Echo Agent is a self-hosted, long-running AI agent that connects language models, tools, memory, permissions, and messaging channels in one system. Individuals and teams use it for private automation that retains context across sessions, develops skills, schedules tasks, and requires approval for high-risk actions.
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 fuyuxiang/echo-agent --skill finance-trackergit clone --depth 1 https://github.com/fuyuxiang/echo-agentWrote 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/fuyuxiang/echo-agent/finance-tracker)<a href="https://agentmods.dev/skills/fuyuxiang/echo-agent/finance-tracker"><img src="https://agentmods.dev/badge/skills/fuyuxiang/echo-agent/finance-tracker/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/fuyuxiang/echo-agent/finance-tracker"><img src="https://agentmods.dev/badge/skills/fuyuxiang/echo-agent/finance-tracker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.00393 |
| Opus 5.5 | $0.00009 | $0.00157 |
| Sonnet 5.5 | $0.00005 | $0.00079 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
finance-tracker 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 1mo 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.
What it actually says
Finance Tracker
Personal finance tracking with SQLite backend. Supports Chinese bank CSV imports.
Storage
SQLite database at ~/.echo-agent/finance.db.
Script
# Add expense
python3 scripts/finance_manager.py add 35.5 餐饮 --note "午餐"
# Import bank CSV
python3 scripts/finance_manager.py import ~/Downloads/alipay_record.csv
# Monthly summary
python3 scripts/finance_manager.py summary --month 2026-06
# Trend (month-over-month)
python3 scripts/finance_manager.py trend --months 6
# Budget check
python3 scripts/finance_manager.py budget 餐饮 3000
Categories (默认)
餐饮, 交通, 购物, 住房, 娱乐, 医疗, 教育, 通讯, 投资, 其他
CSV Import
Auto-detects format from common Chinese sources:
- 支付宝 (Alipay) —
alipay_record_*.csv - 微信支付 (WeChat Pay) —
微信支付账单*.csv - 招商银行 — 信用卡账单 CSV
- 工商银行 — 明细导出 CSV
Database Schema
CREATE TABLE expenses (
id INTEGER PRIMARY KEY,
amount REAL NOT NULL,
category TEXT NOT NULL,
note TEXT,
date TEXT DEFAULT (date('now')),
source TEXT DEFAULT 'manual',
created_at TEXT DEFAULT CURRENT_TIMESTAMP
);
Reports
Monthly summary shows total spending by category with percentage breakdown. Trend analysis compares month-over-month changes per category.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 67 lines · 23 tokens per session scan A aa264b8d29fe
finance-tracker is a skill published in the GitHub repository fuyuxiang/echo-agent (1,058 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 393 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.
context-compression
A guide to shortening the information an agent carries between steps. It explains how to replace large tool outputs and old conversation details with compact, useful summaries while preserving important decisions and constraints.
tushare-data
A Chinese-language workflow for researching financial and market data through Tushare, a data service covering Chinese stocks and related economic information. It turns natural-language questions into steps for retrieving, cleaning, comparing, filtering, exporting, and briefly analysing data.
openlore
Query and publish to an OpenLore knowledge repository over SSH using ordinary shell commands. Use when a task needs project documentation, runbooks, shared team knowledge, or a place to publish findings.