Goose-skills competitor-post-engagers

install

source · Clone the upstream repo

git clone https://github.com/gooseworks-ai/goose-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/gooseworks-ai/goose-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/capabilities/competitor-post-engagers" ~/.claude/skills/gooseworks-ai-goose-skills-competitor-post-engagers && rm -rf "$T"

manifest: skills/capabilities/competitor-post-engagers/SKILL.md

Competitor Post Engagers

Find ICP-fit leads by scraping engagers from a competitor's top-performing LinkedIn posts. Given one or more company page URLs, this skill finds their highest-engagement recent posts, extracts everyone who reacted or commented, and classifies by ICP fit.

Core principle: Scrape all posts in one call per company, then locally rank and select the top N. This minimizes Apify costs while maximizing lead quality.

Phase 0: Intake

Ask the user these questions:

Target Companies

LinkedIn company page URL(s) to scrape (e.g.,
```
https://www.linkedin.com/company/11x-ai/
```
)
Time window — how many days back to look (default: 30)
Top N posts per company to extract engagers from (default: 1)

ICP Criteria

ICP keywords — job title/role terms that indicate a good lead (e.g., "sales", "SDR", "revenue")
Exclude keywords — roles to filter out (e.g., "software engineer", "designer")
Geographic focus (optional, e.g., "United States")

Save config in the current working directory (or user-specified path):

competitor-post-engagers-config.json

Config JSON structure:

{
  "name": "<run-name>",
  "company_urls": ["https://www.linkedin.com/company/<competitor>/"],
  "days_back": 30,
  "max_posts": 50,
  "max_reactions": 500,
  "max_comments": 200,
  "top_n_posts": 1,
  "icp_keywords": ["sales", "revenue", "growth", "SDR", "BDR", "outbound"],
  "exclude_keywords": ["software engineer", "developer", "designer"],
  "enrich_companies": true,
  "competitor_company_names": ["<competitor-name>"],
  "industry_keywords": ["freight", "logistics", "trucking", "transportation", "3pl", "supply chain", "carrier", "brokerage", "shipping", "warehousing"],
  "output_dir": "output"
}

```
enrich_companies
```
— Enable Apollo company enrichment (default: true). Set to false or use
```
--skip-company-enrich
```
to skip.
```
competitor_company_names
```
— Company names to exclude from enrichment (the competitor itself).
```
industry_keywords
```
— Industry terms that indicate ICP fit. Matched against Apollo's industry field.

The

output_dir

is relative to the script directory by default. Override it with an absolute path to write output to a specific location.

Phase 1: Run the Pipeline

python3 skills/competitor-post-engagers/scripts/competitor_post_engagers.py \
  --config competitor-post-engagers-config.json \
  [--test] [--yes] [--skip-company-enrich] [--top-n 3] [--max-runs 30]

Flags:

```
--config
```
(required) — path to config JSON
```
--test
```
— small limits (20 posts, 50 profiles, 1 top post)
```
--yes
```
— skip cost confirmation prompts
```
--skip-company-enrich
```
— skip Apollo company enrichment step (saves credits)
```
--top-n
```
— override top_n_posts from config
```
--max-runs
```
— override Apify run limit

Pipeline Steps

Step 1: Scrape company posts + engagers — For each company URL, one Apify call using

harvestapi/linkedin-company-posts

with

scrapeReactions: true, scrapeComments: true

. Returns posts, reactions, and comments in a single dataset.

Step 2: Rank & select top posts — Filter posts by time window (

days_back

), rank by total engagement (reactions + comments), select top N per company. Then extract engagers (reactors + commenters) only from those selected posts. Deduplication by name. Score engagers by position:

```
+3
```
Commenter (higher intent)
```
+2
```
Position matches ICP keywords
```
-5
```
Position matches exclude keywords

Step 3: Company enrichment (Apollo) — Extract unique company names from engagers, call

apollo.enrich_organization(name=...)

for each. Returns industry, employee count, description, and location. ~1 Apollo credit per unique company. Merge data back to all engagers from that company. Skip with

--skip-company-enrich

"enrich_companies": false

Step 4: ICP classify & export — Classify as Likely ICP / Possible ICP / Unknown / Tech Vendor. Uses both headline keyword matching AND company industry data (from Step 3) — if the engager's company industry matches

industry_keywords

, they're classified as "Likely ICP" regardless of role. Export CSV.

Cost Estimates

Parameter	Test	Standard
Posts scraped per company	20	50
Max reactions	50	500
Max comments	50	200
Est. Apify cost (1 company)	~$0.10	~$0.50-1
Est. Apollo credits (company enrich)	~10-20	~30-80 unique companies
Est. Apollo cost	~$0.05-0.10	~$0.15-0.40

Phase 2: Review & Refine

Present results:

Post selection — which posts were chosen and why (engagement counts, preview)
Per-company breakdown — how many leads from each competitor
ICP breakdown — counts by tier
Top 15 leads — name, role, company, engagement type

Common adjustments:

Too many irrelevant leads — tighten
```
icp_keywords
```
or add
```
exclude_keywords
```
Missing ICP leads — broaden
```
icp_keywords
```
Wrong posts selected — increase
```
top_n_posts
```
or adjust
```
days_back
```
Too expensive — use
```
--test
```
mode or lower
```
max_reactions
```
/
```
max_comments
```

Phase 3: Output

CSV exported to

{output_dir}/{name}-engagers-{date}.csv

Column	Description
Name	Full name
LinkedIn URL	Profile link
Role	Parsed from headline
Company	Parsed from headline
Company Industry	From Apollo enrichment
Company Size	Estimated employee count from Apollo
Company Description	Short company description from Apollo
Company Location	City, State, Country from Apollo
Source Page	Which competitor's page
Post URL	Link to the specific post
Post Preview	First 120 chars of post content
Engagement Type	Comment or Reaction
Comment Text	Their comment (personalization gold)
ICP Tier	Likely ICP / Possible ICP / Unknown / Tech Vendor
Pre-Filter Score	Priority score from pre-filter

Tools Required

Apify API token — set as
```
APIFY_API_TOKEN
```
in
```
.env
```
Apollo API key — set as
```
APOLLO_API_KEY
```
in
```
.env
```
(for company enrichment)
Apify actors used:
- ```
harvestapi/linkedin-company-posts
```
  (post + engager scraping)
Apollo endpoints used:
- ```
organizations/enrich
```
  (company industry/size lookup, 1 credit per company)

Example Usage

Trigger phrases:

"Find leads engaging with [competitor]'s LinkedIn posts"
"Scrape engagers from [company]'s top posts"
"Who is interacting with [competitor]'s content?"
"Run competitor-post-engagers for [company]"

Test mode:

python3 skills/competitor-post-engagers/scripts/competitor_post_engagers.py \
  --config competitor-post-engagers-config.json --test --yes

Full run:

python3 skills/competitor-post-engagers/scripts/competitor_post_engagers.py \
  --config competitor-post-engagers-config.json --yes