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AC learn-moralis

Learn about Moralis and Web3 development. Invoked without a question, gives a friendly platform walkthrough — what's available, what data you can fetch, and how everything fits together. Invoked with a question, answers it directly. Use for "what is Moralis", "can Moralis do X", "what chains are supported", "how do I get started", "which API should I use", pricing, feature comparisons, or any exploratory questions. Routes to the correct technical skill (@moralis-data-api or @moralis-streams-api) after answering.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 2 449 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Learn about Moralis and Web3 development.

As a process C 60/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationData and analyticstype 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
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
60/100
Has gaps
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

How to improve

    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 · 0

    ✓ No critical or high findings

    Files scanned: 4. 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 60/100

    • 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. 2 mutating operations with no state check
    • 70Failures and branches. 5 branches
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 54 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2449 tokens
    • 100Progress reporting. Reports progress
    • low 13 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

    • +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
    • -5TODO / placeholder text left in the skill
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 517: enough signal without eating the budget
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 54 items
    • +4Reference files are cited in the instructions (3 of 3)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.