Unipile Linkedin SDK
LeoYeAI/openclaw-master-skills
LinkedIn integration via Unipile's official Node.js SDK. An agent skill from LeoYeAI/openclaw-master-skills.
Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X.
Install Kol Content Monitor
$ npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills kol-content-monitor --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/monitoring/composites/kol-content-monitor .claude/skills/kol-content-monitor && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "kol-content-monitor" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/monitoring/composites/kol-content-monitor into .claude/skills/kol-content-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kol-content-monitor", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/gooseworks-ai/goose-skills/tree/main/skills/monitoring/composites/kol-content-monitorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills kol-content-monitor --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/monitoring/composites/kol-content-monitor .agents/skills/kol-content-monitor && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "kol-content-monitor" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/monitoring/composites/kol-content-monitor into .agents/skills/kol-content-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kol-content-monitor", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills kol-content-monitor --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/monitoring/composites/kol-content-monitor .cursor/skills/kol-content-monitor && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "kol-content-monitor" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/monitoring/composites/kol-content-monitor into .cursor/skills/kol-content-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kol-content-monitor", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/gooseworks-ai/goose-skills.git --path skills/monitoring/composites/kol-content-monitor--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills kol-content-monitor --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/monitoring/composites/kol-content-monitor .gemini/skills/kol-content-monitor && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "kol-content-monitor" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/monitoring/composites/kol-content-monitor into .gemini/skills/kol-content-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kol-content-monitor", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install gooseworks-ai/goose-skills kol-content-monitorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/monitoring/composites/kol-content-monitor .github/skills/kol-content-monitor && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "kol-content-monitor" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/monitoring/composites/kol-content-monitor into .github/skills/kol-content-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kol-content-monitor", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills kol-content-monitor --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/monitoring/composites/kol-content-monitor .opencode/skills/kol-content-monitor && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "kol-content-monitor" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/monitoring/composites/kol-content-monitor into .opencode/skills/kol-content-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kol-content-monitor", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
kol-content-monitor- GitHub stars
- 1.2k
- Used in
- 1 other repo
- Token cost
- ~1.7k tokens
- SKILL.md length
- 500 words
- Files
- 2
- Skills in repo
- 273
- Repo updated
- First seen
- Licence
- MIT
At a glance
Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X.
- Works in 6 steps: Intake → Scrape LinkedIn Posts → Scrape Twitter/X Posts → …
- A marketing team wants to ride trends rather than create them from scratch
- SKILL.md covers When to Use, Phase 0: Intake, Phase 1: Scrape LinkedIn Posts and Phase 2: Scrape Twitter/X Posts, plus 7 more sections
- Calls python3; reaches linkedin.com; needs APIFY_API_TOKEN
What it does
Kol Content Monitor is an agent skill from gooseworks-ai/goose-skills. Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X. Surfaces trending narratives, high-engagement topics, and early signals of emerging conversations before they peak. Chains linkedin-profile-post-scraper and twitter-mention-tracker. Use when a marketing team wants to ride trends rather than create them from scratch, or when a founder wants to know which topics are resonating with their audience.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).
It sits in Writing & Content, covering Web scraping and Resume and CV writing. It works with X (Twitter) and LinkedIn. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.
When your agent uses it
- A marketing team wants to ride trends rather than create them from scratch
- A founder wants to know which topics are resonating with their audience
Example prompts
- “/kol-content-monitor”
Requirements
- Python 3
- A credential in APIFY_API_TOKEN
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
linkedin.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
APIFY_API_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Kol Content Monitor loads about 1.7k tokens when it runs. Until then it costs about 115 tokens; SKILL.md has 500 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 500 words, ~1,687 tokens.
Download SKILL.mdSave it as
.claude/skills/kol-content-monitor/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- kol-content-monitor
- description
- Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X. Surfaces trending narratives, high-engagement topics, and early signals of emerging conversations before they peak. Chains linkedin-profile-post-scraper and twitter-mention-tracker. Use when a marketing team wants to ride trends rather than create them from scratch, or when a founder wants to know which topics are resonating with their audience.
- tags
- monitoring
KOL Content Monitor
Track what Key Opinion Leaders in your space are writing about. Surface trending narratives early — before they peak — so your team can join the conversation at the right time with relevant content.
Core principle: For seed-stage teams, the fastest path to content distribution is riding a wave that's already breaking, not creating one from scratch.
When to Use
- "What are the top voices in [our space] posting about?"
- "What topics are trending on LinkedIn in [industry]?"
- "I want to know what content is resonating before I write anything"
- "Track [list of founders/experts] and tell me what they're saying"
- "Find trending narratives I can contribute to"
Phase 0: Intake
KOL List
- Names and LinkedIn URLs of KOLs to track (if known)
- If unknown: use
kol-discoveryskill first to build the list
- If unknown: use
- Twitter/X handles for the same KOLs (optional but recommended for full picture)
- Any specific topics/keywords you care about? (for filtering noisy feeds)
Scope
- How far back? (default: 7 days for weekly monitor, 30 days for first run)
- Minimum engagement threshold to include a post? (default: 20 reactions/likes)
Save config to the current working directory as kol-monitor.json (or user-specified path).
{
"kols": [
{
"name": "Lenny Rachitsky",
"linkedin": "https://www.linkedin.com/in/lennyrachitsky/",
"twitter": "@lennysan"
},
{
"name": "Kyle Poyar",
"linkedin": "https://www.linkedin.com/in/kylepoyar/",
"twitter": "@kylepoyar"
}
],
"days_back": 7,
"min_reactions": 20,
"keywords": ["GTM", "growth", "AI", "outbound", "founder"],
"output_path": "kol-monitor-[DATE].md"
}Phase 1: Scrape LinkedIn Posts
Run linkedin-profile-post-scraper for all KOL LinkedIn profiles:
python3 skills/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \
--profiles "<url1>,<url2>,<url3>" \
--days <days_back> \
--max-posts 20 \
--output jsonFilter results: only include posts with reactions ≥ min_reactions.
Phase 2: Scrape Twitter/X Posts
Run twitter-mention-tracker for each handle:
python3 skills/twitter-mention-tracker/scripts/search_twitter.py \
--query "from:<handle>" \
--since <YYYY-MM-DD> \
--until <YYYY-MM-DD> \
--max-tweets 20 \
--output jsonFilter: only include tweets with likes ≥ min_reactions / 2 (Twitter engagement is lower than LinkedIn).
Phase 3: Topic Clustering
Group all posts across all KOLs by topic/theme:
Clustering approach:
- Extract the main topic from each post (1-3 word label)
- Group similar topics together
- Count: how many KOLs touched this topic? How many total posts?
- Rank by: total engagement (sum of reactions/likes across all posts on that topic)
This surfaces topics with broad consensus (multiple KOLs talking about it) vs. individual takes.
Show full SKILL.md (189 more words)Show less
Signal types to flag:
| Signal | Meaning | Example |
|---|---|---|
| Convergence | 3+ KOLs on same topic in same week | Multiple founders posting about "AI SDR fatigue" |
| Spike | Topic that 2x'd in volume vs last week | Suddenly everyone's talking about [new thing] |
| Underdog | 1 KOL posting about topic nobody else covers | Potential early-mover opportunity |
| Controversy | Posts with high comment/reaction ratio | Debate you could weigh in on |
Phase 4: Output Format
# KOL Content Monitor — Week of [DATE]
## Tracked KOLs
[N] KOLs | [N] LinkedIn posts | [N] tweets | Period: [date range]
---
## Trending Topics This Week
### 1. [Topic Name] — CONVERGENCE SIGNAL
- **KOLs discussing:** [Name 1], [Name 2], [Name 3]
- **Total posts:** [N] | **Total engagement:** [N] reactions/likes
- **Trend direction:** ↑ New this week / ↑↑ Growing / → Stable
**Best posts on this topic:**
> "[Post excerpt — first 150 chars]"
— [Author], [Date] | [N] reactions
[LinkedIn URL]
> "[Tweet text]"
— [@handle], [Date] | [N] likes
[Twitter URL]
**Content opportunity:** [1-2 sentences on how to contribute to this conversation]
---
### 2. [Topic Name]
...
---
## High-Engagement Posts (Top 5 This Week)
| Post | Author | Platform | Engagement | Topic |
|------|--------|----------|------------|-------|
| "[Preview...]" | [Name] | LinkedIn | [N] reactions | [topic] |
...
---
## Emerging Topics to Watch
Topics picked up by 1 KOL this week — too early to call a trend but worth tracking:
- [Topic] — [KOL name] — [brief description]
- [Topic] — ...
---
## Recommended Content Actions
### This Week (Ride the Wave)
1. **[Topic]** is peaking — ideal moment to publish your take. Suggested angle: [angle]
2. **[Controversy]** is generating debate — consider a nuanced response post. Your positioning: [suggestion]
### Next Week (Get Ahead)
1. **[Emerging topic]** is early-stage — write something now before it gets crowded.Save to the current working directory as kol-monitor-[YYYY-MM-DD].md (or user-specified path).
Phase 5: Build Trigger-Based Content Calendar
Optional: from the monitor output, propose a content calendar entry for each "Ride the Wave" opportunity:
Topic: [topic]
Best post format: [LinkedIn insight post / tweet thread / blog]
Suggested hook: [hook]
Supporting points: [3 bullets from your product/experience]
Ideal publish date: [within 3 days of peak]Scheduling
Run weekly (Friday afternoon — catches the week's peaks and gives weekend to draft):
0 14 * * 5 python3 run_skill.py kol-content-monitor --client <client-name>Cost
| Component | Cost |
|---|---|
| LinkedIn post scraping (per profile) | ~$0.05-0.20 (Apify) |
| Twitter scraping (per run) | ~$0.01-0.05 |
| Total per weekly run (10 KOLs) | ~$0.50-2.00 |
Tools Required
- Apify API token —
APIFY_API_TOKENenv var - Upstream skills:
linkedin-profile-post-scraper,twitter-mention-tracker - Optional upstream:
kol-discovery(to build initial KOL list)
Trigger Phrases
- "What are the top voices in [space] posting about this week?"
- "Track my KOL list and give me content ideas"
- "Run KOL content monitor for [client]"
- "What's trending on LinkedIn in [industry]?"
© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in skills/monitoring/composites/kol-content-monitor of gooseworks-ai/goose-skills.
- SKILL.md
- skill.meta.json
Open the folder on GitHubat commit c650c6d
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Kol Content Monitor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Kol Content Monitor this skillgooseworks-ai/goose-skills | 1.2k | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Unipile Linkedin SDKLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.1k | Automated safety check: Notes | MIT | |
| Agent ReachPanniantong/Agent-Reach | 95k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Xquik Social ResearchXquik-dev/x-twitter-scraper | 211 | 1 repos | ~961 | Automated safety check: Pass | MIT | |
| X Tweet Searchbrowser-act/skills | 6.1k | — | ~2.7k | Automated safety check: Pass | MIT | |
| Deepapidavidondrej/skills | 4.1k | — | ~2.5k | Automated safety check: Pass | MIT |
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Related
Works with
Questions about Kol Content Monitor
What does Kol Content Monitor do?
Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X. Kol Content Monitor is an agent skill from gooseworks-ai/goose-skills. Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X.
When should I use Kol Content Monitor?
Kol Content Monitor fits situations like: A marketing team wants to ride trends rather than create them from scratch; A founder wants to know which topics are resonating with their audience.
How do I install Kol Content Monitor in Claude Code?
Run `npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor -a claude-code`. Or copy the skill folder (skills/monitoring/composites/kol-content-monitor in gooseworks-ai/goose-skills) into .claude/skills/kol-content-monitor in your project. Claude Code loads it when a task matches its description.
How do I install Kol Content Monitor in Codex?
Run `npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor -a codex`. Or copy the skill folder (skills/monitoring/composites/kol-content-monitor in gooseworks-ai/goose-skills) into .agents/skills/kol-content-monitor in your project. Codex loads it when a task matches its description.
Can I use Kol Content Monitor in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kol-content-monitor, .gemini/skills/kol-content-monitor, .github/skills/kol-content-monitor and .opencode/skills/kol-content-monitor in your project.
What does Kol Content Monitor need to run?
Going by SKILL.md and its folder, Kol Content Monitor needs the command-line tools its instructions call (python3) and credentials named APIFY_API_TOKEN. Our summary lists: Python 3; A credential in APIFY_API_TOKEN.
Does Kol Content Monitor access the network?
SKILL.md names 1 domain. In commands or code: linkedin.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Kol Content Monitor safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Kol Content Monitor use?
Kol Content Monitor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Kol Content Monitor use?
About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Kol Content Monitor?
Skills that share tags, products or a category with Kol Content Monitor: Unipile Linkedin SDK (LeoYeAI/openclaw-master-skills, 2.2k stars), Agent Reach (Panniantong/Agent-Reach, 95k stars), Xquik Social Research (Xquik-dev/x-twitter-scraper, 211 stars) and X Tweet Search (browser-act/skills, 6.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Kol Content Monitor?
gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.
Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.