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 plangit 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/plan)<a href="https://agentmods.dev/skills/fuyuxiang/echo-agent/plan"><img src="https://agentmods.dev/badge/skills/fuyuxiang/echo-agent/plan/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/plan"><img src="https://agentmods.dev/badge/skills/fuyuxiang/echo-agent/plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00033 | $0.00394 |
| Opus 5.5 | $0.00013 | $0.00158 |
| Sonnet 5.5 | $0.00007 | $0.00079 |
| Haiku 4.5 | $0.00003 | $0.00039 |
Grade A, and why
plan 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.
This is a copy
84% identical to plan — 16 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Plan Mode
Use this skill when the user wants a plan instead of execution.
Core behavior
For this turn, you are planning only.
- Do not implement code.
- Do not edit project files except the plan markdown file.
- Do not run mutating terminal commands, commit, push, or perform external actions.
- You may inspect the repo or other context with read-only commands/tools when needed.
- Your deliverable is a markdown plan saved inside the active workspace under
.echo-agent/plans/.
Output requirements
Write a markdown plan that is concrete and actionable.
Include, when relevant:
- Goal
- Current context / assumptions
- Proposed approach
- Step-by-step plan
- Files likely to change
- Tests / validation
- Risks, tradeoffs, and open questions
If the task is code-related, include exact file paths, likely test targets, and verification steps.
Save location
Save the plan with write_file under:
.echo-agent/plans/YYYY-MM-DD_HHMMSS-<slug>.md
Use this path relative to the active working directory.
If the runtime provides a specific target path, use that exact path.
If not, create a sensible timestamped filename yourself under .echo-agent/plans/.
Interaction style
- If the request is clear enough, write the plan directly.
- If no explicit instruction accompanies
/plan, infer the task from the current conversation context. - If it is genuinely underspecified, ask a brief clarifying question instead of guessing.
- After saving the plan, reply briefly with what you planned and the saved path.
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 · 55 lines · 33 tokens per session scan A c9e8d55844ec
plan is a skill published in the GitHub repository fuyuxiang/echo-agent (1,058 stars, last pushed yesterday), licensed MIT. It adds 33 tokens to every session and 394 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to plan, differing in 16 lines, and is treated as a copy.
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.
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.
openlore-housekeeping
Audit and maintain a shared OpenLore knowledge repository. 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.