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 63/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

GeneratorInfrastructuretype 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
F
35/100
safety, quality, tests
Safety 60%
0
Quality 40%
88
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Obfuscation
If you install

The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.

For the author

Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 7

  • high Obfuscation uni-zero-width references/concepts.md:13
    Zero-width / invisible characters (possible hidden text) (160 occurrences)
    - Strategy implementation␀
  • high Obfuscation uni-zero-width references/data.md:13
    Zero-width / invisible characters (possible hidden text) (102 occurrences)
    - Overview␀
  • high Obfuscation uni-zero-width references/getting_started.md:13
    Zero-width / invisible characters (possible hidden text) (52 occurrences)
    - From PyPI␀
  • high Obfuscation uni-zero-width references/other.md:13
    Zero-width / invisible characters (possible hidden text) (107 occurrences)
    - Sections␀
  • high Obfuscation uni-zero-width references/strategies.md:13
    Zero-width / invisible characters (possible hidden text) (42 occurrences)
    - class Controller␀
Medium and low: 2
  • medium Obfuscation uni-zero-width references/backtesting.md:13
    Zero-width / invisible characters (possible hidden text) (9 occurrences) (test fixture / example file)
    - class BaseDataCatalog␀
    fixture
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Write Edit Bash Glob Grep

Files scanned: 16. 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 63/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2872 tokens
  • 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +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
  • +3Description length 166: enough signal without eating the budget
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (13 code blocks)
  • +4Reference files are cited in the instructions (6 of 11)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

External checks

ClawHub: suspicious
This is a real-money trading skill whose main purpose is disclosed, but it includes runnable mainnet examples that can place trades or change leverage without strong warnings or confirmation gates.
LLM: suspicious (high) · VirusTotal: benign · 28 May 2026