SKILLEMALL.ai

AC renzo

Query Renzo crypto liquid restaking protocol — DeFi vault yields, TVL, ezETH exchange rates, EigenLayer operators, supported blockchain networks, user token balances, and withdrawal status.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files · 1 script body ≈ 3 293 tokens Open the sourcegithub.com analyzed 2 d ago

Query Renzo crypto liquid restaking protocol — DeFi vault yields, TVL, ezETH exchange rates, EigenLayer operators, supported blockchain networks, user token…

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
96
Quality 40%
83
Run on models
none yet
Process rating
C
59/100
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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Secrets in code secret-high-entropy-token renzo-mcp.sh:33
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      renzo-mcp.sh get_token_balances '{"address":"0xd8…045"}'
      quoted
    • low Secrets in code secret-high-entropy-token renzo-mcp.sh:34
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      renzo-mcp.sh get_withdrawal_requests '{"address":"0xd8…045"}'
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:68
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      ./skills/renzo/renzo-mcp.sh get_token_balances '{"address":"0xd8…045"}'
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:69
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      ./skills/renzo/renzo-mcp.sh get_withdrawal_requests '{"address":"0xd8…045"}'
      quoted

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 59/100

    • 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. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 27 steps, 1 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3293 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 189: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 27 items
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.