sensys-artifact-evaluation
Use when packaging a SenSys artifact for the Artifact Evaluation Committee — choosing which of the three ACM badges (Available, Functional, Reproduced) to pursue, building a hardware-optional evaluation path for reviewers without your testbed, documenting energy and hardware provenance, and passing the smoke run that proves functionality.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation --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 Artifact Evaluation SenSys awards **three independent ACM badges** through an Artifact Evaluation Committee: **Artifacts Available**, **Artifacts Evaluated — Functional**, and **Results Reproduced**. They are independent — you may pursue one, two, or all three — and awarded badges are printed on the paper and recorded in the ACM DL. The hard part at SenSys is that your evidence is **physical**: an evaluator usually does not have your motes, your harvester, or your deployment, so the artifact must be built to be judged *without* them. ## Choose the badges deliberately | Badge | Bar | Hardest part at SenSys | |---|---|---| | **Artifacts Available** | Artifact deposited in a permanent public archive with a DOI | Deciding what firmware/traces you can legally and safely release | | **Artifacts Evaluated — Functional** | The artifact runs and does what the paper says | Giving an evaluator without your hardware a way to reach "it runs" | | **Results Reproduced** | Key results re-obtained by the evaluator | Reproducing hardware-measured energy/latency numbers off your testbed | Available is the cheapest and worth claiming almost always. Functional and Reproduced are where the **ha
- Choose the badges deliberately
- Build the hardware-optional path first
- Document the provenance the AEC cannot infer
- Smoke-run before you submit
- Work with the AEC's iteration
- Output format
Package hygiene (manifests, seeds, scripts) — see resources/code/README.md python3 ../../../shared-resources/ml-conference-methods/code/check_repro_package.py ./artifact Then the SenSys-specific smoke: does the trace-replay reproduce a headline figure? cd artifact/analysis && ./reproduce_fig4.sh # should regenerate Fig. 4 from traces/, no hardware
What does the sensys-artifact-evaluation skill do?
Use when packaging a SenSys artifact for the Artifact Evaluation Committee — choosing which of the three ACM badges (Available, Functional, Reproduced) to pursue, building a hardware-optional evaluation path for reviewers without your testbed, documenting energy and hardware provenance, and passing the smoke run that proves functionality.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation --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.