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 fitness-nutritiongit 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/fitness-nutrition)<a href="https://agentmods.dev/skills/fuyuxiang/echo-agent/fitness-nutrition"><img src="https://agentmods.dev/badge/skills/fuyuxiang/echo-agent/fitness-nutrition/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/fitness-nutrition"><img src="https://agentmods.dev/badge/skills/fuyuxiang/echo-agent/fitness-nutrition.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 18 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.
- medium Data Exfiltration · line 44 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00684 |
| Opus 5 | $0.00013 | $0.00342 |
| Sonnet 5 | $0.00005 | $0.00137 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade C, and why
fitness-nutrition 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 11d 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://wger.de/api/v2/muscle/?format=json" | 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://wger.de/api/v2/exercise/search/?term=squat&language=english&format=json" What it actually says
Fitness & Nutrition
Exercise database and food nutrition lookup via free public APIs.
Exercise Search (wger API — free, no key)
# Search exercises
curl -s "https://wger.de/api/v2/exercise/search/?term=squat&language=english&format=json"
# List muscle groups
curl -s "https://wger.de/api/v2/muscle/?format=json" | python3 -c "
import sys,json; [print(f\"{m['id']}: {m['name_en']}\") for m in json.load(sys.stdin)['results']]"
# Exercises for specific muscle (e.g. chest=4)
curl -s "https://wger.de/api/v2/exercise/?muscles=4&language=2&format=json"
Muscle Groups
| ID | English | 中文 |
|---|---|---|
| 1 | Biceps | 肱二头肌 |
| 2 | Shoulders | 肩部 |
| 4 | Chest | 胸部 |
| 9 | Legs | 腿部 |
| 10 | Abs/Core | 核心/腹肌 |
| 12 | Back | 背部 |
| 5 | Triceps | 肱三头肌 |
Nutrition Lookup (USDA FoodData Central)
# Search food (free, DEMO_KEY works)
curl -s "https://api.nal.usda.gov/fdc/v1/foods/search?query=chicken+breast&pageSize=3&api_key=DEMO_KEY" | \
python3 -c "import sys,json; [print(f\"{f['description']}: {[n for n in f.get('foodNutrients',[]) if 'Energy' in n.get('nutrientName','')]}\") for f in json.load(sys.stdin)['foods']]"
Calculators
# BMI
bmi = weight_kg / (height_m ** 2)
# Categories: <18.5 underweight, 18.5-24.9 normal, 25-29.9 overweight, 30+ obese
# TDEE (Harris-Benedict)
bmr_male = 88.362 + (13.397 * weight_kg) + (4.799 * height_cm) - (5.677 * age)
bmr_female = 447.593 + (9.247 * weight_kg) + (3.098 * height_cm) - (4.330 * age)
# Activity multiplier: sedentary 1.2, light 1.375, moderate 1.55, active 1.725
Script
python3 scripts/health_query.py exercise "push up"
python3 scripts/health_query.py exercise-detail 123
python3 scripts/health_query.py food "鸡胸肉"
python3 scripts/health_query.py bmi 75 1.78
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.
- 11d ago First seen · 69 lines · 25 tokens per session scan C 9fac4ea4ebd4
fitness-nutrition is a skill published in the GitHub repository fuyuxiang/echo-agent (1,056 stars, last pushed 9d ago), licensed MIT. It adds 25 tokens to every session and 684 once invoked, about $0.0001 per session on Opus 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
openlore
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openlore-housekeeping
Audit and maintain a shared OpenLore knowledge base. Use on a schedule or on request to find stale docs, broken links, unreviewed inbox items, and missing skill coverage, then publish an audit report.
lore
Long-term Markdown project memory for AI coding agents. Use when the user wants to record, recall, audit, sync, or compress project decisions, architecture, conventions, monorepo scopes, or .lore/ entries, including natural-language requests like "remember this decision" or explicit lore…
acontext-installer
Install Acontext, Login & Init Acontext Project, Add Skill Memory to Agent.
mindmemos-cli
Give an AI agent persistent, cross-session long-term memory through MindMemOS. Covers installing and authenticating the mindmemos CLI, the full command interface (add / search / get / update / delete / feedback / dreaming) with parameters and examples, guidance on which capability to use when, plus a Python SDK…
learning-capture
Log corrections, errors, and discoveries to orbital/LESSONS.md so future sessions avoid repeating mistakes.