SKILLEMALL.ai
GitHub ↗ RU Sign in

AC k3-blockchain-agent

Build automated blockchain analysis workflows on K3 — from natural language requests to deployed, running automations that fetch on-chain data, analyze it with AI, and deliver insights via email, Telegram, or Slack. Use this skill whenever the user mentions blockchain workflows, on-chain analytics, DeFi monitoring, token tracking, wallet alerts, pool analysis, protocol dashboards, NFT tracking, automated trading, smart contract monitoring, or wants to automate anything involving blockchain data. Also trigger when the user mentions K3, workflow builder, or wants scheduled crypto/DeFi reports. Even if they just say "monitor this wallet" or "track this token" — this skill applies.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 2 619 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Build automated blockchain analysis workflows on K3 — from natural language requests to deployed, running automations that fetch on-chain data, analyze it…

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureTelegramSlackInfrastructureData and analyticsAI 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
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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: 5. 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 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 16 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 32 steps, 2 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2619 tokens
    • 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

    • +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
    • +5Description quotes 2 example trigger phrases
    • +3Description length 686: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.