Signal Detection Pipeline
Detect buying signals from multiple sources, qualify leads, and generate outreach context
by Athina AInpx gooseworks install --all # then, in Claude Code, Cursor, or Codex: /gooseworks use the signal-detection-pipeline skill
Signal Detection Pipeline
Monitor multiple signal sources to find companies actively in-market for your client's solution. Combine signals for higher-confidence leads.
When to Use
- "Find companies that might need [our product]"
- "Run signal detection for [problem area]"
- "Find buying signals in [industry/topic]"
Signal Sources
Run the sources relevant to the client's ICP. Each is independent — run in parallel.
Job Posting Signals (Strongest)
Skill: job-posting-intent
Companies hiring for roles in the problem area = budget allocated and pain acknowledged.
- Input: Job keywords, ICP criteria
- Output: Qualified companies with outreach angles
Funding Signals
Skill: funding-signal-monitor
Recently funded companies = budget available, growth mandate.
- Input: Industry, funding stage filter
- Output: Funded companies with timing context
Conference Attendance Signals
Skill: luma-event-attendees
People attending events in the problem space = actively engaged.
- Input: Event URLs or topic search
- Output: Person/company list
Reddit Pain Signals
Skill: reddit-post-finder
People complaining about or discussing the problem = experiencing the pain.
- Input: Keywords, relevant subreddits
- Output: Posts with authors, context
LinkedIn Content Signals
Skill: linkedin-post-research + linkedin-commenter-extractor
People posting about or engaging with the problem = thought leaders or practitioners.
- Input: Keywords, time frame
- Output: Posters and commenters with engagement data
Combining Signals
After running relevant sources:
- Deduplicate companies appearing across multiple signals (multi-signal = strongest leads)
- Score each lead: assign signal strength based on source quality and recency
- Job posting + funding = highest intent
- LinkedIn post + Reddit complaint = validated pain
- Single conference attendance = lowest (awareness only)
- Enrich top leads with web search for company details
- Consolidate into a single Google Sheet: Company, Signal Sources, Signal Strength, Context, Outreach Angle
- Prioritize companies with multiple signal types
Human Checkpoints
- After combining signals: Review consolidated list before outreach
What's included
Render Model Comparison Grid
Render a 'model comparison grid' video from a config — a fal-style "same prompt, N contenders" showcase — a dark real-DOM stage where per beat a monospace prompt fades in centered, docks to a small top strip, then a labeled 2-4 panel grid (static images OR muted video clips, mixable per cell) staggers in and holds for comparison, plus a minimal end card — frame-stepped via Playwright (video cells are frame-seeked deterministically) and encoded with FFmpeg. Deterministic assembly, FREE (cell media comes from create-image-fal / create-video-fal, music from create-music-elevenlabs), text stays pixel-crisp. Use for the model-comparison-grid format.
Render Offer Ad
Render a punchy ~12s vertical (9:16) music-only direct-response OFFER ad as a 4-beat kinetic-typography film — HEADLINE slam → real PRODUCT drop → CLAIM/proof → CTA pill — from one config of copy slots, a real product photo, a brand palette, fonts, bpm, and beat split. DETERMINISTIC + FREE (a bundled Remotion project; springs + interpolate, no AI-gen for visuals). Backgrounds are engine gradient divs off the palette, props are inline SVG, the ONLY composited bitmap is the REAL product photo (objectFit:contain, never stretched), and ALL headline/claim/CTA/URL/wordmark text is typeset in the engine — never AI-rendered (the format's credibility guard). A driver binds the config to Remotion input props, renders the 9:16 master, and derives a 1:1 center-crop with ffmpeg. Two gating checks run before render (claim verbs must match the product's physical format; the claim beat needs an edge-entry mechanism prop). Use for the motion-graphics-offer-ad format.
Render Myth Vs Fact
Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word onsets, concat at a uniform fps, karaoke `.ass` captions burned last (suppressed on the proof + end-card beats), and a VO + optional music mix (music −20 dB, `amix normalize=0`, tail fade). FREE (Python + Playwright + ffmpeg); the recipe supplies the copy / palette / end-card / VO and gates the paid VO / music / Whisper calls to their own capabilities. Use for the myth-vs-fact format.