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 calculatorgit 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/calculator)<a href="https://agentmods.dev/skills/fuyuxiang/echo-agent/calculator"><img src="https://agentmods.dev/badge/skills/fuyuxiang/echo-agent/calculator/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/calculator"><img src="https://agentmods.dev/badge/skills/fuyuxiang/echo-agent/calculator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Supply Chain · line 63 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
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 | $0.00025 | $0.00657 |
| Opus 5.5 | $0.00010 | $0.00263 |
| Sonnet 5.5 | $0.00005 | $0.00131 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade C, and why
calculator scanned grade C with 2 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 7d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s "https://open.er-api.com/v6/latest/USD" | python3 -c " Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://open.er-api.com/v6/latest/USD" | python3 -c " What it actually says
Calculator
Math, units, dates, and currency conversion.
Math Expressions
Use the bundled calculator, which validates supported AST nodes before evaluating:
from subprocess import run
run(["python3", "scripts/calc.py", "eval", "2**10 + 3.14 * 2"], check=True)
Complex math:
import math
math.sqrt(144) # 12.0
math.log2(1024) # 10.0
math.factorial(10) # 3628800
math.pi # 3.14159...
Unit Conversions
| Category | Conversions |
|---|---|
| Temperature | C↔F: F = C*9/5+32 |
| Length | km↔mi (×0.621371), m↔ft (×3.28084), cm↔in (×0.393701) |
| Weight | kg↔lb (×2.20462), g↔oz (×0.035274) |
| Volume | L↔gal (×0.264172) |
| Data | KB↔MB↔GB↔TB (÷1024) |
Date Calculations
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
# Days between dates
(datetime(2026, 12, 31) - datetime.now()).days
# Add/subtract time
datetime.now() + timedelta(days=30, hours=2)
# Timezone conversion
dt = datetime.now(ZoneInfo("Asia/Shanghai"))
dt.astimezone(ZoneInfo("America/New_York"))
Currency Exchange
Free API (no key required):
curl -s "https://open.er-api.com/v6/latest/USD" | python3 -c "
import sys,json; d=json.load(sys.stdin)['rates']
print(f\"1 USD = {d['CNY']:.4f} CNY\")
print(f\"1 USD = {d['EUR']:.4f} EUR\")
print(f\"1 USD = {d['JPY']:.2f} JPY\")
"
Common Formulas
# BMI
bmi = weight_kg / (height_m ** 2)
# Compound interest
A = P * (1 + r/n) ** (n*t)
# Percentage change
change = (new - old) / old * 100
Script
python3 scripts/calc.py eval "2**32 - 1"
python3 scripts/calc.py convert 100 km mi
python3 scripts/calc.py date "2026-12-31" days-until
python3 scripts/calc.py currency 100 USD CNY
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.
- 7d ago Changed 9424a1b99153
- 1mo ago First seen · 92 lines · 25 tokens per session scan C a5e5a044c657
calculator is a skill published in the GitHub repository fuyuxiang/echo-agent (1,034 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 657 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). 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.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
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.