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BB lp-agent

Run automated liquidity provision strategies on concentrated liquidity (CLMM) DEXs using Hummingbot API.

ClawHub Agent Skills author: Michael Feng v1.0.1 19 files · 4 scripts body ≈ 7 497 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 68/100 · Nearly there — weak spots: result and completion

IntegrationInfrastructureAI 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
79/100
safety, quality, tests
Safety 60%
90
Quality 40%
63
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
When it triggers w 12
50
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 10

✓ No critical or high findings

Medium and low: 10
  • low Secrets in code secret-high-entropy-token scripts/get_meteora_pool.py:10
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    python scripts/get_meteora_pool.py BGm1…D5Y
    quoted
  • low Secrets in code secret-high-entropy-token scripts/get_meteora_pool.py:11
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    python scripts/get_meteora_pool.py BGm1…D5Y --json
    quoted
  • low Secrets in code secret-high-entropy-token scripts/get_meteora_pool.py:371
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    %(prog)s BGm1…D5Y
    quoted
  • low Secrets in code secret-high-entropy-token scripts/get_meteora_pool.py:372
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    %(prog)s BGm1…D5Y --json
    quoted
  • low Secrets in code secret-high-entropy-token scripts/visualize_lp_executor.py:14
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    python scripts/visualize_lp_executor.py --id ryUB…iob
    quoted
  • low Secrets in code secret-high-entropy-token scripts/visualize_lp_executor.py:15
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    python scripts/visualize_lp_executor.py --id 5w7G…N8Z --output /tmp/sol_usdc.html
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:391
    High-entropy token-like string (may be an id, hash or a credential)
    python scripts/get_meteora_pool.py ATrB…Sms
  • low Secrets in code secret-high-entropy-token SKILL.md:394
    High-entropy token-like string (may be an id, hash or a credential)
    python scripts/get_meteora_pool.py ATrB…Sms --json
  • low Secrets in code secret-high-entropy-token SKILL.md:397
    High-entropy token-like string (may be an id, hash or a credential)
    python scripts/get_meteora_pool.py ATrB…Sms --no-gateway
  • low Secrets in code secret-high-entropy-token SKILL.md:506
    High-entropy token-like string (may be an id, hash or a credential)
    --pool ATrB…Sms \

Files scanned: 19. 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")
  • warning body-long SKILL.md body ≈ 7497 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "commands"

Process rating: all ten parameters 68/100

  • 0Result and completion. Does not say what the result is
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 7497 tokens
  • 100Steps. 64 steps
  • 100Failures and branches. 8 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress

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 104: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +4Structure: 48 headings
  • +3Step-by-step instructions: 64 items
  • +4Has examples (26 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 14 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.

External checks

ClawHub: suspicious
This appears to be a real crypto liquidity-provision skill, but it needs Review because it can control live wallets and trading infrastructure while shipping weak credentials and unsafe install/reset behaviors.
LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026