LobsterAI is an open-source desktop AI agent that operates files, commands, browsers, documents, spreadsheets, slides, messaging channels, and scheduled jobs in a user's working environment. It supports office work, research, and custom multi-agent workflows, while catalogue add-ons extend the agent with additional skills and workflows.
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 netease-youdao/LobsterAI --skill content-plannergit clone --depth 1 https://github.com/netease-youdao/LobsterAIWrote 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/netease-youdao/lobsterai/content-planner)<a href="https://agentmods.dev/skills/netease-youdao/lobsterai/content-planner"><img src="https://agentmods.dev/badge/skills/netease-youdao/lobsterai/content-planner/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/netease-youdao/lobsterai/content-planner"><img src="https://agentmods.dev/badge/skills/netease-youdao/lobsterai/content-planner.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 | $0.00063 | $0.01209 |
| Opus 5 | $0.00032 | $0.00605 |
| Sonnet 5 | $0.00013 | $0.00242 |
| Haiku 4.5 | $0.00006 | $0.00121 |
Grade A, and why
content-planner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- content-planner — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Topic Planning + Content Calendar
Use Cases
- User says "帮我规划下周公众号内容"
- User says "最近有什么热门选题可以写"
- User says "帮我做一份内容日历"
- User wants to know what competitor accounts are writing about
- Need to make topic decisions based on data
Dependencies
Node.js + cheerio (install once):
npm install -g cheerio
Script Directory
| Script | Purpose | Usage |
|---|---|---|
scripts/wechat_search.js |
Sogou WeChat article search | node "$SKILLS_ROOT/content-planner/scripts/wechat_search.js" "keyword" |
Search Script Parameters
IMPORTANT: Always use the $SKILLS_ROOT environment variable to locate scripts.
# Basic search
node "$SKILLS_ROOT/content-planner/scripts/wechat_search.js" "keyword"
# Limit result count
node "$SKILLS_ROOT/content-planner/scripts/wechat_search.js" "keyword" -n 15
# Save to file
node "$SKILLS_ROOT/content-planner/scripts/wechat_search.js" "keyword" -n 20 -o result.json
# Parse real URLs (extra network requests, may be blocked by anti-scraping)
node "$SKILLS_ROOT/content-planner/scripts/wechat_search.js" "keyword" -n 5 -r
Output Fields: Article title, article URL, article summary, publish time, source account name
Workflow
Step 1: Clarify Planning Scope
Confirm the following information with the user (ask all at once):
帮你规划内容，先确认几件事：
1. 规划周期？（本周 / 下周 / 自定义时间范围）
2. 有没有特定想写的方向或关键词？
3. 每周几篇？（默认3篇）
Step 2: Trending Scan
Execute multiple rounds of searches covering different dimensions:
Search Strategy:
- Core domain keyword search — Search with 2-3 core keywords related to the account's field
- User-specified keyword search — If user has specific directions
- General trending search — Search with combinations of "热点", "热门", "最新" with domain keywords
# Example: Tech domain
node "$SKILLS_ROOT/content-planner/scripts/wechat_search.js" "AI 最新趋势" -n 10
node "$SKILLS_ROOT/content-planner/scripts/wechat_search.js" "大模型应用" -n 10
node "$SKILLS_ROOT/content-planner/scripts/wechat_search.js" "科技热点 2026" -n 10
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 · 159 lines · 63 tokens per session scan A f9584dd4d4d8
content-planner is a skill published in the GitHub repository netease-youdao/LobsterAI (6,007 stars, last pushed yesterday), licensed MIT. It adds 63 tokens to every session and 1,209 once invoked, about $0.0003 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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