SKILLEMALL.ai

AC blinko

Play Blinko (on-chain Plinko) headlessly on Abstract chain. Use when an agent wants to play Blinko games, check game stats, view leaderboards, or track honey rewards. Handles the full commit-reveal flow including API auth, on-chain game creation, simulation, and settlement.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 787 tokens Open the sourcegithub.com analyzed 2 d ago

Play Blinko (on-chain Plinko) headlessly on Abstract chain.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
97
Quality 40%
87
Run on models
none yet
Process rating
C
51/100
Has gaps
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

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token scripts/commit-reveal-abi.json:977
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "name": "ERC1…ion",
      quoted
    • low Secrets in code secret-high-entropy-token scripts/play-blinko.js:12
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      const CONTRACT = '0x18…763';
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:102
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Contract | `0x18…763` |
      table

    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 51/100

    • 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. 7 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 13 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 787 tokens
    • 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 274: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (7 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.