sensys-topic-selection
Use when deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built, measured sensing/embedded/IoT/on-device-AI system rather than a pure algorithm, a mobile-networking mechanism, or an offline ML result, and routing misfits to MobiCom, MobiSys, or an ML/DSP venue.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-topic-selection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SenSys Topic Selection SenSys accepts a **system you built and measured under embedded constraints**. The 2026 merger of SenSys, IPSN, and IoTDI widened the mandate — low-power networked sensing, embedded systems, IoT, and on-device AI now share one venue — but it did not soften the systems bar: a strong SenSys paper still has a **buildable mechanism** whose value is shown in **energy, latency, memory, and deployment behavior on real hardware**, not in a proof or a leaderboard delta. ## The fit test Ask these in order; a "no" is a routing signal, not a verdict on the work's quality. 1. **Is there a system?** Can you point to the artifact — firmware, a node, a protocol, a deployment — that embodies the contribution? A pure algorithm with no embedded realization is a signal-processing or ML contribution. 2. **Do embedded constraints bind?** Does energy, memory, compute, bandwidth, or intermittent power actually shape the design? If the method would run unchanged on a workstation, the constraint is not doing work and SenSys is a weak fit. 3. **Is the evidence physical?** Are the headline numbers **measured on hardware** — current draw, duty cycle, on-device latency, deployment uptim
- The fit test
- Routing table
- Post-merger boundary calls
- When two venues both fit
- Output format
What does the sensys-topic-selection skill do?
Use when deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built, measured sensing/embedded/IoT/on-device-AI system rather than a pure algorithm, a mobile-networking mechanism, or an offline ML result, and routing misfits to MobiCom, MobiSys, or an ML/DSP venue.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-topic-selection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 984 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.