一键导入
auto
Morning Check / Session Entry Point. Delta pipeline: Stage 1 COLLECT → Stage 2 DETECT (+ Feed parallel). On-the-fly engagement analysis, no stored contacts.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Morning Check / Session Entry Point. Delta pipeline: Stage 1 COLLECT → Stage 2 DETECT (+ Feed parallel). On-the-fly engagement analysis, no stored contacts.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | auto |
| description | Morning Check / Session Entry Point. Delta pipeline: Stage 1 COLLECT → Stage 2 DETECT (+ Feed parallel). On-the-fly engagement analysis, no stored contacts. |
| user-invocable | true |
| allowed-tools | ["Bash","Read","Write","Edit","Agent","Glob","Grep"] |
The central entry point for every automated session. Runs the 2-stage pipeline and gives the user a prioritized summary.
Replaces the previous /daily and /auto. Called by cron job (daily ~08:00) or manually.
IMPORTANT: Delegate the work to the specialized agents. Do NOTHING yourself — you are only the orchestrator.
Use the Agent tool to start the following agents sequentially (Stage 1→2) or in parallel (2a+2b):
data-collector agentsignal-detector agent (with Stage 1 output)feed-analyst agent (parallel to 2a)/auto # Full Morning Check (2-stage pipeline)
/auto quick # Stage 1 only (collect data, no feed)
Stage 1: COLLECT Stage 2: DETECT
data-collector ────────────► signal-detector
(Data Analyst) parallel:
feed-analyst
(Social Media Scout)
What happens:
Output → Stage 2: Engagement data (commenters, reactors, profile viewers, new connections — with name, headline, post URN).
2a: signal-detector agent
2b: feed-analyst agent (parallel)
Morning Check complete (since last session: 18h)
DATA:
[n] notifications processed
[n] post metrics updated
[n] lifecycle transitions (Active→Cooling: 1, Archived: 0)
[n] new posts auto-discovered
SIGNALS ([n] new):
HIGH: comment_reply — Sarah K commented on "AI in Practice"
HIGH: outreach_candidate — Anna Schmidt (CTO @ TechAG, ICP: High)
MEDIUM: keyword_mention — "AI Agents" in post by @tech-leader
FEED:
Trending: "AI Agents" (7x, 2.3x avg), "Remote Work" (4x)
Comment Opportunities:
1. [HIGH] @sarah-k: "The future of..." (89 Rx in 3h)
2. [MEDIUM] @tech-leader: "Why we switched..." (45 Rx in 5h)
NEEDS YOUR DECISION:
- 2 comment replies pending → reply on LinkedIn
- Last post 5 days ago → /draft for new post?
- New patterns detected → /evolve for strategy update?
- 2 comment opportunities → Write draft?
ALL GOOD:
Content pipeline: 5 Ideas, 2 Drafts
Competitors: current (8 days ago)
Strategy: v1.2
After the pipeline — hints only, no actions:
session.last_session_dateComplete reference for the linkedin-cli tool. Load this skill when you need to call the CLI via Bash to fetch LinkedIn data, manage connections, or send messages.
Quick status check without API calls. Shows current state from data files + session info.
Complete data schema for LinkedIn tracking. Markdown file-per-record with YAML frontmatter. Naming conventions, schemas for all 11 record types, Glob/Read/Write/Edit access patterns.
Generate personalized outreach message for a LinkedIn contact.
Deep onboarding for LinkedIn Commander. Interview + historical post analysis + competitor deep dives + system generation. The system starts "warmed up", not empty.
Analyze post performance. Compares with baseline, detects patterns, evaluates experiments, analyzes comments, updates ICP. Lifecycle-aware.