Kubeshark Installer
kubeshark/kubeshark
Installs and configures Kubeshark on a Kubernetes cluster, choosing between the quick CLI path and a Helm install with custom values.
A skill your agent uses when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, honest sensor ground truth…
Install Sensys Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-experiments --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-experiments .claude/skills/sensys-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-experiments into .claude/skills/sensys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-experiments", 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-experimentsType 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-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-experiments --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-experiments .agents/skills/sensys-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-experiments into .agents/skills/sensys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-experiments", 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-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-experiments --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-experiments .cursor/skills/sensys-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-experiments into .cursor/skills/sensys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-experiments", 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-experiments--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-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-experiments --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-experiments .gemini/skills/sensys-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-experiments into .gemini/skills/sensys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-experiments", 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-experimentsInstalls 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-experiments -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-experiments .github/skills/sensys-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-experiments into .github/skills/sensys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-experiments", 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-experiments -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-experiments --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-experiments .opencode/skills/sensys-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-experiments into .opencode/skills/sensys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-experiments", 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-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 517 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, honest sensor ground truth…
- Auditing a SenSys evaluation — energy and low-power measurement with a named instrument
- SKILL.md covers The five measurement axes, Measure energy like it is the…, Ground truth is a first-class… and Deployment vs. bench, kept…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Real-testbed and deployment realism
What it does
Sensys Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, honest sensor ground truth, on-device latency and memory, and same-hardware baselines, so the evidence meets SenSys's built-and-measured bar rather than a simulation or offline-benchmark one.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud, covering Deployment. 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
- Auditing a SenSys evaluation — energy and low-power measurement with a named instrument
- Real-testbed and deployment realism
- Honest sensor ground truth
- On-device latency and memory
Example prompts
- “/sensys-experiments”
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
No scripts in the folder and no shell commands in SKILL.md.
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 Experiments loads about 1.4k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 517 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). 517 words, ~1,378 tokens.
Download SKILL.mdSave it as
.claude/skills/sensys-experiments/SKILL.md (or your agent's skills folder).- name
- sensys-experiments
- description
- Use when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, honest sensor ground truth, on-device latency and memory, and same-hardware baselines, so the evidence meets SenSys's built-and-measured bar rather than a simulation or offline-benchmark one.
SenSys Experiments
At SenSys the evaluation is the contribution's proof. A mechanism is only as strong as the measurements that show it behaving on real hardware, under real energy budgets, against honest ground truth. This skill audits an evaluation for the failure modes SenSys reviewers flag first: unmeasured energy, simulation standing in for deployment, and accuracy scored against an unstated truth.
The five measurement axes
Every SenSys evaluation should be explicit about these; a gap in any one is a reviewer question.
| Axis | What to report | Common failure |
|---|---|---|
| Energy / power | Energy per operation, average current (µA/mA), duty cycle — with instrument + sampling rate + wake/sleep boundaries | "Low-power" as an adjective; no method |
| Latency | On-device latency as a distribution (median, tail), not a single number | One workstation timing, no tail |
| Accuracy vs. ground truth | Metric plus how truth was obtained and its own error | Accuracy with unstated reference |
| Deployment realism | Node count, placement, environment, duration, uptime/failures | One-run bench result called a deployment |
| Footprint (embedded AI) | Quantized model size, RAM/flash peak on the actual MCU | Offline model size on a workstation |
Measure energy like it is the headline
Because it is. State the instrument (source-meter, shunt + DAQ, or power monitor), its sampling rate, and the boundaries of what you integrated (does "energy per inference" include sensor acquisition and radio, or only compute?). Report energy with the same rigor as a latency CDF — ideally a power trace annotated with the phases it covers.
Energy reporting template (put the method in the paper, not just the number):
Instrument: Keithley/Otii/INA-class monitor, model + firmware
Sampling rate: e.g. 10 kHz; enough to resolve the wake spike
Integration: from sensor-on to label-out; radio TX included? Y/N
Boundaries: sleep floor measured separately; not inferred from datasheet
Report: energy/op + duty cycle + projected lifetime with battery/harvester specA projected battery life or harvesting budget must be derived from measured draw, not from a datasheet's nominal current — reviewers who have deployed will catch the difference.
Ground truth is a first-class artifact
An accuracy number is only as trustworthy as the truth it is scored against. State how reference labels were obtained — a reference instrument, a controlled stimulus, or a documented human annotation protocol — and the truth's own uncertainty. Sensor experiments where "ground truth" is another uncalibrated sensor, or where labels were assigned by the authors without a protocol, invite exactly the challenge that sinks the result in review.
Show full SKILL.md (179 more words)Show less
Deployment vs. bench, kept separate
Bench experiments control variables to isolate a mechanism; deployments expose it to reality. Report both and label which is which. A deployment carries node count, placement, environmental conditions, duration, and honest uptime/failure accounting — a node that died on day 3 is data, not an embarrassment to hide. A single controlled bench run is not a deployment claim.
Baselines on the same hardware
Compare against the right prior system on the same platform, tuned as well as your own. An apples-to-oranges comparison — your system on an optimized MCU against a baseline you ran untuned, or against numbers copied from a paper that used different silicon — is the most common reviewer objection. If you must cite cross-hardware numbers, say so and bound the comparison.
Intermittent power and harvesting
For batteryless or energy-harvesting systems, the evaluation must include the energy source's
behavior: the harvest trace (indoor light, RF, vibration), the capacitor/energy-buffer sizing,
and behavior across power failures. A harvesting claim without the input-energy conditions is not
reproducible even with the code (see sensys-reproducibility).
Audit checklist
[ ] Energy reported with instrument + sampling rate + integration boundaries.
[ ] Latency as a distribution (median + tail), measured on the target device.
[ ] Accuracy paired with ground-truth provenance and the truth's own error.
[ ] Deployment: node count, environment, duration, uptime/failures stated.
[ ] Baselines run on the same hardware, tuned; cross-hardware numbers flagged.
[ ] Embedded-AI: quantized size + RAM/flash peak on the actual MCU.
[ ] Harvesting: input-energy trace + buffer sizing + power-failure behavior.
[ ] No simulation-only or single-run claim standing in for deployed behavior.Output format
[Axes] which of the five measurement axes are covered / missing
[Energy] method stated? instrument + sampling rate + boundaries — pass/gap
[Truth] ground-truth provenance and its error — pass/gap
[Deploy] deployment realism + honest uptime — pass/gap
[Baseline] same-hardware, tuned comparison — pass/gap
[Open] the single measurement whose absence most weakens the paper© 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-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sensys Experiments 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 Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Kubeshark Installerkubeshark/kubeshark | 12k | — | ~3.6k | Automated safety check: Notes | Apache-2.0 | |
| GreptimeDB Dev Docker ImageGreptimeTeam/greptimedb | 6.7k | — | ~4k | Automated safety check: Notes | Apache-2.0 | |
| KubeSphere ServiceMesh Managerkubesphere/kubesphere | 17k | — | ~2.4k | Automated safety check: Pass | Custom licence | |
| Vercelremotion-dev/remotion | 63k | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| AWS Cdk Developmentzxkane/aws-skills | 367 | 2 repos | ~2.5k | Automated safety check: Pass | MIT |
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Categories
Questions about Sensys Experiments
What does Sensys Experiments do?
A skill your agent uses when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, honest sensor ground truth…. Sensys Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, honest sensor ground truth, on-device latency and memory, and same-hardware baselines, so the evidence meets SenSys's built-and-measured bar rather than a simulation or offline-benchmark one.
When should I use Sensys Experiments?
Sensys Experiments fits situations like: auditing a SenSys evaluation — energy and low-power measurement with a named instrument; real-testbed and deployment realism; honest sensor ground truth; on-device latency and memory.
How do I install Sensys Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-experiments -a claude-code`. Or copy the skill folder (SenSys-Skills/skills/sensys-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sensys-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Sensys Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-experiments -a codex`. Or copy the skill folder (SenSys-Skills/skills/sensys-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sensys-experiments in your project. Codex loads it when a task matches its description.
Can I use Sensys Experiments 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-experiments -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-experiments, .gemini/skills/sensys-experiments, .github/skills/sensys-experiments and .opencode/skills/sensys-experiments in your project.
What does Sensys Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Sensys Experiments is instructions for the agent only.
Does Sensys Experiments 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 Experiments 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 Experiments use?
Sensys Experiments 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 Experiments use?
About 1.4k tokens (SKILL.md is roughly 5.5k 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 Experiments?
Skills that share tags, products or a category with Sensys Experiments: Kubeshark Installer (kubeshark/kubeshark, 12k stars), GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars), KubeSphere ServiceMesh Manager (kubesphere/kubesphere, 17k stars) and Vercel (remotion-dev/remotion, 63k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sensys Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.