Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add roedyrustam/vibes-plug --skill agentic-m2m-settlementgit clone --depth 1 https://github.com/roedyrustam/vibes-plugWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/roedyrustam/vibes-plug/agentic-m2m-settlement)<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/agentic-m2m-settlement"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/agentic-m2m-settlement/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/agentic-m2m-settlement"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/agentic-m2m-settlement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00089 | $0.03954 |
| Opus 5.5 | $0.00036 | $0.01582 |
| Sonnet 5.5 | $0.00018 | $0.00791 |
| Haiku 4.5 | $0.00009 | $0.00395 |
Grade A, and why
agentic-m2m-settlement scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic M2M Settlement & Clearing Protocol (2026 Edition)
English
Orchestration & Integration
Connects and orchestrates with agentic-micro-economy-architect, doku-payment-gateway, doku-mcp-server, payment-gateway-expert, saas-billing, multi-agent-orchestration, formal-spec-z3-verifier, database-orm-expert, and zero-trust-secret-vault.
Description
In multi-agent swarms where sub-agents consume paid third-party APIs, compute clusters, specialized data scraping, or delegated reasoning, agents require an autonomous, real-time clearing and settlement rail. Agentic M2M Settlement establishes production-grade financial rails for autonomous Agent-to-Agent (A2A) and Agent-to-Infrastructure (A2I) transactions. It supports Dual-Rail Clearing:
- Fiat Rail: Dynamic Bank Indonesia SNAP BI Standard QRIS/VA settlement and DOKU Checkout with HMAC-SHA512 webhook idempotency.
- Stablecoin / Account Abstraction Rail: Instant, sub-cent micro-streaming (USDC on Base/Arbitrum) using ERC-4337 smart accounts and scoped session keys.
It enforces strict velocity caps, non-bypassable spending ceilings, cryptographic invoice signing, and milestone-gated critic escrow.
Trigger Conditions
Activate this skill when:
- Designing systems where autonomous AI agents purchase API services, vector compute, or data from other agents.
- Implementing financial transaction rails between autonomous swarms and payment gateways (DOKU SNAP BI, Stripe Treasury).
- Enforcing strict FinOps budget envelopes with atomic kill-switches on agent wallets.
- Building decentralized or hybrid agent marketplaces with cryptographic escrow release.
- Implementing formal verification (Z3/SMT) on multi-agent billing state transitions to guarantee zero double-spend.
Core Architecture & Settlement Protocols (2026 Standard)
[ Swarm Director Treasury ]
│
┌─────────────────┴─────────────────┐
▼ ▼
[ Fiat Settlement Rail ] [ Stablecoin Micro Rail ]
(DOKU SNAP BI QRIS / VA) (USDC via Base ERC-4337)
│ │
└─────────────────┬─────────────────┘
│
[ Escrow & Verification Vault ]
(Critic Agent + SMT Invariants)
│
▼
[ Target Worker Sub-Agent ]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +117 lines 2b90e8bc5c27
- 3d ago First seen · 223 lines · 89 tokens per session scan A fec92b721f3a
agentic-m2m-settlement is a skill published in the GitHub repository roedyrustam/vibes-plug (95 stars, last pushed today), licensed MIT. It adds 89 tokens to every session and 3,954 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-17.
Other skills, from other repositories
iflytek-ocr-invoice
An image-reading tool that extracts structured information from Chinese invoices, receipts, bills, and tickets. OCR means turning text in a photo or scan into computer-readable data.
ai-pricing
When the user wants to price an AI product, choose a charge metric, design pricing tiers, or optimize margins. Also use when the user mentions 'AI pricing,' 'usage-based pricing,' 'consumption pricing,' 'outcome pricing,' 'BYOK,' 'bring your own key,' 'per-seat pricing,' 'pricing tiers,' 'AI margins,' 'cost per…
alpha-vantage
Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.
founder-operating-system
Founders face unique challenges in how they work. Maker vs manager schedule conflicts. Default alive vs dead math. Time and money slowly leaking away. Effective founders develop systems for navigating these challenges. This skill synthesizes PG's essays on founder productivity and decision-making into an operating…
execution-algorithms
World-class trade execution - minimize market impact, optimize order routing, reduce slippage. The difference between backtest and live is usually execution. Use when "execution, slippage, market impact, order routing, TWAP, VWAP, iceberg, fill rate, best execution, " mentioned.
burn-rate-management
Don't die. Runway calculation, default alive vs. default dead, when to raise vs. cut, zero-based budgeting for startups. The math that keeps companies alive long enough to win. Burn rate is not just accounting - it's strategic leverage. Companies with runway have options. Companies without runway have desperation. Use…