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DB shll-run

Execute DeFi transactions on BSC via SHLL AgentNFA. The AI handles all commands and users only need to chat.

Not recommendedcritical or high security findings · low grade D
ClawHub Agent Skills author: kledx v6.0.4 3 files body ≈ 3 402 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Execute DeFi transactions on BSC via SHLL AgentNFA.

As a process B 73/100 · Nearly there — weak spots: running it twice

ProcedureAI 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
D
69/100
safety, quality, tests
Safety 60%
69
Quality 40%
69
Run on models
none yet
Process rating
B
73/100
Nearly there
Running it twice w 4
30
When it triggers w 12
50
Tools and files w 18
60
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: shll-run (ClawHub)

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.

Broad scope
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.

Risky intent 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 purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.

For the author

Explain in the description why the access is needed and how it is limited; add tests that show refusals on dangerous requests.

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.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 6

  • high Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: RUNNER_PRIVATE_KEY (high-value: RUNNER_PRIVATE_KEY) — verify each one is needed for the stated purpose
    required_environment_variables: RUNNER_PRIVATE_KEY
Medium and low: 5
  • medium Risky intent intent-wallet-secrets SKILL.md:15
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    Operator wallet private key (AI-only hot wallet).
  • medium Risky intent intent-wallet-secrets SKILL.md:136
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    4. Never ask for or handle the owner wallet private key.
  • low Secrets in code secret-high-entropy-token README.md:40
    High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
    "RUNNER_PRIVATE_KEY": "0x_Y…KEY"
    placeholder
  • low Secrets in code secret-high-entropy-token README.md:60
    High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
    "RUNNER_PRIVATE_KEY": "0x_Y…KEY"
    placeholder
  • low Secrets in code secret-high-entropy-token SKILL.md:113
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    PolicyGuard contract: `0x25…BB3`
    detector

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "website"
  • note frontmatter-key unknown frontmatter key "twitter"
  • note frontmatter-key unknown frontmatter key "repository"
  • note frontmatter-key unknown frontmatter key "install"
  • note frontmatter-key unknown frontmatter key "update"
  • note frontmatter-key unknown frontmatter key "env"
  • note frontmatter-key unknown frontmatter key "credentials"

Process rating: all ten parameters 73/100

  • 30Running it twice. 2 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 147 steps, 1 vague phrases
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3402 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 19 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (26 tags): a typed call is more reliable

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)
  • +3Description length 108: 120–800 characters recommended
  • +1No license
  • +2Single-language instructions
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 147 items
  • +3Output format is stated explicitly
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
This skill is openly designed for AI-assisted DeFi trading, but it gives an agent transaction-signing ability and tells it to activate the hot-wallet key automatically, so it needs careful review before use.
LLM: suspicious (high) · VirusTotal: · 29 May 2026