# Uist Artifact Evaluation > Use when packaging the artifacts behind a UIST paper — code, toolkits, hardware design files, and datasets — first as anonymous review-time evidence that the system is real, then as a public release engineered for reuse, in a venue with no formal badge committee doing the checking for you. - Skill: `thedixitjain/uist-artifact-evaluation` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/uist-artifact-evaluation` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/uist-artifact-evaluation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/uist-artifact-evaluation --- # UIST Artifact Evaluation UIST has no artifact-evaluation committee or badge track (none was found for the 2026 cycle — 待核实 each year); the CFP-level instrument of proof is the video figure. That absence raises rather than lowers the packaging bar: your artifacts are judged twice, informally — at review time as *evidence the system exists as claimed*, and after publication as *infrastructure other builders adopt*. Nobody will certify either; both simply succeed or fail. ## What counts as the artifact, by paper type | Paper type | Review-time artifact | Reuse-time artifact | |---|---|---| | Interaction technique | Reference implementation + demo scene | Portable library with the technique isolated | | Toolkit / authoring system | Runnable toolkit + the example apps from the paper | Documented API, tutorials, package registry entry | | Hardware / fabrication | Design files, firmware, BOM, assembly photos | Fab-ready files + sourcing notes + calibration guide | | Sensing / recognition pipeline | Trained models + capture data + eval harness | Dataset with collection protocol + retraining scripts | | Human-AI / LLM system | Prompts, orchestration code, pinned model IDs, logged transcripts | Same, plus cost and drift notes | ## Review-time packaging: the five-minute skeptic A reviewer gives your supplement five minutes, anonymously, on a machine you don't control. Optimize for that reader: - One `README` at the archive root: what this is, which paper section each directory backs, and one command (or one video) per claim. - Prefer a **recorded run alongside the code** for anything with hardware, drivers, or GPU dependencies — reviewers cannot rebuild your rig, so show the harness producing the paper's numbers. - Pin everything (lockfiles, container image digests, model checkpoints); "latest" is a broken artifact by review week. - Anonymize as strictly as the PDF: repository history, notebook authorship cells, hardcoded home paths, calibration files named after lab members (see `uist-submission` for the sweep). ```text supplement.zip ├── README.md # claim → artifact map; 5-minute quickstart ├── technique/ # core implementation, pinned deps ├── hardware/ # schematics, PCB, STL/STEP, BOM.csv, firmware/ ├── eval/ # harness + raw logs behind Tables 1-2 │ └── rerun.sh # regenerates the paper's numbers from logs ├── media/ # per-claim capture clips (beyond the video figure) └── LICENSES.md # third-party components and their terms ``` ## Release-time packaging: engineering for strangers At camera-ready (see `uist-camera-ready`), the audience flips from three skeptics to an open-ended stream of builders: 1. **De-anonymize deliberately** — publish to the real org, restore attribution, add the paper citation and BibTeX to the README. 2. **Cut a release tag** matching the camera-ready ("as-published") so later development never orphans the paper's claims. 3. **Choose licenses by artifact class**: code (e.g. MIT/Apache-2.0), hardware designs (e.g. CERN-OHL), data (e.g. CC-BY) — one archive often needs all three, and institutional tech-transfer rules for hardware are worth checking early. 4. **Archive beyond the repo**: deposit the tagged release with a DOI service so the URL in the proceedings outlives your hosting choices. 5. **State the support posture** honestly in the README — "research prototype, issues welcome, no maintenance promised" is respectable; silence is not. ## What the informal evaluators open first Order the package for actual reading behavior: 1. **README, thirty seconds.** If the claim → artifact map is not visible without scrolling, the evaluation is over. 2. **The media directory, two minutes.** Clips of the harness producing the paper's numbers get watched; they are the highest-credibility artifact per byte, especially for hardware. 3. **One quickstart command, two minutes.** Whatever you name in the README as "run this" will be run in a fresh environment; test it in a container or a colleague's clean machine, not your dev box. 4. **Source spot-checks.** Reviewers grep for the mechanism the paper claims is novel; if the "self-calibrating controller" is a 30-line stub, the paper's credibility inverts. Never ship scaffolding that contradicts the prose. ## Toolkit papers: adoption is the long evaluation For toolkit and authoring-system contributions, the release *is* the deferred evaluation, and small engineering choices compound: - Publish to the ecosystem's registry (pip/npm/crates/Arduino library manager) — installability is adoption's first filter. - Ship the paper's example applications as runnable starters; they are the tutorials people actually read. - Keep the API surface documented at the level of the paper's abstractions, so citations of the toolkit describe your concepts in your vocabulary. - Track downstream uses; a "built with X" list is both maintenance motivation and the evidence base for the retrospective the venue's decade-scale memory eventually invites. ## Hardware honesty Physical artifacts cannot be uploaded, so their evidence standard is reconstruction: exact part numbers with sources, tolerances that matter, assembly sequence photos, firmware flashing instructions, and the calibration procedure with expected readings. A paper whose device only the authors can build has published a demo, not a contribution — reviewers from fabrication-heavy labs apply exactly that test (see `uist-reproducibility` for the replication ledger). ## Output format ```text [Artifact class] technique / toolkit / hardware / pipeline / hybrid [Review package] five-minute test passes? claim→artifact map complete? [Anonymity] archive-level sweep clean? [Release plan] tag · licenses (code/hardware/data) · DOI deposit · support posture [Gap list] ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `UIST-Skills/skills/uist-artifact-evaluation/SKILL.md`