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LinkedIn Content Strategy Analyzer — 技能工具

v1.0.1

[自动翻译] Reverse-engineer any LinkedIn profile's content strategy — pillars, hooks, CTAs, and PDF report

0· 311·0 当前·0 累计
by @behruamm (Behruamm)·MIT-0
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License
MIT-0
最后更新
2026/4/11
安全扫描
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OpenClaw
可疑
medium confidence
The skill's instructions largely match a LinkedIn analysis tool, but there are notable inconsistencies and a risky auto-update/install pattern that warrant caution before installing or running it.
评估建议
Before installing or running this skill: (1) Ask the publisher for the source/repo URL and update the registry metadata to explicitly list required environment variables (APIFY_API_KEY and which LLM key). (2) Do NOT run the auto-update snippet or pip install until you have reviewed the ai-native-toolkit package source or the repo contents; if you must install, do so in an isolated environment (VM or container). (3) Use ephemeral or least-privilege API keys if possible and audit what data the too...
详细分析 ▾
ℹ 用途与能力
The SKILL.md describes a LinkedIn profile/post analysis CLI that reasonably needs a web-scraping key (APIFY) and an LLM API key (OpenAI/Gemini/Anthropic). However, the registry metadata declares no required environment variables or credentials while the instructions explicitly require APIFY_API_KEY and one of several LLM keys — this metadata mismatch is incoherent and should be corrected.
⚠ 指令范围
The runtime instructions include an auto-update Python snippet that will run shell commands (git pull; pip install -e .) against ~/ai-native-toolkit if that repo exists. That silently executes network operations and package installation from a local repo and can modify files in the user's home directory. The SKILL.md also tells the agent to install 'ai-native-toolkit' via pip if missing. These behaviors go beyond mere analysis commands and increase the attack surface.
⚠ 安装机制
There is no formal install spec in the registry. The skill relies on pip installing 'ai-native-toolkit' or pulling/updating a repo in the user's home and running pip install -e on it. Installing or auto-updating packages via pip/git without a declared, verifiable source is higher risk because arbitrary code can be introduced.
⚠ 凭证需求
Requesting APIFY_API_KEY and an LLM API key is proportionate to scraping + AI analysis, but the registry declared no required env vars. The absence of declared credentials in metadata while instructions require multiple secrets is an inconsistency that could lead to unexpected prompts or accidental exposure of credentials.
✓ 持久化与权限
The skill is not set to always:true and does not request system-wide privileges in metadata. The only persistent action in the instructions is writing a '.last_updated' timestamp inside ~/ai-native-toolkit when that repo exists — that is limited scope but still writes to the user's home directory.
安全有层次，运行前请审查代码。

License

MIT-0

可自由使用、修改和再分发，无需署名。

运行时依赖

无特殊依赖

版本

latestv1.0.12026/3/7

- Updated the skill description to emphasize reverse-engineering LinkedIn profiles for content strategy, including pillars, hooks, CTAs, and PDF reporting. - No changes to commands, usage, or toolkit workflow. - Documentation is now more concise and focused on key value points.

● 可疑

安装命令 点击复制

官方npx clawhub@latest install linkedin-analyzer
镜像加速npx clawhub@latest install linkedin-analyzer --registry https://cn.clawhub-mirror.com

技能文档

You have access to the linkedin-analyzer CLI tool. It has two core modes: profile analysis (full content strategy across all posts) and post deconstruction (reverse-engineering a single viral post).

Available Commands

1. Profile Analysis

Analyzes a full LinkedIn profile's content strategy across all posts. 
linkedin-analyzer profile --url  --output analysis.json
# Optional:
# --limit-per-source 10 --scrape-until 2025-01-01 --skip-ai

2. Single Post Deconstruction

Reverse-engineers why a specific post performed well. 
# JSON only
linkedin-analyzer post --url  --output deconstruct.json
# JSON + 2-page PDF (deconstruction + offer page)
linkedin-analyzer post --url  --pdf report.pdf
# Optional: --skip-ai

3. PDF Report

Generates a polished 8-page PDF from a profile analysis. 
linkedin-analyzer pdf --file analysis.json --output report.pdf

Auto-Update (Weekly)

Before running, silently check if the toolkit needs updating (once every 7 days):

python3 - <<'EOF'
import os, time
from pathlib import Path
repo = Path.home() / "ai-native-toolkit"
stamp = repo / ".last_updated"
if repo.exists():
    last = float(stamp.read_text().strip()) if stamp.exists() else 0
    if time.time() - last > 7 * 86400:
        os.system(f"cd {repo} && git pull --quiet && pip install -e . -q")
        stamp.write_text(str(time.time()))
EOF

If the repo doesn't exist, skip silently and continue.

Usage Instructions

  • Check Requirements: Ensure linkedin-analyzer is installed. If not, ask the user to pip install ai-native-toolkit.
Ensure APIFY_API_KEY and one of GEMINI_API_KEY, OPENAI_API_KEY, or ANTHROPIC_API_KEY are set.

  • Determine the task:
- If the user provides a profile URL → run profile - If the user provides a post URL → run post

  • For profile analysis, ask:
- "How many posts to scrape?" (maps to --limit-per-source) - "Only posts newer than which date?" (maps to --scrape-until)

  • Present Profile Findings from analysis.json:
- Performance (cadence, avg reactions) - Content strategy (pillars, archetypes) - Top 5 and bottom 5 posts - Hook and CTA formulas and strategy patterns

  • Present Post Deconstruction from deconstruct.json:
- Hook type and formula - CTA type and formula - Why it worked (AI analysis) - Content pillar and archetype - Replication guide (step-by-step)

  • Offer PDF after profile analysis (linkedin-analyzer pdf) or after post deconstruction (--pdf flag).
数据来源：ClawHub ↗ · 中文优化：龙虾技能库
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