Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when making a SenSys result reproducible across a different testbed — capturing energy-measurement method, hardware and firmware provenance, sensor ground-truth protocol, and…
Install Sensys Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-reproducibility --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-reproducibility .claude/skills/sensys-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-reproducibility into .claude/skills/sensys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-reproducibility", 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-reproducibilityType 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-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-reproducibility --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-reproducibility .agents/skills/sensys-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-reproducibility into .agents/skills/sensys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-reproducibility", 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-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-reproducibility --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-reproducibility .cursor/skills/sensys-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-reproducibility into .cursor/skills/sensys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-reproducibility", 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-reproducibility--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-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-reproducibility --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-reproducibility .gemini/skills/sensys-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-reproducibility into .gemini/skills/sensys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-reproducibility", 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-reproducibilityInstalls 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-reproducibility -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-reproducibility .github/skills/sensys-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-reproducibility into .github/skills/sensys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-reproducibility", 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-reproducibility -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-reproducibility --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-reproducibility .opencode/skills/sensys-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-reproducibility into .opencode/skills/sensys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-reproducibility", 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-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 379 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when making a SenSys result reproducible across a different testbed — capturing energy-measurement method, hardware and firmware provenance, sensor ground-truth protocol, and…
- Making a SenSys result reproducible across a different testbed — capturing energy-measurement method
- SKILL.md covers Capture it live, not later, The reproducibility that…, Decide early what can ship and Reproducibility checklist, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Hardware and firmware provenance
What it does
Sensys Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making a SenSys result reproducible across a different testbed — capturing energy-measurement method, hardware and firmware provenance, sensor ground-truth protocol, and deployment conditions while the testbed is still live, and deciding early which traces and firmware can legally and safely ship.
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.
It sits in Research & Science, covering Reproducible research and 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
- Making a SenSys result reproducible across a different testbed — capturing energy-measurement method
- Hardware and firmware provenance
- Sensor ground-truth protocol
- Deployment conditions while the testbed is still live
Example prompts
- “/sensys-reproducibility”
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 Reproducibility loads about 1.2k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 379 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). 379 words, ~1,172 tokens.
Download SKILL.mdSave it as
.claude/skills/sensys-reproducibility/SKILL.md (or your agent's skills folder).- name
- sensys-reproducibility
- description
- Use when making a SenSys result reproducible across a different testbed — capturing energy-measurement method, hardware and firmware provenance, sensor ground-truth protocol, and deployment conditions while the testbed is still live, and deciding early which traces and firmware can legally and safely ship.
SenSys Reproducibility
A SenSys result is reproducible when someone on different hardware can understand — and, from your traces, re-obtain — your numbers. The threat is not messy code; it is provenance that evaporates when the deployment ends. Energy measured without its method, accuracy scored against forgotten ground truth, and firmware whose toolchain is unrecorded cannot be reproduced at any price once the testbed is torn down. Capture it while the nodes are still powered.
Capture it live, not later
| Provenance | Capture while live | Why it cannot be reconstructed |
|---|---|---|
| Energy method | Instrument model, sampling rate, integration boundaries, sleep floor | A stored current number loses its method; "42 µA" of what phases? |
| Hardware | MCU/SoC part + clock, sensor config, radio + TX power, board revision, battery/harvester spec | A later "same board" is rarely bit-identical |
| Firmware | Sources + toolchain version + compiler flags, pinned | A rebuild on a new toolchain shifts timing and energy |
| Ground truth | Reference protocol + the reference's own error | Labels without their protocol are unfalsifiable |
| Deployment | Placement, environment, duration, node uptime/failures | Conditions are gone once the deployment ends |
| Harvest input | Energy-source trace (light/RF/vibration), buffer sizing | Behavior across brownouts is unreproducible without the input |
Show full SKILL.md (191 more words)Show less
The reproducibility that matters is cross-hardware
Reproducing your own result on your own testbed proves little. The SenSys bar is that a
different lab can interpret a mismatch: when their number differs from yours, the provenance
tells them why (different MCU clock, different harvest input) rather than leaving them to guess.
Ship recorded traces so the analysis can be re-run even by someone who lacks your hardware —
this is what turns a deployment result into a reproducible one and underpins the trace-replay
path in sensys-artifact-evaluation.
Minimum reproducible bundle for a SenSys figure:
traces/fig4_power.csv # raw power samples + timestamps
traces/fig4_sensor.csv # the sensor stream behind the same figure
analysis/reproduce_fig4.py # regenerates Fig. 4 from the two traces
ENERGY.md # instrument, rate, integration boundaries
HARDWARE.md # part numbers, clocks, board rev, harvester spec
firmware/ + toolchain.lock # pinned build that produced the tracesDecide early what can ship
Some SenSys artifacts carry release constraints that a purely computational paper never faces:
- Sensor data of people or spaces may need consent, anonymization, or aggregation before it can be released — decide at collection time, because retroactive consent is usually impossible.
- Firmware and hardware designs may touch third-party IP or a vendor NDA; confirm what is releasable before promising an Available badge.
- Deployment locations can be sensitive (critical infrastructure, private property); scrub identifying detail from released traces.
Resolving these late forces a choice between a weak artifact and a broken promise. Resolve them in
the experiment plan (sensys-experiments).
Reproducibility checklist
[ ] Energy method (instrument, rate, boundaries) recorded, not just the number.
[ ] Hardware provenance: parts, clocks, board revision, battery/harvester spec.
[ ] Firmware sources + pinned toolchain + compiler flags archived.
[ ] Ground-truth protocol and the reference's own error documented.
[ ] Deployment conditions + honest uptime/failure log captured while live.
[ ] Harvest-input traces + buffer sizing stored for batteryless results.
[ ] Recorded traces shipped so figures re-generate without your hardware.
[ ] Release constraints (consent, NDA, location) resolved at collection time.Output format
[Live-capture] which provenance is captured vs. still at risk while the testbed runs
[Cross-HW] can a different lab interpret a mismatch from what you shipped? Y/N
[Traces] do shipped traces regenerate the headline figures without hardware? Y/N
[Release] consent/NDA/location constraints resolved? open items
[Open] the provenance whose loss would most damage reproducibility© 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-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sensys Reproducibility 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 Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
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CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
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Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Categories
Questions about Sensys Reproducibility
What does Sensys Reproducibility do?
A skill your agent uses when making a SenSys result reproducible across a different testbed — capturing energy-measurement method, hardware and firmware provenance, sensor ground-truth protocol, and…. Sensys Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making a SenSys result reproducible across a different testbed — capturing energy-measurement method, hardware and firmware provenance, sensor ground-truth protocol, and deployment conditions while the testbed is still live, and deciding early which traces and firmware can legally and safely ship.
When should I use Sensys Reproducibility?
Sensys Reproducibility fits situations like: making a SenSys result reproducible across a different testbed — capturing energy-measurement method; hardware and firmware provenance; sensor ground-truth protocol; deployment conditions while the testbed is still live.
How do I install Sensys Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-reproducibility -a claude-code`. Or copy the skill folder (SenSys-Skills/skills/sensys-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sensys-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Sensys Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-reproducibility -a codex`. Or copy the skill folder (SenSys-Skills/skills/sensys-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sensys-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Sensys Reproducibility 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-reproducibility -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-reproducibility, .gemini/skills/sensys-reproducibility, .github/skills/sensys-reproducibility and .opencode/skills/sensys-reproducibility in your project.
What does Sensys Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Sensys Reproducibility is instructions for the agent only.
Does Sensys Reproducibility 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 Reproducibility 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 Reproducibility use?
Sensys Reproducibility 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 Reproducibility use?
About 1.2k tokens (SKILL.md is roughly 4.7k 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 Reproducibility?
Skills that share tags, products or a category with Sensys Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sensys Reproducibility?
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.