# Prospect Posts > A Claude skill from Zevenue/gtm-skills. - **Type:** Skill - **Install:** `agentstack add skill-zevenue-gtm-skills-prospect-posts` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [Zevenue](https://agentstack.voostack.com/s/zevenue) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [Zevenue](https://github.com/Zevenue) - **Source:** https://github.com/Zevenue/gtm-skills/tree/main/.claude/skills/prospect-posts ## Install ```sh agentstack add skill-zevenue-gtm-skills-prospect-posts ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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: ```bash python3 utils/prospect_posts.py \ --profile-url "" \ --profile-url "" \ --count 20 \ --output-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: ```markdown # 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 `utils/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 `utils/prospect_posts.py` | ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [Zevenue](https://github.com/Zevenue) - **Source:** [Zevenue/gtm-skills](https://github.com/Zevenue/gtm-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** yes - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-zevenue-gtm-skills-prospect-posts - Seller: https://agentstack.voostack.com/s/zevenue - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.