--- name: linkedin-engager-analytics description: Pull the people who liked or commented on any LinkedIn post and segment them by ICP fit (peer / aspirational / prospect / other). Produces an engager roster, tier breakdown, and outbound action lists (follow back, comment-drop, DM-able with one-line openers). Powered by Apify, no LinkedIn login. Triggers on "who liked my post", "who engaged", "engagers report", "audience analytics". Not for tracking author replies to your comments (use linkedin-thread-monitor). ---

# LinkedIn Engager Analytics

Pull every liker and commenter on a LinkedIn post and bucket them by ICP fit. Outputs a roster + action list you can feed into your DM or outreach queue.

Depends on `APIFY_TOKEN`. Without it, falls back to user-paste of the engager list.

## When to use

- After publishing a post: "Who actually engaged? Are they ICP?" - Before a campaign: "Pull the last 5 viral posts in my niche, group their commenters by company size" - Reviewing competitor engagement: which prospects show up across multiple authors

## Input

- One or more LinkedIn post URLs - Optional: ICP definition (target titles, company size, industry) - Optional: max engagers per post (default 100)

## Output

Output format (engager roster, tier breakdown, action lists): see `references/output-spec.md`. Headline: a table of engagers labelled by ICP tier and a per-tier action list.

## Steps

1. **Fetch engagers.** Call `lib.ApifyClient.fetch_post_engagers(post_url=<url>, max_items=100)`. Returns a list of dicts with `type` ("commenters" | "likers"), `name`, `subtitle` (job title + company), `url_profile`, `content` (comment text if commenter), `datetime`. Cost is roughly $0.005 per engager-record. 2. **Parse subtitle into structured fields.** The `subtitle` typically reads "Director at Acme Corp" or "Founder & CEO at SaaS Inc". Extract: title, company, seniority bucket (IC / Manager / Director / VP / C-suite / Founder). 3. **Score ICP fit.** Use the user's supplied ICP rules: - Title match (regex or keyword list) - Company size proxy (look up via the user's CRM if integrated, else mark Unknown) - Industry match (parse company name + subtitle keywords) 4. **Assign tier.** - Peer: founder / operator at similar-stage company in same niche - Aspirational: senior leader (Director+) at larger company in adjacent niche - Prospect: title in ICP target list AND company in ICP target list - Other: no match 5. **Produce action lists.** - Follow back: peers with active posting (heuristic: appears as author in `fetch_user_recent_comments` of any team member) - Comment-drop targets: aspirational tier - DM-able: prospect tier, with a one-line DM opener referencing the specific post they engaged with ("Saw you reacted to <post angle>. Curious. Are you currently <ICP problem>?") 6. **Optional cross-post analysis.** If the user supplied multiple post URLs, deduplicate engagers and flag people who engaged with 2+ posts (highest-intent signal).

## Inbound-quality signals

High-quality = follow up: founder/operator title, company in ICP, active posting history, >10 mutual 2nd-degree connections, prior thoughtful comments on user's posts.

Low-quality = skip: generic praise, template language ("I'd love to hop on a quick call"), sales/agency profile with no operator history, same comment copy-pasted across many creators.

## Hard rules

Global voice rules: see root `SKILL.md` §Voice rules. Additional skill-specific rules:

- Don't run engager analytics on posts you didn't write or aren't tracking with permission. The data is technically public but high-volume scraping of someone else's audience reads as creepy. - Don't DM a prospect on the same day they engaged with your post. Wait 24-72h to avoid the "thirsty" pattern. - One DM opener per engager, not three. If the first didn't land in 5 business days, drop it.

## Cost accounting

| Action | Apify call | Cost (free tier) | |---|---|---| | Engager analytics on one post (50 engagers) | `fetch_post_engagers(max_items=50)` | $0.25 | | Engager analytics on one post (200 engagers) | `fetch_post_engagers(max_items=200)` | $1.00 |

A weekly engager-analytics run on 1-2 posts stays well under the $5 free monthly credit.

## Files

- `SKILL.md` — this file - `references/output-spec.md` — engager roster shape, tier breakdown, action lists, sample run

## Related skills

- `linkedin-thread-monitor` — track author replies to YOUR comments (different surface) - `linkedin-comment-drafter` — draft outreach comments to engagers from this report - `linkedin-reply-handler` — draft DM follow-ups

--- name: linkedin-engager-analytics description: Pull the people who liked or commented on any LinkedIn post and segment them by ICP fit (peer / aspirational / prospect / other). Produces an engager roster, tier breakdown, and outbound action lists (follow back, comment-drop, DM-able with one-line openers). Powered by Apify, no LinkedIn login. Triggers on "who liked my post", "who engaged", "engagers report", "audience analytics". Not for tracking author replies to your comments (use linkedin-thread-monitor). ---

# LinkedIn Engager Analytics

Pull every liker and commenter on a LinkedIn post and bucket them by ICP fit. Outputs a roster + action list you can feed into your DM or outreach queue.

Depends on `APIFY_TOKEN`. Without it, falls back to user-paste of the engager list.

## When to use

- After publishing a post: "Who actually engaged? Are they ICP?" - Before a campaign: "Pull the last 5 viral posts in my niche, group their commenters by company size" - Reviewing competitor engagement: which prospects show up across multiple authors

## Input

- One or more LinkedIn post URLs - Optional: ICP definition (target titles, company size, industry) - Optional: max engagers per post (default 100)

## Output

Output format (engager roster, tier breakdown, action lists): see `references/output-spec.md`. Headline: a table of engagers labelled by ICP tier and a per-tier action list.

## Steps

1. **Fetch engagers.** Call `lib.ApifyClient.fetch_post_engagers(post_url=<url>, max_items=100)`. Returns a list of dicts with `type` ("commenters" | "likers"), `name`, `subtitle` (job title + company), `url_profile`, `content` (comment text if commenter), `datetime`. Cost is roughly $0.005 per engager-record. 2. **Parse subtitle into structured fields.** The `subtitle` typically reads "Director at Acme Corp" or "Founder & CEO at SaaS Inc". Extract: title, company, seniority bucket (IC / Manager / Director / VP / C-suite / Founder). 3. **Score ICP fit.** Use the user's supplied ICP rules: - Title match (regex or keyword list) - Company size proxy (look up via the user's CRM if integrated, else mark Unknown) - Industry match (parse company name + subtitle keywords) 4. **Assign tier.** - Peer: founder / operator at similar-stage company in same niche - Aspirational: senior leader (Director+) at larger company in adjacent niche - Prospect: title in ICP target list AND company in ICP target list - Other: no match 5. **Produce action lists.** - Follow back: peers with active posting (heuristic: appears as author in `fetch_user_recent_comments` of any team member) - Comment-drop targets: aspirational tier - DM-able: prospect tier, with a one-line DM opener referencing the specific post they engaged with ("Saw you reacted to <post angle>. Curious. Are you currently <ICP problem>?") 6. **Optional cross-post analysis.** If the user supplied multiple post URLs, deduplicate engagers and flag people who engaged with 2+ posts (highest-intent signal).

## Inbound-quality signals

High-quality = follow up: founder/operator title, company in ICP, active posting history, >10 mutual 2nd-degree connections, prior thoughtful comments on user's posts.

Low-quality = skip: generic praise, template language ("I'd love to hop on a quick call"), sales/agency profile with no operator history, same comment copy-pasted across many creators.

## Hard rules

Global voice rules: see root `SKILL.md` §Voice rules. Additional skill-specific rules:

- Don't run engager analytics on posts you didn't write or aren't tracking with permission. The data is technically public but high-volume scraping of someone else's audience reads as creepy. - Don't DM a prospect on the same day they engaged with your post. Wait 24-72h to avoid the "thirsty" pattern. - One DM opener per engager, not three. If the first didn't land in 5 business days, drop it.

## Cost accounting

| Action | Apify call | Cost (free tier) | |---|---|---| | Engager analytics on one post (50 engagers) | `fetch_post_engagers(max_items=50)` | $0.25 | | Engager analytics on one post (200 engagers) | `fetch_post_engagers(max_items=200)` | $1.00 |

A weekly engager-analytics run on 1-2 posts stays well under the $5 free monthly credit.

## Files

- `SKILL.md` — this file - `references/output-spec.md` — engager roster shape, tier breakdown, action lists, sample run

## Related skills

- `linkedin-thread-monitor` — track author replies to YOUR comments (different surface) - `linkedin-comment-drafter` — draft outreach comments to engagers from this report - `linkedin-reply-handler` — draft DM follow-ups

--- name: linkedin-engager-analytics description: Pull the people who liked or commented on any LinkedIn post and segment them by ICP fit (peer / aspirational / prospect / other). Produces an engager roster, tier breakdown, and outbound action lists (follow back, comment-drop, DM-able with one-line openers). Powered by Apify, no LinkedIn login. Triggers on "who liked my post", "who engaged", "engagers report", "audience analytics". Not for tracking author replies to your comments (use linkedin-thread-monitor). ---

# LinkedIn Engager Analytics

Pull every liker and commenter on a LinkedIn post and bucket them by ICP fit. Outputs a roster + action list you can feed into your DM or outreach queue.

Depends on `APIFY_TOKEN`. Without it, falls back to user-paste of the engager list.

## When to use

- After publishing a post: "Who actually engaged? Are they ICP?" - Before a campaign: "Pull the last 5 viral posts in my niche, group their commenters by company size" - Reviewing competitor engagement: which prospects show up across multiple authors

## Input

- One or more LinkedIn post URLs - Optional: ICP definition (target titles, company size, industry) - Optional: max engagers per post (default 100)

## Output

Output format (engager roster, tier breakdown, action lists): see `references/output-spec.md`. Headline: a table of engagers labelled by ICP tier and a per-tier action list.

## Steps

1. **Fetch engagers.** Call `lib.ApifyClient.fetch_post_engagers(post_url=<url>, max_items=100)`. Returns a list of dicts with `type` ("commenters" | "likers"), `name`, `subtitle` (job title + company), `url_profile`, `content` (comment text if commenter), `datetime`. Cost is roughly $0.005 per engager-record. 2. **Parse subtitle into structured fields.** The `subtitle` typically reads "Director at Acme Corp" or "Founder & CEO at SaaS Inc". Extract: title, company, seniority bucket (IC / Manager / Director / VP / C-suite / Founder). 3. **Score ICP fit.** Use the user's supplied ICP rules: - Title match (regex or keyword list) - Company size proxy (look up via the user's CRM if integrated, else mark Unknown) - Industry match (parse company name + subtitle keywords) 4. **Assign tier.** - Peer: founder / operator at similar-stage company in same niche - Aspirational: senior leader (Director+) at larger company in adjacent niche - Prospect: title in ICP target list AND company in ICP target list - Other: no match 5. **Produce action lists.** - Follow back: peers with active posting (heuristic: appears as author in `fetch_user_recent_comments` of any team member) - Comment-drop targets: aspirational tier - DM-able: prospect tier, with a one-line DM opener referencing the specific post they engaged with ("Saw you reacted to <post angle>. Curious. Are you currently <ICP problem>?") 6. **Optional cross-post analysis.** If the user supplied multiple post URLs, deduplicate engagers and flag people who engaged with 2+ posts (highest-intent signal).

## Inbound-quality signals

High-quality = follow up: founder/operator title, company in ICP, active posting history, >10 mutual 2nd-degree connections, prior thoughtful comments on user's posts.

Low-quality = skip: generic praise, template language ("I'd love to hop on a quick call"), sales/agency profile with no operator history, same comment copy-pasted across many creators.

## Hard rules

Global voice rules: see root `SKILL.md` §Voice rules. Additional skill-specific rules:

- Don't run engager analytics on posts you didn't write or aren't tracking with permission. The data is technically public but high-volume scraping of someone else's audience reads as creepy. - Don't DM a prospect on the same day they engaged with your post. Wait 24-72h to avoid the "thirsty" pattern. - One DM opener per engager, not three. If the first didn't land in 5 business days, drop it.

## Cost accounting

| Action | Apify call | Cost (free tier) | |---|---|---| | Engager analytics on one post (50 engagers) | `fetch_post_engagers(max_items=50)` | $0.25 | | Engager analytics on one post (200 engagers) | `fetch_post_engagers(max_items=200)` | $1.00 |

A weekly engager-analytics run on 1-2 posts stays well under the $5 free monthly credit.

## Files

- `SKILL.md` — this file - `references/output-spec.md` — engager roster shape, tier breakdown, action lists, sample run

## Related skills

- `linkedin-thread-monitor` — track author replies to YOUR comments (different surface) - `linkedin-comment-drafter` — draft outreach comments to engagers from this report - `linkedin-reply-handler` — draft DM follow-ups

--- name: linkedin-engager-analytics description: Pull the people who liked or commented on any LinkedIn post and segment them by ICP fit (peer / aspirational / prospect / other). Produces an engager roster, tier breakdown, and outbound action lists (follow back, comment-drop, DM-able with one-line openers). Powered by Apify, no LinkedIn login. Triggers on "who liked my post", "who engaged", "engagers report", "audience analytics". Not for tracking author replies to your comments (use linkedin-thread-monitor). ---

# LinkedIn Engager Analytics

Pull every liker and commenter on a LinkedIn post and bucket them by ICP fit. Outputs a roster + action list you can feed into your DM or outreach queue.

Depends on `APIFY_TOKEN`. Without it, falls back to user-paste of the engager list.

## When to use

- After publishing a post: "Who actually engaged? Are they ICP?" - Before a campaign: "Pull the last 5 viral posts in my niche, group their commenters by company size" - Reviewing competitor engagement: which prospects show up across multiple authors

## Input

- One or more LinkedIn post URLs - Optional: ICP definition (target titles, company size, industry) - Optional: max engagers per post (default 100)

## Output

Output format (engager roster, tier breakdown, action lists): see `references/output-spec.md`. Headline: a table of engagers labelled by ICP tier and a per-tier action list.

## Steps

1. **Fetch engagers.** Call `lib.ApifyClient.fetch_post_engagers(post_url=<url>, max_items=100)`. Returns a list of dicts with `type` ("commenters" | "likers"), `name`, `subtitle` (job title + company), `url_profile`, `content` (comment text if commenter), `datetime`. Cost is roughly $0.005 per engager-record. 2. **Parse subtitle into structured fields.** The `subtitle` typically reads "Director at Acme Corp" or "Founder & CEO at SaaS Inc". Extract: title, company, seniority bucket (IC / Manager / Director / VP / C-suite / Founder). 3. **Score ICP fit.** Use the user's supplied ICP rules: - Title match (regex or keyword list) - Company size proxy (look up via the user's CRM if integrated, else mark Unknown) - Industry match (parse company name + subtitle keywords) 4. **Assign tier.** - Peer: founder / operator at similar-stage company in same niche - Aspirational: senior leader (Director+) at larger company in adjacent niche - Prospect: title in ICP target list AND company in ICP target list - Other: no match 5. **Produce action lists.** - Follow back: peers with active posting (heuristic: appears as author in `fetch_user_recent_comments` of any team member) - Comment-drop targets: aspirational tier - DM-able: prospect tier, with a one-line DM opener referencing the specific post they engaged with ("Saw you reacted to <post angle>. Curious. Are you currently <ICP problem>?") 6. **Optional cross-post analysis.** If the user supplied multiple post URLs, deduplicate engagers and flag people who engaged with 2+ posts (highest-intent signal).

## Inbound-quality signals

High-quality = follow up: founder/operator title, company in ICP, active posting history, >10 mutual 2nd-degree connections, prior thoughtful comments on user's posts.

Low-quality = skip: generic praise, template language ("I'd love to hop on a quick call"), sales/agency profile with no operator history, same comment copy-pasted across many creators.

## Hard rules

Global voice rules: see root `SKILL.md` §Voice rules. Additional skill-specific rules:

- Don't run engager analytics on posts you didn't write or aren't tracking with permission. The data is technically public but high-volume scraping of someone else's audience reads as creepy. - Don't DM a prospect on the same day they engaged with your post. Wait 24-72h to avoid the "thirsty" pattern. - One DM opener per engager, not three. If the first didn't land in 5 business days, drop it.

## Cost accounting

| Action | Apify call | Cost (free tier) | |---|---|---| | Engager analytics on one post (50 engagers) | `fetch_post_engagers(max_items=50)` | $0.25 | | Engager analytics on one post (200 engagers) | `fetch_post_engagers(max_items=200)` | $1.00 |

A weekly engager-analytics run on 1-2 posts stays well under the $5 free monthly credit.

## Files

- `SKILL.md` — this file - `references/output-spec.md` — engager roster shape, tier breakdown, action lists, sample run

## Related skills

- `linkedin-thread-monitor` — track author replies to YOUR comments (different surface) - `linkedin-comment-drafter` — draft outreach comments to engagers from this report - `linkedin-reply-handler` — draft DM follow-ups