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Autonomous M2M agent settlement, SNAP BI, and stablecoin clearing / Settlement agen M2M otonom, SNAP BI, dan kliring stablecoin.

roedyrustam/vibes-plug Agent Skills author: roedyrustam MIT 1 file body ≈ 4 316 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Autonomous M2M agent settlement, SNAP BI, and stablecoin clearing / Settlement agen M2M otonom, SNAP BI, dan kliring stablecoin.

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

ProcedurePostgreSQLAI and agentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
98
Quality 40%
72
Run on models
none yet
Process rating
C
55/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Risky intent intent-offensive-security SKILL.md:18
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    In enterprise multi-agent swarms, specialized subagents frequently hire peer agents for niche subtasks (e.g., automated red teaming, formal verification, dataset crawling). Without an autonomous, cryp
  • low Risky intent intent-offensive-security SKILL.md:353
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    Pada swarm multi-agen skala perusahaan, sub-agen terspesialisasi kerap menugaskan agen lain untuk menangani tugas khusus (misal: red teaming otomatis, verifikasi formal, atau crawling dataset). Tanpa

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

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 55/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Result and completion. Does not say what the result is
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4316 tokens
  • 100Steps. 29 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations

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 128: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.