SKILLEMALL.ai
GitHub ↗ RU Sign in

BD genlayer-dev-claw-skill

Build GenLayer Intelligent Contracts - Python smart contracts with LLM calls and web access. Use for writing/deploying contracts, SDK reference, CLI commands, equivalence principles, storage types. Triggers: write intelligent contract, genlayer contract, genvm, gl.Contract, deploy genlayer, genlayer CLI, genlayer SDK, DynArray, TreeMap, gl.nondet, gl.eq_principle, prompt_comparative, strict_eq, genlayer deploy, genlayer up. (For explaining GenLayer concepts, use genlayer-claw-skill instead.)

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 2 470 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Build GenLayer Intelligent Contracts - Python smart contracts with LLM calls and web access.

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorInfrastructureAI 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
83/100
safety, quality, tests
Safety 60%
79
Quality 40%
88
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
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.

Exfiltration
If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 · 4

  • high Exfiltration intent-browser-credential-store SKILL.md:15
    Accesses a browser credential / cookie store
    GenLayer enables **Intelligent Contracts** - Python smart contracts that can call LLMs, fetch web data, and handle non-deterministic operations while maintaining blockchain consensus.
Medium and low: 3
  • low Secrets in code secret-high-entropy-token references/sdk-api.md:227
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    addr = Address("0x03…b3F")
    quoted
  • low Secrets in code secret-high-entropy-token references/sdk-api.md:231
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    addr.as_hex     # "0x03…b3F"
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:129
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    addr = Address("0x03…b3F")
    quoted

Files scanned: 8. 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 43/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (genlayer-dev-claw-skill) differs from the folder (genlayer-dev)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 85Steps. 26 steps, 1 vague phrases
  • 100Execution cost. Instruction body is 2470 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 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 496: enough signal without eating the budget
  • +4Structure: 41 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (20 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

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