Topics
ZimoLiao/scholaraio
A skill your agent uses when the user asks about research themes, topic distribution, BERTopic clustering, topic overview, topic papers, topic merges, or HTML topic visualizations.
A skill your agent uses when deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built, measured…
MITAuto-check passed
Install Sensys Topic Selection
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-topic-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-topic-selection --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-topic-selection .claude/skills/sensys-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-topic-selection into .claude/skills/sensys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-topic-selection", 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-topic-selectionType 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-topic-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-topic-selection --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-topic-selection .agents/skills/sensys-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-topic-selection into .agents/skills/sensys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-topic-selection", 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-topic-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-topic-selection --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-topic-selection .cursor/skills/sensys-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-topic-selection into .cursor/skills/sensys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-topic-selection", 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-topic-selection--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-topic-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-topic-selection --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-topic-selection .gemini/skills/sensys-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-topic-selection into .gemini/skills/sensys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-topic-selection", 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-topic-selectionInstalls 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-topic-selection -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-topic-selection .github/skills/sensys-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-topic-selection into .github/skills/sensys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-topic-selection", 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-topic-selection -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-topic-selection --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-topic-selection .opencode/skills/sensys-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-topic-selection into .opencode/skills/sensys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-topic-selection", 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-topic-selection- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 569 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built, measured…
- Works in 4 steps: Is there a system? Can you point to the… → Do embedded constraints bind? Does… → Is the evidence physical? Are the… → …
- Deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built
- SKILL.md covers The fit test, Routing table, Post-merger boundary calls and When two venues both fit, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Sensys Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built, measured sensing/embedded/IoT/on-device-AI system rather than a pure algorithm, a mobile-networking mechanism, or an offline ML result, and routing misfits to MobiCom, MobiSys, or an ML/DSP venue.
Its SKILL.md is about 1.3k 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
- Deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built
- Measured sensing/embedded/IoT/on-device-AI system rather than a pure algorithm
- A mobile-networking mechanism
- An offline ML result
Example prompts
- “/sensys-topic-selection”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Is there a system? Can you point to the artifact — firmware, a node, a protocol, a
- Do embedded constraints bind? Does energy, memory, compute, bandwidth, or intermittent
- Is the evidence physical? Are the headline numbers measured on hardware — current
- Is sensing or embedded computation central? SenSys is about turning **physical signals
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 Topic Selection loads about 1.3k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 569 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). 569 words, ~1,322 tokens.
Download SKILL.mdSave it as
.claude/skills/sensys-topic-selection/SKILL.md (or your agent's skills folder).- name
- sensys-topic-selection
- description
- Use when deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built, measured sensing/embedded/IoT/on-device-AI system rather than a pure algorithm, a mobile-networking mechanism, or an offline ML result, and routing misfits to MobiCom, MobiSys, or an ML/DSP venue.
SenSys Topic Selection
SenSys accepts a system you built and measured under embedded constraints. The 2026 merger of SenSys, IPSN, and IoTDI widened the mandate — low-power networked sensing, embedded systems, IoT, and on-device AI now share one venue — but it did not soften the systems bar: a strong SenSys paper still has a buildable mechanism whose value is shown in energy, latency, memory, and deployment behavior on real hardware, not in a proof or a leaderboard delta.
The fit test
Ask these in order; a "no" is a routing signal, not a verdict on the work's quality.
- Is there a system? Can you point to the artifact — firmware, a node, a protocol, a deployment — that embodies the contribution? A pure algorithm with no embedded realization is a signal-processing or ML contribution.
- Do embedded constraints bind? Does energy, memory, compute, bandwidth, or intermittent power actually shape the design? If the method would run unchanged on a workstation, the constraint is not doing work and SenSys is a weak fit.
- Is the evidence physical? Are the headline numbers measured on hardware — current draw, duty cycle, on-device latency, deployment uptime — or are they simulation/offline accuracy? SenSys reviewers discount simulation-only and single-run results.
- Is sensing or embedded computation central? SenSys is about turning physical signals into information under resource limits, or computing on constrained nodes — not about the wireless link itself (that is MobiCom) or the mobile application platform (that is MobiSys).
Routing table
| If the core contribution is... | The likely home is... | Because |
|---|---|---|
| A built sensing system measured on real hardware for energy/latency/accuracy | SenSys | The merged venue's center of mass |
| Low-power networked sensing / mote-class protocols (formerly IPSN) | SenSys (post-merger) | IPSN's community joined SenSys in 2026 |
| IoT design/implementation, edge deployments (formerly IoTDI) | SenSys (post-merger) | IoTDI's community joined SenSys in 2026 |
| An on-device / TinyML model with measured footprint on an MCU | SenSys | Embedded-AI systems are in-scope after the merger |
| A wireless link, PHY/MAC, or over-the-air networking mechanism | MobiCom | The mechanism is the radio, not the sensing system |
| A mobile-platform / smartphone systems contribution | MobiSys | The platform, not embedded sensing under energy limits |
| A new estimator/algorithm with no embedded realization | an ML or DSP venue | The contribution is the math, not a built system |
| A datacenter/OS/networking systems result | NSDI / OSDI / SIGCOMM | Not sensing/embedded and not energy-bound |
Show full SKILL.md (186 more words)Show less
Post-merger boundary calls
The merger creates new adjacencies to reason about explicitly:
- On-device AI vs. an ML paper. If the novelty is model architecture or accuracy, it is an ML venue. If the novelty is running inference within a real MCU's RAM/flash/energy budget — quantization-for-hardware, intermittent-power inference, on-sensor compute — it is SenSys.
- IoT deployment vs. an applications paper. A deployment is SenSys when the system design or measurement methodology is the contribution, not merely that a known stack was installed.
- Sensing vs. signal processing. A new algorithm belongs at a DSP venue unless it is embodied and measured on the constrained node where it must actually run.
Fit self-check (answer before choosing SenSys):
[ ] There is a concrete built system/artifact, not only a method.
[ ] An embedded constraint (energy/memory/compute/power) shapes the design.
[ ] Headline numbers are measured on real hardware, not simulation-only.
[ ] Sensing or on-node computation — not the radio or the app platform — is central.
[ ] If on-device AI: the claim is measured footprint/latency, not offline accuracy.When two venues both fit
Some work genuinely spans SenSys and MobiCom (e.g. a sensing system that also innovates on its link). Route by where the reviewable novelty lives: if a reviewer would spend most of their judgment on the sensing/embedded system and its energy behavior, submit to SenSys and treat the link as engineering; if the defensible novelty is the wireless mechanism, submit to MobiCom. Pick one — a paper straddling both usually reads as under-contributing to each.
Output format
[Verdict] SenSys fit: strong / plausible / weak
[System] the concrete artifact that embodies the contribution
[Constraint] which embedded limit binds the design (energy/memory/compute/power)
[Evidence] hardware-measured? or simulation/offline (a re-route risk)
[Reroute] if weak — target venue + the one sentence that would move it
[Open] any post-merger boundary ambiguity to resolve before committing© 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-topic-selection of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sensys Topic Selection 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 Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| TopicsZimoLiao/scholaraio | 577 | — | ~294 | Automated safety check: Pass | MIT | |
| Topic Modelingbrycewang-stanford/Auto-Empirical-Research-Skills | 4.6k | — | ~3.7k | Automated safety check: Pass | Custom licence | |
| Bestblogs Topicginobefun/BestBlogs | 4.1k | — | ~670 | Automated safety check: Pass | None | |
| Zsxq Topicitwanger/toBeBetterJavaer | 18k | — | ~564 | Automated safety check: Pass | None | |
| Pubmed Topic Recommendaipoch/medical-research-skills | 1.9k | — | ~1.9k | Automated safety check: Pass | MIT |
Similar skills
Topic Modeling
brycewang-stanford/Auto-Empirical-Research-Skills
Structural topic modeling: STM spec, topic count, coherence-exclusivity.
Bestblogs Topic
ginobefun/BestBlogs
A skill your agent uses when the user asks about a specific topic, subject area, or wants to explore curated topic pages on BestBlogs.
Zsxq Topic
itwanger/toBeBetterJavaer
知识星球主题管理：搜索主题、查看主题详情、发布帖子、编辑主题、发表评论、回复某条评论（楼中楼）、回答提问、删除主题；通过 api call 查看主题评论列表、设置精华、设置标签、查看自己提的问题与已回答记录。当用户需要查找内容、发帖、编辑主题、评论、回复评论、回答问题、删除主题、查看主题评论、查看自己的提问记录、或管理主题精华和标签时使用。
Pubmed Topic Recommend
aipoch/medical-research-skills
Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly.
TiXL UI Topic Doc Writer
tixl3d/tixl
Fills in empty embedded help text for TiXL's UI topics by distilling the maintainer's own video explanations into short, user-facing doc entries.
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Questions about Sensys Topic Selection
What does Sensys Topic Selection do?
A skill your agent uses when deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built, measured…. Sensys Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built, measured sensing/embedded/IoT/on-device-AI system rather than a pure algorithm, a mobile-networking mechanism, or an offline ML result, and routing misfits to MobiCom, MobiSys, or an ML/DSP venue.
When should I use Sensys Topic Selection?
Sensys Topic Selection fits situations like: deciding whether a project belongs at SenSys after the 2026 merger absorbed IPSN and IoTDI — testing whether the contribution is a built; measured sensing/embedded/IoT/on-device-AI system rather than a pure algorithm; A mobile-networking mechanism; an offline ML result.
How do I install Sensys Topic Selection in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-topic-selection -a claude-code`. Or copy the skill folder (SenSys-Skills/skills/sensys-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sensys-topic-selection in your project. Claude Code loads it when a task matches its description.
How do I install Sensys Topic Selection in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-topic-selection -a codex`. Or copy the skill folder (SenSys-Skills/skills/sensys-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sensys-topic-selection in your project. Codex loads it when a task matches its description.
Can I use Sensys Topic Selection 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-topic-selection -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-topic-selection, .gemini/skills/sensys-topic-selection, .github/skills/sensys-topic-selection and .opencode/skills/sensys-topic-selection in your project.
What does Sensys Topic Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Sensys Topic Selection is instructions for the agent only.
Does Sensys Topic Selection 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 Topic Selection 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 Topic Selection use?
Sensys Topic Selection 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 Topic Selection use?
About 1.3k tokens (SKILL.md is roughly 5.3k 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 Topic Selection?
Skills that share tags, products or a category with Sensys Topic Selection: Topics (ZimoLiao/scholaraio, 577 stars), Topic Modeling (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars), Bestblogs Topic (ginobefun/BestBlogs, 4.1k stars) and Zsxq Topic (itwanger/toBeBetterJavaer, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sensys Topic Selection?
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.