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AD jb-fund-access-limits

Query and display Juicebox V5 fund access limits (payout limits, surplus allowances). Use when: (1) surplusAllowancesOf or payoutLimitsOf returns empty but values exist, (2) need to detect "unlimited" values which can be various max integers not just uint256, (3) fund access limits show as zero for USDC-based projects when querying with ETH token, (4) REVDeployer stageOf or configurationOf reverts, (5) CONFIGURING project deployment and need unlimited payouts - CRITICAL: empty fundAccessLimitGroups means ZERO payouts not unlimited, must use uint224.max for unlimited. Covers JBFundAccessLimits querying, multi-token support (ETH/USDC), ruleset chain walking, unlimited value detection, and proper configuration for project deployment.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 2 032 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Query and display Juicebox V5 fund access limits (payout limits, surplus allowances).

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
95
Quality 40%
84
Run on models
none yet
Process rating
D
41/100
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

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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token SKILL.md:64
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      token: '0x00…EEe',    // Native token
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:77
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      token: '0xA0…B48',    // USDC on Ethereum
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:113
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      const NATIVE_TOKEN = '0x00…EEe'
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:117
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      1: '0xA0…B48',    // Ethereum
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:118
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      10: '0x0b…f85',   // Optimism
      quoted

    Files scanned: 1. 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 41/100

    • 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. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 85Steps. 37 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2032 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 740: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 37 items
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.