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 remindergit 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/reminder)<a href="https://agentmods.dev/skills/fuyuxiang/echo-agent/reminder"><img src="https://agentmods.dev/badge/skills/fuyuxiang/echo-agent/reminder.svg" alt="Measured on agentmods" 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 | $0.01094 |
| Opus 5 | $0.00012 | $0.00547 |
| Sonnet 5 | $0.00005 | $0.00219 |
| Haiku 4.5 | $0.00002 | $0.00109 |
Grade A, and why
reminder 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 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.
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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reminder
Set timed reminders and manage todos.
IMPORTANT: how a reminder actually fires
A row in reminders.db is just a note — nothing scans it or delivers it. To make
a reminder that actually fires and gets delivered, you MUST create a scheduled
job with the cronjob tool, which persists to the scheduler and is delivered
through the active channel by Echo Agent's scheduler service:
cronjob(action="create", name="weekly-report",
schedule="0 9 * * 1", command="提醒你写周报")
The reminder_store.py script below is only a lightweight local note-taking
list (list/done/delete). It has NO delivery path on its own. If the user wants
to be reminded at a time, use cronjob — optionally also recording a note via
the script. For a background one-off that must take effect, spawn_task can run
the cronjob call for you.
Jobs that produce a file (audio/image/document)
When the scheduled command generates an artifact the user must receive (e.g. a
voice briefing), make the delivery happen inside the same tool call — do not
assume a later step will send it. For audio use text_to_speech(..., deliver=true)
(see the tts-voice skill); for other files call send_file explicitly with the
target channel/chat. An unattended cron run may end right after producing the
file, so "generate then hope it gets sent" silently drops the artifact.
After creating a job: confirm, then stop
Once cronjob(action="create") returns, you are done. Send the user ONE
confirmation with the concrete facts and end the turn:
- job id, the schedule, and the next fire time (from the create result)
- if a delivery target was inferred, say so; if the create result carried the "⚠️ 无法确定投递目标" warning, relay it and ask for target_channel/target_chat_id.
Do NOT try to "prove delivery works" by exercising the agent's own plumbing —
reading gateway/server.py, a2a/server.py, or curling internal endpoints like
/v1/chat or /api/v1/message is not verification, it's a rabbit hole that
burns the turn and leaves the user with no reply. If you genuinely need to sanity
-check the schedule, use cronjob(action="list"); trigger a real run at most
once and only if the user asked. The scheduled job itself is the delivery test —
it will fire on schedule.
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 First seen · 116 lines · 23 tokens per session scan A 6c30ec95c566
reminder is a skill published in the GitHub repository fuyuxiang/echo-agent (1,054 stars, last pushed 5d ago), licensed MIT. It adds 23 tokens to every session and 1,094 once invoked, about $0.0001 per session on Opus 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.
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