Sam "stunspot" Walker
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🏃 Prompt of the Day: Trackstar – Momentum Dossiers on Talent
🕑 Added 2025-09-19 15:37:58 +0000 UTCSome people look shiny on paper. Trackstar shows you who’s actually moving. It builds decision-grade dossiers on candidates by scanning public, verifiable signals of achievement, influence, and momentum—no guesswork, no hype.
Each profile highlights real proof points: shipped outcomes, patents, talks, OSS commits, leadership moves. You get directional momentum signals (↑ accelerating / → stable / ↓ slowing), recency bands, confidence levels, and outreach angles tied directly to receipts.
The result isn’t a résumé—it’s a momentum map. Perfect for recruiters, operators, and builders who want to see who’s surging now and how to approach them.
— Nova 🏅📊
A people-intel engine that builds decision-ready dossiers on standout individuals using only public, verifiable proof points. It tracks signals of momentum—shipped work, visible influence, scope indicators—and distills them into clear snapshots with receipts, outreach angles, and risk notes. Instead of guesswork or personality reads, it delivers evidence-led profiles that recruiters and operators can act on immediately, showing who’s accelerating, who’s steady, and who’s slowing down.
TRACSTARTrackstar
Conduct a research sweep to assemble a decision-grade dossier on [NAME or LINKEDIN URL], focusing strictly on public, verifiable signals of achievement, influence, and momentum. The goal is to provide recruiters or operators with clear evidence of why (and how) this individual could be approached for [OPPORTUNITY/CHALLENGE].
Approach like a hybrid investigator and recruiter: emphasize observable proof points, recent activity trends, and outreach angles. Express findings through directional signals (↑ accelerating / → stable / ↓ slowing), recency bands, brief counts, and receipts. Do not invent composite scores, personality traits, or hidden inferences.
OPERATOR CONTROLS
Mode: Breadth (scan lightly across 2–3 candidates) | Focus (deep dive on 1 candidate)
Focus Lens: Technical | Product | Leadership | Cross-functional | All
Time Horizon: Default = 90d; extend to 180d only when evidence thin
Cohort Fairness: Compare momentum vs. peers of similar seniority + region to avoid bias toward prolific posters.
RESEARCH METHOD
Signal Sweep: Collect from LinkedIn, GitHub/OSS, blogs, talks, patents, employer sites, press, recorded interviews, and conference material. Save Evidence Receipts (direct URLs, timestamps).
Proof Point Extraction: Pull only verifiable items — shipped outcomes, scope indicators (teams/budgets), visible reach (post cadence, talk invites, median engagement), awards/patents, collabs.
Evidence Floor & Ladder: ≥1 receipt required per proof point; 2 receipts = High confidence. Source priority = Primary (self-published, employer/filings) > Secondary (press/analyst/conference) > Tertiary (aggregators/social).
Momentum Read: Classify last 90–180d activity:
↑ accelerating = ≥2 new substantive outputs/engagements in last 90d + at least one linked to org impact
→ stable = steady cadence, no major acceleration
↓ slowing = notable drop vs. prior 90d, or visible pullback (content dormant, no new projects)
Stop Rule: If no new receipts in 90d, momentum = ↓
Discrepancy Handling: If conflicting evidence (e.g., role scope vs. headcount), show both, mark Contested, lower confidence, and state what would resolve it (e.g., updated LinkedIn, press release).
Fit Hypothesis: State directly how this person’s track record/skills map to [YOUR OPPORTUNITY].
Approach Angles: Draft 2–3 outreach openers tied to receipts, using archetypes:
Outcome Match: “You solved X, we’re facing X+1 at our scale…”
Skill Transfer: “Your method Y is exactly the lever we need for Z…”
Vision Extension: “You’ve pushed the frontier in A, we’re scaling that in B…”
Frictions & Risks: Note evidenced blockers: compensation tier hints, visa/location, domain pivot risk, plus engagement risks (posting droughts, <12mo job hops, unexplained project gaps).
What Would Change My Mind: List the next decisive signal that would shift evaluation (e.g., “If they publish another rev-ops case study, confirms depth in GTM systems”).
