# Who likes your competitor's posts – as checked leads.

People who like or comment on your competitor's posts are thinking about your topic right now. ListPlus imports the reactions, keeps only people who fit your ICP, enriches them – and your agent fetches the rows above quality X as JSON.

https://listplus.ai/en/gtm-workflows/linkedin-post-reactions-competitor-leads

## Inputs and connections

One or more post URLs – a competitor's latest posts or an industry influencer's. Your ICP as score criteria. You start the import in ListPlus; everything after that your agent can do.

The import runs in ListPlus (a scraper priced per result). To fetch, the agent needs the list with Read rights; to enrich, “Full”.

## Steps

1. **Import the reactions** (ListPlus · manual in ListPlus). Paste the post URL, pick a result limit, see the price. Every row keeps post and reaction; two reactions by one person are one record with two reasons.
2. **Check against your ICP** (ListPlus · runs automatically). A score column rates role, company size and industry – plus an AI criterion for what no rule captures. Customers and competitors drop out through the reference list. Whoever does not fit stays in the list but costs nothing more.
3. **Enrich only the fits** (ListPlus · runs automatically). For rows scoring 80 or more the rule sets the status “ICP” and looks up the work email – with a budget per contact, charged only on a hit. The checks verify the found address.
4. **Fetch above quality X as JSON** (Your agent · by call). query_records with the filter status “ICP” and a valid email returns the rows as JSON – from Claude Code, n8n or ChatGPT. Your stack decides what happens next: sequence, CRM, sheet.

## Expected sample result

Of 184 reactions, 31 fit the ICP; 19 of them have a valid work email and come back as JSON. Mia Winter reacted twice – one record with two reasons. Tom Lenz is on the competitor reference list and drops out.

## Costs and boundaries

Importing up to 25 reactions costs at most 7.5 credits ($0.26 on Pro): 0 credits base, 0.3 per result. Checks and a rule-based score cost nothing, an AI criterion in the score 0.02 credits per AI answer; the email lookup for the fits is charged per hit, within the budget you set.

A reaction is a reason, not a buying signal. Never open with “I saw you liked…” – take the topic of the post. Real people: names and addresses belong in your stack, not in screenshots.

## Agent prompt

In the list “Competitor reactions Q4”: read the score. Give me every row with score ≥ 80 and a valid work email as JSON (name, role, company, email, post, reaction). Rows between 60 and 79: name, role, company only – no enrichment. Change nothing, send nothing.

## Tools

`describe_list`, `query_records`, `add_column`, `create_trigger`, `enrich_records`

MCP: https://listplus.ai/mcp
REST: https://listplus.ai/a/v1/{action}
API: https://listplus.ai/a

## Sample calls

These calls use fictional IDs. Use the IDs returned by your workspace.

```text
query_records({
  "list_id": "<shared_list_id>",
  "filter": {
    "ar_status": "ICP",
    "Email": {
      "condition": "valid_email"
    }
  },
  "columns": [
    "first_name",
    "last_name",
    "job_title",
    "company_name",
    "email",
    "Fit score"
  ]
})
```