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AC jb-v5-api

Juicebox V5 protocol API reference. Function signatures, parameters, and return values for all contracts. Use for "what functions exist?" and "what are the signatures?" questions. For internal mechanics and tradeoffs, use /jb-v5-impl instead.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 6 368 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Juicebox V5 protocol API reference.

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

IntegrationInfrastructuretype 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%
99
Quality 40%
79
Run on models
none yet
Process rating
C
55/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token SKILL.md:819
    High-entropy token-like string (may be an id, hash or a credential)
    address constant NATIVE_TOKEN = 0x00…EEe;

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6368 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 55/100

  • 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. 28 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 6368 tokens
  • 100Steps. 85 steps
  • 100Consistency. Name and required fields are in place
  • low 15 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
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 242: enough signal without eating the budget
  • +4Structure: 68 headings
  • +3Step-by-step instructions: 85 items
  • +4Has examples (21 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.