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 IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy/latency/footprint measurement on real…
Install Ipsn Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ipsn-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ipsn-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/IPSN-Skills/skills/ipsn-experiments .claude/skills/ipsn-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 "ipsn-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/IPSN-Skills/skills/ipsn-experiments into .claude/skills/ipsn-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsn-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/IPSN-Skills/skills/ipsn-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 ipsn-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ipsn-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/IPSN-Skills/skills/ipsn-experiments .agents/skills/ipsn-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 "ipsn-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/IPSN-Skills/skills/ipsn-experiments into .agents/skills/ipsn-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsn-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 ipsn-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ipsn-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/IPSN-Skills/skills/ipsn-experiments .cursor/skills/ipsn-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 "ipsn-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/IPSN-Skills/skills/ipsn-experiments into .cursor/skills/ipsn-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsn-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 IPSN-Skills/skills/ipsn-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 ipsn-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ipsn-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/IPSN-Skills/skills/ipsn-experiments .gemini/skills/ipsn-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 "ipsn-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/IPSN-Skills/skills/ipsn-experiments into .gemini/skills/ipsn-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsn-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 ipsn-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 ipsn-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/IPSN-Skills/skills/ipsn-experiments .github/skills/ipsn-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 "ipsn-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/IPSN-Skills/skills/ipsn-experiments into .github/skills/ipsn-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsn-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 ipsn-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 ipsn-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/IPSN-Skills/skills/ipsn-experiments .opencode/skills/ipsn-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 "ipsn-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/IPSN-Skills/skills/ipsn-experiments into .opencode/skills/ipsn-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ipsn-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
ipsn-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 606 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 IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy/latency/footprint measurement on real…
- Auditing IPSN-lineage evaluations
- SKILL.md covers Evaluation audit, Claim-to-evidence design table, On-device / TinyML measurement… and Ground-truth and calibration…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering real testbeds and deployments
What it does
Ipsn Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy/latency/footprint measurement on real hardware, on-device/TinyML profiling, estimation-theoretic baselines and bounds, and matching evidence to the shape of each sensing claim across the IP and SPOTS tracks.
Its SKILL.md is about 1.5k 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 IPSN-lineage evaluations
- Covering real testbeds and deployments
- Ground-truth instrumentation
- Energy/latency/footprint measurement on real hardware
Example prompts
- “/ipsn-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
Ipsn Experiments loads about 1.5k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 606 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). 606 words, ~1,451 tokens.
Download SKILL.mdSave it as
.claude/skills/ipsn-experiments/SKILL.md (or your agent's skills folder).- name
- ipsn-experiments
- description
- Use when designing or auditing IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy/latency/footprint measurement on real hardware, on-device/TinyML profiling, estimation-theoretic baselines and bounds, and matching evidence to the shape of each sensing claim across the IP and SPOTS tracks.
IPSN Experiments
Use this before submission when the evaluation is not yet locked. IPSN reviewers are sensor-systems and information-processing specialists; the evaluation is where a good idea is won or lost. The organizing principle is evidence measured on real hardware against real ground truth — the evaluation must test the sensing claim the paper actually makes, on platforms and baselines a skeptic would accept.
Evaluation audit
- Measure on real hardware, not just simulation. Simulation can motivate or scale-test, but a sensing claim needs real sensors: an estimator run on real traces, a pipeline profiled on the actual MCU, a deployment in a real environment. "Simulation only" is IPSN's classic reject.
- Instrument ground truth. Localization needs surveyed positions; detection needs hand-labeled events; a physical estimate needs a co-located reference instrument. Report the ground truth's own error — perfect ground truth is a red flag.
- Measure energy, latency, and footprint on the platform. Report joules/µJ per operation from an instrumented power rail (name the shunt/instrument/sampling rate), end-to-end latency on the real SoC at a stated clock, and RAM/flash used vs available. Estimated energy is not measured energy.
- Choose fair, real baselines. Include the strongest prior method and a simple-but-reasonable alternative (often a classical-DSP or analytic baseline), run under equal conditions on the same hardware. For IP-track claims, compare to an estimation-theoretic bound (e.g., a Cramér-Rao-style lower bound) where one exists.
- Isolate the learning's marginal value (on-device/TinyML). Ablate the learned component against a heuristic/DSP baseline so "did the model help, or the sensing setup?" is answered.
- Design limits in, not on. Know before you deploy which site-specificity, calibration drift, and generalization limits the study will have, and instrument to bound them.
Claim-to-evidence design table
| Sensing claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "Estimator is more accurate" | Error vs ground truth on real traces, with CIs, vs a tuned baseline / a bound | "Simulated inputs only" |
| "Runs within an energy budget" | Measured µJ/op on an instrumented rail on the real MCU | "Energy estimated from datasheet" |
| "Localizes to X meters" | Surveyed ground-truth positions; error distribution, not just mean | "Ground truth from the same model being tested" |
| "Deploys reliably" | Yield, sync error, packet loss over a real deployment duration | "Idealized single-run numbers" |
| "The on-device model adds value" | Ablation vs classical DSP / heuristic on the same hardware | "Model's marginal contribution never isolated" |
| "Scales to N nodes" | Real or emulated multi-hop at realistic scale, with the bottleneck named | "Two-node bench test, universal claim" |
Show full SKILL.md (205 more words)Show less
On-device / TinyML measurement floor
[Platform] exact MCU/SoC, clock, RAM/flash; the sensor and sampling regime
[Energy] µJ per inference/op, instrument named; duty cycle if always-on
[Latency] end-to-end on-device latency; number of runs and variance
[Footprint] model/pipeline RAM+flash vs available; what had to be quantized/pruned
[Contamination] for learned components, keep train/field data disjoint; report the split
[Ablation] learned component vs DSP/heuristic baseline on the same nodeGround-truth and calibration floor
- Name the ground-truth reference and its own measurement error; a claim can be no better than its reference.
- Document calibration: procedure, when it was done, and drift over the deployment.
- Archive raw sensor traces and the calibration data, not just derived metrics (see
ipsn-reproducibility).
Deployment reporting floor
- Report yield (fraction of expected data received), synchronization error, packet loss, and energy over the actual deployment duration, not a best single run.
- State the environment and why it is representative (or not) — external validity for a physical system is site-bound.
- Report failures: nodes that died, data gaps, and what caused them. Honest deployment reporting is itself a contribution and a reviewer trust signal.
Vignette: evaluating a localization estimator (IP track)
The paper claims a new estimator localizes better than the prior method. The matching plan: collect real RF/acoustic traces at surveyed positions; run both estimators on the same traces under equal tuning; report the full error distribution (not just the mean) with confidence intervals; compare against the relevant estimation-theoretic bound; and state the environments (indoor/outdoor, multipath regimes) as a bounded external-validity limit — every number traceable to a logged run and the surveyed ground truth in the artifact.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: platform / ground truth / metric / statistic>
[Real-hardware check] measured on real sensors/MCU, not simulation only? yes/no
[Energy accounting] <µJ/op measured? instrument named? footprint reported?>
[Baseline fairness] <strongest prior + simple baseline, equal conditions, same hardware?>
[Limits-by-design] <site / calibration / generalization -> instrumentation to bound it>
[Decision-critical next run] <one experiment or deployment extension>© 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 IPSN-Skills/skills/ipsn-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Ipsn 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 |
|---|---|---|---|---|---|---|
| Ipsn Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | 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 | 62k | — | ~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 Ipsn Experiments
What does Ipsn Experiments do?
A skill your agent uses when designing or auditing IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy/latency/footprint measurement on real…. Ipsn Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy/latency/footprint measurement on real hardware, on-device/TinyML profiling, estimation-theoretic baselines and bounds, and matching evidence to the shape of each sensing claim across the IP and SPOTS tracks.
When should I use Ipsn Experiments?
Ipsn Experiments fits situations like: auditing IPSN-lineage evaluations; covering real testbeds and deployments; ground-truth instrumentation; energy/latency/footprint measurement on real hardware.
How do I install Ipsn Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ipsn-experiments -a claude-code`. Or copy the skill folder (IPSN-Skills/skills/ipsn-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/ipsn-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Ipsn Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ipsn-experiments -a codex`. Or copy the skill folder (IPSN-Skills/skills/ipsn-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/ipsn-experiments in your project. Codex loads it when a task matches its description.
Can I use Ipsn 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 ipsn-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/ipsn-experiments, .gemini/skills/ipsn-experiments, .github/skills/ipsn-experiments and .opencode/skills/ipsn-experiments in your project.
What does Ipsn Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Ipsn Experiments is instructions for the agent only.
Does Ipsn 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 Ipsn 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 Ipsn Experiments use?
Ipsn 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 Ipsn Experiments use?
About 1.5k tokens (SKILL.md is roughly 5.8k 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 Ipsn Experiments?
Skills that share tags, products or a category with Ipsn 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, 62k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ipsn Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 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.