Arize Evaluator
github/awesome-copilot
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and…
Agent skill
Sensys Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging a SenSys artifact for the Artifact Evaluation Committee — choosing which of the three ACM badges (Available, Functional, Reproduced) to pursue, building a…
MITAuto-check passed
Install Sensys Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-artifact-evaluation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/SenSys-Skills/skills/sensys-artifact-evaluation .claude/skills/sensys-artifact-evaluation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "sensys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-artifact-evaluation into .claude/skills/sensys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-artifact-evaluation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-artifact-evaluationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-artifact-evaluation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/SenSys-Skills/skills/sensys-artifact-evaluation .agents/skills/sensys-artifact-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sensys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-artifact-evaluation into .agents/skills/sensys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-artifact-evaluation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-artifact-evaluation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/SenSys-Skills/skills/sensys-artifact-evaluation .cursor/skills/sensys-artifact-evaluation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "sensys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-artifact-evaluation into .cursor/skills/sensys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-artifact-evaluation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path SenSys-Skills/skills/sensys-artifact-evaluation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-artifact-evaluation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/SenSys-Skills/skills/sensys-artifact-evaluation .gemini/skills/sensys-artifact-evaluation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "sensys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-artifact-evaluation into .gemini/skills/sensys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-artifact-evaluation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-artifact-evaluationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/SenSys-Skills/skills/sensys-artifact-evaluation .github/skills/sensys-artifact-evaluation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "sensys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-artifact-evaluation into .github/skills/sensys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-artifact-evaluation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-artifact-evaluation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/SenSys-Skills/skills/sensys-artifact-evaluation .opencode/skills/sensys-artifact-evaluation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "sensys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-artifact-evaluation into .opencode/skills/sensys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-artifact-evaluation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
sensys-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 357 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging a SenSys artifact for the Artifact Evaluation Committee — choosing which of the three ACM badges (Available, Functional, Reproduced) to pursue, building a…
- Packaging a SenSys artifact for the Artifact Evaluation Committee — choosing which of the three ACM badges (Available
- SKILL.md covers Choose the badges deliberately, Build the hardware-optional…, Document the provenance the… and Smoke-run before you submit, plus 2 more sections
- Calls python3
- Reproduced) to pursue
What it does
Sensys Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Packaging a SenSys artifact for the Artifact Evaluation Committee — choosing which of the three ACM badges (Available
- Reproduced) to pursue
- Building a hardware-optional evaluation path for reviewers without your testbed
- Documenting energy and hardware provenance
Example prompts
- “/sensys-artifact-evaluation”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sensys Artifact Evaluation loads about 1.2k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 357 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 357 words, ~1,155 tokens.
Download SKILL.mdSave it as
.claude/skills/sensys-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- sensys-artifact-evaluation
- description
- 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.
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 hardware-optional path earns its keep.
Build the hardware-optional path first
Most AEC members will not have your testbed. Give them a graded way in:
Tier 0 — Available: archived repo (DOI), firmware sources, traces, README.
Tier 1 — Bench/replay: recorded sensor + power traces the analysis re-runs on any laptop,
reproducing the paper's figures from stored data.
Tier 2 — Emulation: a QEMU/renode-class emulator or a single dev board that reaches
"it runs" without the full deployment.
Tier 3 — Full HW: scripts + BOM for an evaluator who does have the platform.A trace-replay path is the single most valuable thing you can ship: it lets an evaluator reproduce your figures from your recorded energy and sensor data even if they cannot re-run the deployment. Document exactly which figures/tables the replay reproduces and which need hardware.
Show full SKILL.md (122 more words)Show less
Document the provenance the AEC cannot infer
artifact/
README.md # claims → which script/trace reproduces each figure/table
firmware/ # sources + toolchain version + build flags
traces/ # recorded power + sensor data behind each figure
analysis/ # scripts that turn traces into the paper's plots
hardware/ # BOM, board revision, wiring, instrument model + settings
ENERGY.md # instrument, sampling rate, integration boundaries
GROUND_TRUTH.md # how reference labels were obtained and their error
LICENSEThe ENERGY.md and GROUND_TRUTH.md files are SenSys-specific: an evaluator reproducing a
number needs the measurement method, not just the code (see sensys-reproducibility).
Smoke-run before you submit
Prove the package installs and runs from clean before an evaluator ever sees it:
# 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 hardwareIf the replay does not regenerate a figure on a clean machine, no badge claim is safe yet.
Work with the AEC's iteration
The committee may ask for revisions and iterate with you. Respond fast and concretely: a missing dependency or an unclear step is a quick fix, and the AEC is trying to award the badge, not deny it. Keep the anonymity rules of the round if evaluation overlaps the review window.
Output format
[Badges] which of the three you are pursuing, and why each is reachable
[HW-path] the graded path (Available/Replay/Emulation/Full-HW) — which tiers exist
[Provenance] ENERGY.md + GROUND_TRUTH.md + firmware toolchain present? pass/gap
[Smoke] does trace-replay reproduce a headline figure on a clean machine? Y/N
[Open] the gap most likely to block Functional or Reproduced© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in SenSys-Skills/skills/sensys-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sensys Artifact Evaluation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Sensys Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| Artifacts Buildernexu-io/open-design | 100k | — | ~347 | Automated safety check: Pass | Apache-2.0 | |
| Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~969 | Automated safety check: Pass | MIT | |
| Mobisys Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1k | Automated safety check: Pass | MIT | |
| Sigcomm Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT |
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Questions about Sensys Artifact Evaluation
What does Sensys Artifact Evaluation do?
A skill your agent uses when packaging a SenSys artifact for the Artifact Evaluation Committee — choosing which of the three ACM badges (Available, Functional, Reproduced) to pursue, building a…. Sensys Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Sensys Artifact Evaluation?
Sensys Artifact Evaluation fits situations like: packaging a SenSys artifact for the Artifact Evaluation Committee — choosing which of the three ACM badges (Available; reproduced) to pursue; building a hardware-optional evaluation path for reviewers without your testbed; documenting energy and hardware provenance.
How do I install Sensys Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation -a claude-code`. Or copy the skill folder (SenSys-Skills/skills/sensys-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sensys-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Sensys Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation -a codex`. Or copy the skill folder (SenSys-Skills/skills/sensys-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sensys-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Sensys Artifact Evaluation in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-artifact-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sensys-artifact-evaluation, .gemini/skills/sensys-artifact-evaluation, .github/skills/sensys-artifact-evaluation and .opencode/skills/sensys-artifact-evaluation in your project.
What does Sensys Artifact Evaluation need to run?
Going by SKILL.md and its folder, Sensys Artifact Evaluation needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Sensys Artifact Evaluation access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Sensys Artifact Evaluation safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Sensys Artifact Evaluation use?
Sensys Artifact Evaluation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sensys Artifact Evaluation use?
About 1.2k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Sensys Artifact Evaluation?
Skills that share tags, products or a category with Sensys Artifact Evaluation: Arize Evaluator (github/awesome-copilot, 40k stars), Artifacts Builder (nexu-io/open-design, 100k stars), Ccs Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Mobisys Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sensys Artifact Evaluation?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.