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gooseworks-ai / capabilities-linkedin-post-research

LinkedIn Post Research

Search LinkedIn posts by one or more keywords using the Apify `apimaestro/linkedin-posts-search-scraper-no-cookies` actor. The runbook returns author details, post text, engagement metrics, dates, hashtags, activity IDs, and direct LinkedIn URLs without requiring LinkedIn cookies

agent codexmodel gpt-5.5snapshot python312-uveval programmatic7 stepsv1.0.0

Deploy LinkedIn Post Research to your jetty.io

One-click installs this runbook into a collection on your Jetty account. You can run it from the Spot dashboard, schedule it, or pipe inputs in via the API.

Deploy on jetty.io →View source
The shape of the run

7 steps · start to finish.

  1. 1
    Step 1

    Environment Setup

    ▶
    1. Create the results directory and verify required inputs are present.
    2. Verify APIFY_API_TOKEN is set without printing its value.
    3. Install Python dependencies if needed.
    mkdir -p /app/results
python3 -m pip install requests
python3 - <<'PY'
import os, sys
if not os.environ.get("APIFY_API_TOKEN"):
    sys.exit("ERROR: APIFY_API_TOKEN is not set")
PY

    If setup fails, write validation_report.json with the setup stage marked passed=false, then stop.

  2. 2
    Step 2

    Run LinkedIn Post Searches

    ▶

    For each keyword, submit an Apify actor run with `keyword`, `maxItems`, and `sortBy`. Poll until the run reaches `SUCCEEDED`, `FAILED`, `ABORTED`, or the configured timeout. Retry a failed or timed-out actor run once with the same parameters before marking the keyword failed.

  3. 3
    Step 3

    Fetch and Normalize Results

    ▶

    Fetch the actor dataset items for each successful run. Normalize each item to this schema: `author`, `author_headline`, `author_profile_url`, `keyword`, `reactions`, `comments`, `shares`, `reactions_by_type`, `date`, `post_preview`, `full_text`, `url`, `activity_id`, `hashtags`,

  4. 4
    Step 4

    Sort and Export Outputs

    ▶

    Sort the final post list by total reactions descending when `sort_by=relevance`, or by post date descending when `sort_by=date_posted`. Write JSON and CSV outputs to `/app/results/linkedin_posts.json` and `/app/results/linkedin_posts.csv`.

  5. 5
    Step 5

    Summarize Findings

    ▶

    Write `/app/results/summary.md` with:

  6. 6
    Step 6

    Validate Outputs

    ▶

    Validate that every required output file exists and is non-empty. Parse `linkedin_posts.json` as JSON, verify it is an array, and check that each object has `author`, `keyword`, `url`, and `activity_id` fields when results are present. Parse `search_metadata.json` and verify it i

  7. 7
    Step 7

    Iterate on Errors (max 3 rounds)

    ▶

    If validation fails or the actor returns zero results for all keywords, run up to max 3 rounds of targeted fixes: