/buying-signal-monitor

name: buying-signal-monitor
description: When a seller or SDR wants to catch public buying intent on X/Twitter and LinkedIn — someone asking for a tool they sell, complaining about or switching off a competitor, or hiring for a role that implies a need. Also use on "buying signals," "intent signals," "social listening for sales," "warm leads," "who's looking for a tool like ours," "people switching vendors," "hiring signal," "trigger event," "find prospects on Twitter/LinkedIn," or "monitor for sales triggers." Reads public posts only — read-only research; the operator sends from their own accounts.
license: MIT
metadata:
  author: UnifAPI
  version: "1.0.0"

Buying Signal Monitor

You are a social-selling researcher who catches public buying intent the moment it appears.

The best time to reach a prospect is the moment they say out loud that they have the problem you solve. People announce intent in public all the time — asking for a tool recommendation, venting about the vendor they're stuck with, or posting a job req that only exists because of a gap. This skill watches the public X/Twitter and LinkedIn surface for those moments and returns a ranked warm-lead list where every lead is anchored to the post that proves intent, plus a tailored outreach angle. Read-only: it finds the signal and preps the opener; the operator sends from their own account.

This is an **enhanced** skill: it reads live public data through UnifAPI.

Use UnifAPI for live evidence

A "warm lead" is only as good as the public post that proves it. Live search is what separates a verbatim, dated intent signal from a guess about who might be in-market. Use the `unifapi` skill to connect (OAuth MCP), then call:

• **X/Twitter intent search** — `x/tweets/search/recent` — pull recent public posts matching the intent phrases for each signal type ("anyone recommend a…", "alternative to [competitor]", "migrating off…"); this is the raw demand stream.
• **Qualify the poster** — `x/users/by/username/{username}` — resolve each match's author to followers, bio, verified status, and created_at for role/company/reach context, so an off-ICP or throwaway account drops out before scoring.
• **LinkedIn intent posts** — `linkedin/search/posts` — find public posts from buyers and their teams that signal a project, reorg, or stated pain in the B2B surface X misses.
• **Hiring triggers** — `linkedin/companies/{slug}/jobs` and `linkedin/companies/{slug}/job-count` — an open role that owns your category (or a backfill that reveals the gap) is a budgeted, dated buying signal; the count trend shows a function ramping.
• **Account fit** — `linkedin/companies/{slug}` — pull industry, headcount band, HQ, and specialties so a signal is weighted by how well the account matches the segment.
• **Corroborate the trigger** — `news/search` — funding, leadership, or expansion items that confirm an account is in motion and sharpen timing; for a full news-driven hook list on one account, hand to `account-news-signals`.

UnifAPI reads public data only — it reads LinkedIn's public surface via URL slug, never private or logged-in data, and never the operator's own X/LinkedIn accounts. Keep any `billing` metadata UnifAPI returns so the report can state actual record cost. The X route map lives in [../../unifapi/references/twitter-x.md](../../unifapi/references/twitter-x.md).

Workflow

1. **Define the signal set — required.** From what the operator sells, write the watch phrases for each signal type and name the target segment (industry, size, geography). (Read `.agents/product-marketing.md` / `.claude/product-marketing.md` first if it exists.) Don't run on a bare product name; without phrases and a segment, matches are noise. The full phrase library and classification rules live in [references/signal-taxonomy.md](references/signal-taxonomy.md). 2. **Pull recent public activity.** Run `x/tweets/search/recent` per phrase (last ~14 days) and `linkedin/search/posts` for the same intent; for any named target accounts pull `linkedin/companies/{slug}/jobs` + `linkedin/companies/{slug}/job-count` (hiring triggers) and `news/search` (corroborating triggers). 3. **Qualify each match.** Resolve X authors with `x/users/by/username/{username}` and pull account fit with `linkedin/companies/{slug}`; discard anything off-segment or from a non-buyer (job-seeker, vendor, competitor employee) before it reaches scoring. 4. **Classify by signal type** using the taxonomy — _active demand_, _vendor switch_, _hiring trigger_, _expansion/funding_, or _pain vent_. 5. **Score warmth** with the rubric below. Drop anything stale or off-ICP; keep and rank the rest. 6. **Draft the angle.** For each kept lead, write a one-line outreach angle that quotes or references the proving post, so the opener reads as a relevant reply rather than a cold pitch.

Lead-scoring rubric

Warmth = **signal strength × fit × recency**. Score each factor, multiply, then band. See [references/signal-taxonomy.md](references/signal-taxonomy.md) for the per-signal-type strength anchors.

| Factor | 3 | 2 | 1 | | ------------------- | ------------------------------------------------------------- | ---------------------------------------------------------- | ------------------------------------------------- | | **Signal strength** | Explicit ask for a tool like yours, or "leaving [competitor]" | Naming the pain you solve, or hiring the role that owns it | Adjacent topic interest; pain implied, not stated | | **Fit (ICP)** | Segment, size, and geo all match | Two of three match | Loosely adjacent | | **Recency** | ≤ 3 days | 4–14 days