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CD genlayer-claw-skill

Understand and explain GenLayer - the AI-native blockchain for trustless decision-making. Use for investor pitches, protocol explanations, architecture questions, consensus mechanics, positioning, and ecosystem discussions. Triggers: explain genlayer, what is genlayer, genlayer thesis, optimistic democracy, genlayer pitch, genlayer architecture, condorcet jury theorem, equivalence principle, AI blockchain, trustless AI. (For writing contracts, use genlayer-dev-claw-skill instead.)

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 10 files body ≈ 967 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Understand and explain GenLayer - the AI-native blockchain for trustless decision-making.

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorMarketingInfrastructuretype 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
C
67/100
safety, quality, tests
Safety 60%
59
Quality 40%
80
Run on models
none yet
Process rating
D
39/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 3

  • high Exfiltration intent-browser-credential-store architecture.md:7
    Accesses a browser credential / cookie store
    This enables non-deterministic operations—processing text prompts, fetching live web data, executing AI-based decisions—while preserving blockchain reliability and security.
  • high Exfiltration intent-browser-credential-store SKILL.md:41
    Accesses a browser credential / cookie store
    | **Intelligent Contracts** | AI-powered smart contracts in Python that can reason, access web data, and handle non-deterministic operations |
Medium and low: 1
  • medium Exfiltration intent-browser-credential-store consensus.md:3
    Accesses a browser credential / cookie store (quoted — discussed, not commanded)
    Optimistic Democracy is GenLayer's consensus mechanism for validating transactions and Intelligent Contract operations. It's especially good at handling unpredictable outcomes from AI and web data whi
    quoted

Files scanned: 10. 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 39/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (genlayer-claw-skill) differs from the folder (genlayer)
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 100Steps. 16 steps
  • 100Execution cost. Instruction body is 967 tokens

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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 485: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 16 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.