| name | community-radar |
| title | Community radar |
| description | Monitors public community or social mentions of the brand, competitors, or relevant pain language. Classifies sentiment and drafts short, human replies for the right responder. |
| category | Signals |
Instructions
Setup state. Not yet configured for this org. Load the Setup sub-page and walk the user through wiring the community-radar trigger (SCHEDULE + Apify search actors across LinkedIn / X / Reddit / Hacker News, or WEBHOOK from a social listening tool the user already pays for) with the org's brand-and-pain-language keyword list, the platforms to monitor, and the follow-up sequence before running this play. (After setup is performed, rewrite this paragraph via swan-update-skill to describe the current state — trigger type chosen, keywords / brand terms / pain-language phrases monitored, platforms covered, sequence wired, success metric, and last-refreshed date — so future runs see the current configuration without re-checking.)
When this fires
A SCHEDULE trigger running an Apify search actor (LinkedIn search, X / Twitter search, Reddit search, Hacker News scrape) surfaces new mentions of the configured keywords. Or a WEBHOOK from a social listening service the user already pays for (Brand24, Mention, Hootsuite, Triggify, custom Reddit / X monitoring) pushes mentions in. Payload includes: platform, author, mention text, post URL, engagement metrics on the parent post.
Note: LINKEDIN_ENGAGEMENT is not the right trigger here — that one follows specific LinkedIn profiles. For keyword-driven brand-mention sweeps across LinkedIn, use SCHEDULE + an Apify LinkedIn-search actor.
The window is short on public social — 24-48 hours feels reasonable; > 1 week and the reply looks bot-driven.
Step 1 — Classify the mention
| Class | Pattern | Right move |
|---|
| Direct praise | "We love [your product]" | Like, optional thank-you reply. Resharable. |
| Customer Q / mild frustration | "How do I do X in [your product]?" | Helpful reply from support handle. Resolve the question. |
| Public complaint | "[Your product] is broken / disappointing" | Acknowledge, DM to take offline, don't argue publicly. |
| Comparison shopping | "Looking at [you] vs [competitor]" | Soft entry; offer to help with the eval. Don't trash competitor. |
| Competitor switch signal | "Just switched off [competitor]" + same thread mentions you | High-value lead; warm DM. |
| Pain mention (your wedge, no brand) | "Why can't I find a tool for X" | Soft helpful reply. Don't pitch — offer perspective. |
| Generic noise / spam / off-topic | — | Ignore. |
Step 2 — Identify the author
swan-fetch-scraped-url on the author's profile (LinkedIn, Twitter bio, Reddit profile). Capture: role, company, follower count, post pattern. Don't enrich if it's clearly noise.
For LinkedIn: swan-enrich-contact if they look ICP-fit.
For other platforms: company affiliation is often in bio; cross-check via swan-search-companies.
Step 3 — ICP and CRM context
For mentions from ICP-fit authors:
swan-search-companies + (if new) swan-enrich-companyhubspot-search-objects for existing relationship
For non-ICP: still respond if it's a complaint or Q (support obligation), but don't pursue.
Step 4 — Choose the right responder
Public replies should come from the right account:
| Class | Right responder |
|---|
| Praise | Founder / CEO (high-status reply) |
| Customer Q | Support handle / CSM |
| Complaint | Support handle, then CSM via DM |
| Comparison shopping | AE, via DM not public comment |
| Switch signal | AE, via DM, fast |
| Pain mention | Founder / thought leader, public comment |
If multiple senders are connected, pick the one whose voice fits the moment. Don't auto-reply from a generic brand account if a person's voice would land better.
Step 5 — Draft the reply
Templates:
Public complaint:
"Sorry to hear this. DMing now — want to get this sorted today."
(Then DM with substance and a fix.)
Comparison shopping (DM):
"Saw your post — happy to help with the eval, no pitch. What matters most for you in [category]? I can be straight about where we win and where we don't."
Pain mention (no brand):
"Same — this is one of those problems that's worse than people say. Our take: [one-line perspective]. Happy to share more if useful."
Switch signal (DM):
"Just saw your post about leaving [competitor] — congrats on the cleanup. If [your product] is on the eval list, glad to give you the no-pitch tour."
Critical: short, human, no marketing. Public social rewards low-key over polished.
Step 6 — Channel: public reply vs DM
Default to DM for anything sales-adjacent. Public replies should be ones you're OK with anyone in the future reading — they live forever and get screenshot.
Public is right for: praise threads (you're amplifying), pain mentions where helpful insight beats outreach, supportive Q&A.
Step 7 — Route or send
For LOW-stakes (praise, generic Q): the system can auto-reply with the right account. Surface for approval if voice matters.
For MEDIUM-stakes (comparison shopping, switch signals): hand off to the AE via hubspot-create-task — let the human draft. Sales DMs need human nuance.
For HIGH-stakes (complaint, brand crisis): notify the right responder via slack-send-notification immediately. Don't let a slow CRM task be the bottleneck.
Step 8 — Log
swan-update-company to log the mention. If a complaint, log both the issue and the resolution path. If a switch signal converts, log the source — social mentions that convert are some of the highest-ROI to track over time.
Rules
- MUST classify the mention before drafting. The reply for praise and the reply for complaint are different jobs.
- MUST keep public replies human and low-key. Polished marketing in a Reddit thread is brand suicide.
- MUST DM-not-public for anything sales-adjacent.
- NEVER argue publicly with a complaint. Acknowledge, take it offline, fix it.
- NEVER name a competitor pejoratively in a public reply. Even if the OP did.
- NEVER auto-reply to high-stakes mentions. Humans only.
- If sentiment is escalating (multiple replies, growing engagement on a negative thread), escalate fast — that's a brand crisis, not a routine signal.
- If a tool result is truncated, read from
files/tool-outputs/<toolName>_<callId>.json in swan-execute-code.
Tighten over time
After 10-20 fires, read the responder log via swan-search-sequences and review which mention classes actually converted (switch signals and comparison shopping usually outperform pain mentions). Drop low-yield keywords from the trigger, tighten the keyword list to brand + competitor + 3-5 highest-signal pain phrases, and revisit the platform mix — Reddit often outperforms X for B2B signal.
GAP: native social listening (beyond LinkedIn) isn't a Swan-native tool today. Customers route X / Reddit / Hacker News mentions via webhook from external listening tools.
