Prospect Posts

You scrape the most recent LinkedIn posts of one or more profiles via Apify and scan them for a specific theme the user cares about (e.g. "AI-first GTM", "hiring pain", "pivoting to enterprise"). Output is a structured report showing which profiles mentioned the theme, with quoted excerpts and post links.

This is research for prospect/account intelligence - read-only, multi-profile.

How to invoke

The user says something like:

• "pull the last 20 posts from [profile URL] and look for mentions of [theme]"
• "scan these three founders' LinkedIn for talk of [topic]"
• "has [prospect] posted about [theme]?"

Required inputs:

1. Profile URL(s) - one or more LinkedIn profile URLs
2. Theme - what to look for. Can be a topic, belief, pain point, or signal

Optional:

• Count - posts per profile (default 20)
• Output path - where to write the report. Default derived from theme + date (see Step 4)

If either profile URL or theme is missing, ask the user before running.

Prerequisites

• APIFY_API_TOKEN in .env
• requests and python-dotenv installed

Process

Step 1: Prepare

1. Confirm APIFY_API_TOKEN is set. If missing, tell the user to add it.
2. Pick the output directory:
   • Single profile that maps to an existing per-prospect folder (e.g. prospects/{slug}/): save there
   • Otherwise: prospects/_scans/ (default)
3. Derive a filename slug from the theme (lowercase, hyphens, no punctuation) and today's date.
   • JSON path: {output_dir}/{date}-{theme-slug}.json
   • Report path: {output_dir}/{date}-{theme-slug}.md
4. Create prospects/_scans/ if it doesn't exist.

Step 2: Fetch posts

Run the scraper. Repeat --profile-url for each profile:

python3 scripts/prospect_posts.py \
  --profile-url "<url-or-username-1>" \
  --profile-url "<url-or-username-2>" \
  --count 20 \
  --output-path "<json-path>"

The script:

• Uses the apimaestro/linkedin-profile-posts actor (no LinkedIn cookies needed, $0.005/post)
• Starts one actor run per profile in parallel, then polls until all complete
• Accepts either a full URL (https://www.linkedin.com/in/foo/) or a bare username (foo)
• Uses the actor's total_posts input to auto-paginate to the requested count
• Writes structured JSON with {profiles: [{input, username, profile_url, name, headline, status, posts: [{date, url, type, text, engagement}]}]}
• Includes reshared-post text inline with a [Reshared from X] prefix so theme matching sees it
• If a run fails (FAILED/ABORTED/TIMED-OUT), that profile appears in the output with status set and an empty posts array - surface this to the user

Step 3: Scan for the theme

Read the JSON output. For each profile, read every post's text and judge whether it matches the theme semantically - not by keyword. A post about "our GTM team is replacing playbooks with Claude agents" matches "AI-first GTM" even without the exact phrase. Conversely, a post that mentions "AI" in passing while talking about something unrelated should not match.

For each match, capture:

• Post date
• A 1-3 sentence quote showing the match (use the author's own words, don't paraphrase)
• The post URL
• A one-line interpretation of why it matches the theme

If a post is borderline, include it in a separate "Adjacent signals" section with a note on why it's adjacent rather than a direct match.

Step 4: Write the report

Write a markdown report at the report path with this structure:

# Post scan: {theme}

**Scanned:** {date}
**Theme:** {theme exactly as user phrased it}
**Profiles:** {count}
**Posts reviewed:** {total across all profiles}

## {Profile name or URL}

**Profile:** {linkedin url}
**Headline:** {headline if available}
**Posts reviewed:** {n}
**Direct matches:** {m}

### Direct matches

#### {date} - [link]({post_url})
> {quoted excerpt}

**Why it matches:** {one-line interpretation}

{repeat per match}

### Adjacent signals
{only include if any; same format with a "Why it's adjacent" line}

### No-match summary
{if zero matches, one sentence summarizing what they DO post about so the user can judge whether the theme is truly absent or just framed differently}

---

{repeat per profile}

## Cross-profile patterns
{2-4 bullets if multiple profiles: who is loudest on the theme, what angles recur, who's silent. Skip this section for single-profile scans.}

Step 5: Report back to the user

Tell the user:

• Path to the markdown report (relative to repo root)
• One-line summary per profile: {name}: {n} direct matches, {m} adjacent or {name}: no mentions of {theme}
• If there's a standout finding (a strong recent match, or a surprising silence), call it out in one sentence

Do not paste the full report into chat. The user will open the file.

Output locations

• Report and JSON default to prospects/_scans/ - gitignore this path in your project (scan output may contain commercial signals you don't want committed)
• If the profile maps to an existing prospects/{slug}/ folder, save there instead

What this skill does NOT do

• Does not post to LinkedIn.
• Does not download images (themes are textual; skip the image fetch overhead).
• Does not scrape company pages - profiles only. For company-page scraping, a different actor is needed.
• Does not draft outreach based on findings. That's downstream (use email-writer or signal-builder).

Troubleshooting

Issue	Fix
APIFY_API_TOKEN not found	Add to .env
Actor run times out	Increase timeout_secs in scripts/prospect_posts.py or reduce --count
Profile returned 0 posts with status SUCCEEDED	Profile may be private, have no public posts, or the username was wrong. Verify the URL in a browser
A run shows FAILED / TIMED-OUT	Re-run just that profile. Apify actor can be flaky on specific profiles; a retry usually works
Zero matches but you expect some	Widen the theme interpretation, or check the no-match summary - the prospect may frame the topic differently than the user's phrasing
Schema changed / missing text	Inspect raw output with --raw-output /tmp/raw.json and update field names in extract_text() / extract_author() in scripts/prospect_posts.py