Aoineco Ledger — AI Agent Financial Tracking Engine

v1.0.0

Track detailed AI agent expenses, revenues, budgets, and ROI with per-agent attribution and alerts for efficient micro-budget financial management.

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for edmonddantesj/aoineco-ledger.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Aoineco Ledger — AI Agent Financial Tracking Engine" (edmonddantesj/aoineco-ledger) from ClawHub.
Skill page: https://clawhub.ai/edmonddantesj/aoineco-ledger
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Canonical install target

openclaw skills install edmonddantesj/aoineco-ledger

ClawHub CLI

Package manager switcher

npx clawhub@latest install aoineco-ledger
Security Scan
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Purpose & Capability
The SKILL.md and included Python implement a local ledger for per-agent cost tracking which matches the stated purpose. However the header and docs claim features such as “Encrypted storage via VaultCrypto integration” and “auto-conversion” (multi-currency) while the package declares zero external dependencies and no credentials/config for a vault or FX service — that is an internal inconsistency that should be explained.
Instruction Scope
Runtime instructions in SKILL.md are narrowly scoped: import the AoinecoLedger class and call logging/export methods. The implementation shown only reads/writes files in a ledger directory (default ~/.openclaw/workspace/ledger or OPENCLAW_WORKSPACE override). There are no SKILL.md instructions to read other unrelated files, exfiltrate data, or contact external endpoints.
Install Mechanism
No install spec is provided (instruction-only skill + included script). The code claims 'zero external dependencies' and is pure Python; that is low risk from a supply/install perspective. Still, the codebase includes features that normally require external services (vaults, FX rates) but no install/credential hints for those, which is inconsistent.
Credentials
The skill declares no required environment variables or credentials. The implementation optionally respects OPENCLAW_WORKSPACE for the storage location — a reasonable integration point. There are no requests for API keys, cloud credentials, or unrelated secrets in metadata or visible code.
Persistence & Privilege
The skill is not force-enabled (always=false) and will persist only to its own ledger directory. It creates and writes local files (transactions.jsonl, accounts.json, budgets.json, config.json) under the chosen workspace. It does not request elevation or attempt to modify other skills' configs in the visible code.
What to consider before installing
This package looks like a local bookkeeping library and asks for no secrets, but there are a few red flags to check before installing or using it with real data: - Ask the author or inspect the full ledger_engine.py for any network calls, hard-coded endpoints, or subprocess executions that are not visible in the truncated snippet. The file header promises VaultCrypto encryption and currency auto-conversion — verify how those are implemented and where keys or FX rates come from. - Confirm there is no hidden code that attempts to send the ledger contents to an external server or to read unrelated files on disk. Search the full file for 'requests', 'urllib', 'socket', 'subprocess', 'open', or any HTTP endpoints/URLs. - If you plan to record sensitive billing data, run the skill in an isolated environment (sandbox/container) and review the created ledger directory (~/.openclaw/workspace/ledger by default) to ensure only expected files are written. - If you need encrypted storage, require the implementation detail: how keys are derived/stored and whether the vault integration requires secrets. If the skill claims encryption but offers no key management, do not rely on it. If you can provide the remaining portion of scripts/ledger_engine.py (the truncated part) I can re-evaluate and raise the confidence level or surface specific lines of concern.

Like a lobster shell, security has layers — review code before you run it.

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Updated 1mo ago
v1.0.0
MIT-0

Aoineco Ledger — AI Agent Financial Tracking Engine

<!-- 🌌 Aoineco-Verified | S-DNA: AOI-2026-0213-SDNA-AL01 -->

Version: 1.0.0
Author: Aoineco & Co.
License: MIT
Tags: finance, expense-tracking, budget, roi, bootstrap, multi-agent, accounting

Description

Financial tracking engine built for AI agents operating on extreme budgets. Tracks every API call, gas fee, and revenue stream with per-agent cost attribution. Native support for the $7 Bootstrap Protocol.

Rebuilt from agentledger (Node.js) into Python with major enhancements for multi-agent squads and micro-budget operations.

Core metric: Intelligence per Dollar (IPD) — how many operations you get per cent spent.

Problem

AI agents spend money (API calls, gas fees, subscriptions) but rarely track it. When you're running a 9-agent squad on a $7 seed budget, every fraction of a cent matters. You need:

  • Real-time visibility into burn rate and runway
  • Per-agent cost attribution (who's spending what?)
  • Budget alerts BEFORE you overspend
  • Revenue tracking to measure actual ROI

Features

FeatureDescription
Transaction LoggingLog expenses, revenue, API costs, gas fees with one-liner methods
$7 Bootstrap MetricsSeed tracking, ROI%, runway days, daily burn rate, IPD
Per-Agent Cost AttributionTrack spending by squad member (oracle, blue-sound, etc.)
Budget AlertsSet limits per category/period — warns at 80%, blocks at 100%
Category SystemPre-configured for AI ops: API/LLM, Gas/Blockchain, Revenue/Gig, etc.
CSV/JSON ExportFull transaction export for tax, audit, or analysis
JSONL StorageAppend-only transaction log — fast, corruption-resistant
Period FilteringQuery by today, this-week, this-month, this-year, or all

Quick Start

from ledger_engine import AoinecoLedger

ledger = AoinecoLedger()

# Log API cost
ledger.log_api_cost(0.0042, "Google", "gemini-3-flash", tokens_used=150000, agent="oracle")

# Log gas fee
ledger.log_gas(0.0003, chain="base", tx_hash="0xabc123", agent="blue-sound")

# Log revenue
ledger.log_revenue(0.01, "MoltLaunch", "Tier-1 Intel Report", category="Revenue/Gig")

# Set budget
ledger.set_budget("API/LLM", limit=3.00, period="daily")

# Check $7 Bootstrap metrics
metrics = ledger.get_bootstrap_metrics()
# → Seed: $7.00, Burn: $0.02/day, Runway: 300 days, IPD: 257.5 ops/$

Bootstrap Metrics Output

💰 Seed Amount: $7.00
📈 Total Revenue: $0.0150
📉 Total Expenses: $0.0233
🤖 API/LLM Cost: $0.0230
💵 Net Profit: $-0.0083
📊 ROI: -0.1%
💎 Remaining Balance: $6.9917
🔥 Daily Burn Rate: $0.0233/day
⏱️ Runway: 300 days
🧠 IPD: 257.5 ops/$

Pre-Configured Categories

  • API/LLM — OpenAI, Anthropic, Google API costs
  • Gas/Blockchain — On-chain transaction fees
  • Revenue/Gig — MoltLaunch gig income
  • Revenue/DeFi — Meteora, LP yields
  • Revenue/Music — claw.fm royalties
  • Infrastructure, Marketing, Tools, Subscriptions, Other

File Structure

aoineco-ledger/
├── SKILL.md              # This file
└── scripts/
    └── ledger_engine.py  # Main engine (zero external dependencies)

Zero Dependencies

Pure Python 3.10+. No pip install needed. Designed for the $7 Bootstrap Protocol — every byte counts.

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