Linkedin Post Research

What it does

When your agent uses it

• Researching what people are saying about a topic on LinkedIn
• Finding high-engagement content
• Identifying thought leaders
• Discovering warm leads through post engagement

Example prompts

• “/linkedin-post-research”

Requirements

• Python 3
• A credential in APIFY_API_TOKEN

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Takes one or more keywords
2. 2Calls the apimaestro/linkedin-posts-search-scraper-no-cookies Apify actor
3. 3Returns posts with author, text, engagement metrics, date, and URL
4. 4Deduplicates across keywords by activity_id
5. 5Sorts by engagement (total reactions) descending, or by date

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

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python3
  • curl

  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:

  • api.apify.com
  • 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

Linkedin Post Research loads about 1.3k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 360 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~97

When it runs · the whole SKILL.md, loaded when a task matches

~1.3k

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

• NoteMentions a .env fileSKILL.md:30

  ed for searching LinkedIn posts. Set in `.env`:

• NoteMentions a .env fileSKILL.md:156

  _TOKEN` not set | Ask user to add it to `.env` |

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); the scripts in this folder are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 360 words, ~1,316 tokens.

name

linkedin-post-research

description

Search LinkedIn posts by keywords, sorted by engagement or date. Use when researching what people are saying about a topic on LinkedIn, finding high-engagement content, identifying thought leaders, or discovering warm leads through post engagement. Returns author, post text, reactions, comments, shares, post URL, and date. No LinkedIn cookies or login required.

tags

research, lead-generation

APIFY_API_TOKEN=your_token_here

# Single keyword search
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
  --keyword "AI sourcing" --max-items 20

# Multiple keywords
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
  --keyword "AI sourcing" --keyword "recruiting automation" --max-items 30

# Sort by date (most recent first)
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
  --keyword "AI agents" --sort-by date_posted --max-items 20

# Output as CSV
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
  --keyword "AI agents" --output csv --output-file results.csv

# Summary table
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
  --keyword "AI agents" --output summary

curl -X POST "https://api.apify.com/v2/acts/apimaestro~linkedin-posts-search-scraper-no-cookies/runs?token=$APIFY_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "keyword": "AI agents",
    "maxItems": 50,
    "sortBy": "date_posted"
  }'

# Check run status
curl "https://api.apify.com/v2/acts/apimaestro~linkedin-posts-search-scraper-no-cookies/runs/{RUN_ID}?token=$APIFY_API_TOKEN"

# When status is SUCCEEDED, fetch results
curl "https://api.apify.com/v2/datasets/{DATASET_ID}/items?token=$APIFY_API_TOKEN"

{
  "author": "Jane Smith",
  "author_headline": "VP of Sales at Acme Corp",
  "author_profile_url": "https://www.linkedin.com/in/janesmith",
  "keyword": "AI sourcing",
  "reactions": 142,
  "comments": 28,
  "shares": 12,
  "reactions_by_type": {"LIKE": 100, "EMPATHY": 30, "PRAISE": 12},
  "date": "2026-04-01",
  "post_preview": "First 200 chars of the post text...",
  "full_text": "Complete post text...",
  "url": "https://www.linkedin.com/posts/...",
  "activity_id": "7447040447966826496",
  "hashtags": ["#AI", "#sales"],
  "is_repost": false,
  "content_type": "text"
}

© 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 2 other files (scripts) in skills/social/capabilities/linkedin-post-research of gooseworks-ai/goose-skills.

• SKILL.md
• scripts/search_posts.py
• 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 9, 2026.

• criptogus/agent-evolve-network.claude/skills/linkedin-post-research