Exploratory Data Analysis
spacering-net/codeg
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.
A skill your agent uses when conducting and reporting the analysis of a New Media & Society (NM&S) manuscript across qualitative, content/discourse, computational, and mixed methods — making…
Install Newms Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill newms-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills newms-data-analysis --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/New-Media-and-Society-Skills/skills/newms-data-analysis .claude/skills/newms-data-analysis && 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 "newms-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/New-Media-and-Society-Skills/skills/newms-data-analysis into .claude/skills/newms-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "newms-data-analysis", 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/New-Media-and-Society-Skills/skills/newms-data-analysisType 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 newms-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills newms-data-analysis --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/New-Media-and-Society-Skills/skills/newms-data-analysis .agents/skills/newms-data-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "newms-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/New-Media-and-Society-Skills/skills/newms-data-analysis into .agents/skills/newms-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "newms-data-analysis", 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 newms-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills newms-data-analysis --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/New-Media-and-Society-Skills/skills/newms-data-analysis .cursor/skills/newms-data-analysis && 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 "newms-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/New-Media-and-Society-Skills/skills/newms-data-analysis into .cursor/skills/newms-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "newms-data-analysis", 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 New-Media-and-Society-Skills/skills/newms-data-analysis--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 newms-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills newms-data-analysis --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/New-Media-and-Society-Skills/skills/newms-data-analysis .gemini/skills/newms-data-analysis && 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 "newms-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/New-Media-and-Society-Skills/skills/newms-data-analysis into .gemini/skills/newms-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "newms-data-analysis", 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 newms-data-analysisInstalls 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 newms-data-analysis -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/New-Media-and-Society-Skills/skills/newms-data-analysis .github/skills/newms-data-analysis && 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 "newms-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/New-Media-and-Society-Skills/skills/newms-data-analysis into .github/skills/newms-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "newms-data-analysis", 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 newms-data-analysis -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 newms-data-analysis --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/New-Media-and-Society-Skills/skills/newms-data-analysis .opencode/skills/newms-data-analysis && 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 "newms-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/New-Media-and-Society-Skills/skills/newms-data-analysis into .opencode/skills/newms-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "newms-data-analysis", 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
newms-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 525 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when conducting and reporting the analysis of a New Media & Society (NM&S) manuscript across qualitative, content/discourse, computational, and mixed methods — making…
- Conducting and reporting the analysis of a New Media & Society (NM&S) manuscript across qualitative
- SKILL.md covers When to trigger, Qualitative inference…, Content / discourse analysis and Computational analysis, plus 8 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Content/discourse
What it does
Newms Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when conducting and reporting the analysis of a New Media & Society (NM&S) manuscript across qualitative, content/discourse, computational, and mixed methods — making inference transparent and defensible on each tradition's own terms. Strengthens analysis and reporting; it does not collect data.
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 Data & Analytics, covering Data analysis. 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
- Conducting and reporting the analysis of a New Media & Society (NM&S) manuscript across qualitative
- Content/discourse
- Mixed methods — making inference transparent and defensible on each traditions own terms
Example prompts
- “/newms-data-analysis”
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
Newms Data Analysis loads about 1.4k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 525 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). 525 words, ~1,396 tokens.
Download SKILL.mdSave it as
.claude/skills/newms-data-analysis/SKILL.md (or your agent's skills folder).- name
- newms-data-analysis
- description
- Use when conducting and reporting the analysis of a New Media & Society (NM&S) manuscript across qualitative, content/discourse, computational, and mixed methods — making inference transparent and defensible on each tradition's own terms. Strengthens analysis and reporting; it does not collect data.
Data & Analysis (newms-data-analysis)
NM&S spans interpretive, content-analytic, and computational analysis under one interdisciplinary roof.
The standard is the same across them: the analysis must be transparent, credible to a reader from
another tradition, and matched to what the evidence can support. This skill is about inference and
reporting, not study design (newms-research-design).
When to trigger
- Moving from collected data to claims, themes, measures, or results
- A reviewer asked for reliability, robustness, validation, or a clearer analytic trail
- You need to report uncertainty or limits honestly for a cross-method audience
Qualitative inference (interviews / ethnography)
- Analytic transparency: show the path from data to claim — coding/memoing process, how themes were built, and how many informants/instances support each theme (avoid "many participants felt…").
- Negative cases and disconfirmation: report instances that cut against the reading and how they were handled — the strongest signal of credible qualitative work.
- Quote-to-claim discipline: each claim is anchored to specific evidence, not an isolated vivid quote.
Content / discourse analysis
- Quantitative content analysis: report intercoder reliability with the right statistic (Krippendorff's alpha preferred for most designs), the unit of analysis, and how disagreements were resolved; report category distributions with uncertainty, not just counts.
- Interpretive discourse analysis: make the interpretive logic auditable — what features of the text warrant the reading, and what an alternative reading would require.
Computational analysis
- Validation first: report agreement between automated measures and human labels (precision/recall, F1, agreement) before interpreting model output as a finding.
- Robustness: sensitivity to preprocessing, model/hyperparameter choices, time window, and platform; show the result is not an artifact of one pipeline.
- Inference and uncertainty: report confidence/credible intervals; respect non-random API sampling; do not over-claim causality from observational trace data.
Inference honesty (all methods)
State plainly what the analysis establishes — description, association, interpretation, or (rarely) causation — and do not let verbs outrun the design. A cross-method NM&S panel reads candor as strength.
Show full SKILL.md (218 more words)Show less
Robustness & reliability checklist by method
| Method | Minimum credibility move | Common referee ask |
|---|---|---|
| Interviews / ethnography | analytic trail + negative cases | "How representative are these quotes?" |
| Quant content analysis | intercoder reliability (alpha) + unit defined | "What's your reliability?" |
| Discourse analysis | auditable interpretive warrant | "Why this reading not another?" |
| Computational | human-label validation + robustness sweep | "Did you validate the classifier?" |
Worked micro-example (illustrative)
Computational: a classifier labels courier posts as "compliance" vs. "contestation."
Validation: 500 hand-coded posts → F1 = 0.84 reported before any substantive claim.
Robustness: result holds across two embeddings + two time windows; stated explicitly.
Inference framing: "posts shift toward compliance after a ranking change" = association, not proof of
internalization; the qualitative strand supplies the mechanism (triangulation, per mixed design).Referee pushback → NM&S-specific fix
- "How do I know the qualitative themes aren't cherry-picked?" → Supply the analytic trail, theme prevalence, and negative cases.
- "Your classifier is a black box." → Add human-label validation metrics and a robustness sweep.
- "You imply causation from observational traces." → Downgrade the verbs; report as association and say so.
Calibration anchors
- Validate before you interpret. Computational output is not a finding until it is checked against human labels.
- Report what cuts against you. Negative cases and robustness checks build more trust than a clean story.
- Match verbs to design. Description, association, interpretation, causation — name which one, and stop there.
Anti-patterns
- "Participants said…" with no count, trail, or negative cases
- Content analysis with no reliability statistic or undefined unit of analysis
- Computational results with no validation against human labels
- Robustness checks omitted, leaving the result as a single-pipeline artifact
- Causal language on observational, non-random trace data
Output format
【Method】qualitative / content-discourse / computational / mixed
【Inference type】description / association / interpretation / causation
【Credibility move】analytic trail / reliability stat / human-label validation
【Robustness】sensitivity checks / negative cases reported? [Y/N]
【Next】newms-tables-figuresSupplementary resources
../../resources/external_tools.md— reliability, content-analysis, and computational packages../../resources/official-source-map.md— NM&S methodological breadth
© 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 New-Media-and-Society-Skills/skills/newms-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Newms Data Analysis 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 |
|---|---|---|---|---|---|---|
| Newms Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Exploratory Data Analysisspacering-net/codeg | 3.8k | 15 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Excel and CSV Data Analysisbytedance/deer-flow | 83k | 4 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Exploratory Data AnalysisOleafly/Oleafly | 206 | 3 repos | ~3.4k | Automated safety check: Notes | MIT | |
| Python Executorcortega26/chile-hub | 113 | 2 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill | 188 | — | ~4k | Automated safety check: Pass | MIT |
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Questions about Newms Data Analysis
What does Newms Data Analysis do?
A skill your agent uses when conducting and reporting the analysis of a New Media & Society (NM&S) manuscript across qualitative, content/discourse, computational, and mixed methods — making…. Newms Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when conducting and reporting the analysis of a New Media & Society (NM&S) manuscript across qualitative, content/discourse, computational, and mixed methods — making inference transparent and defensible on each tradition's own terms.
When should I use Newms Data Analysis?
Newms Data Analysis fits situations like: conducting and reporting the analysis of a New Media & Society (NM&S) manuscript across qualitative; content/discourse; mixed methods — making inference transparent and defensible on each traditions own terms.
How do I install Newms Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill newms-data-analysis -a claude-code`. Or copy the skill folder (New-Media-and-Society-Skills/skills/newms-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/newms-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Newms Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill newms-data-analysis -a codex`. Or copy the skill folder (New-Media-and-Society-Skills/skills/newms-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/newms-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Newms Data Analysis 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 newms-data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/newms-data-analysis, .gemini/skills/newms-data-analysis, .github/skills/newms-data-analysis and .opencode/skills/newms-data-analysis in your project.
What does Newms Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Newms Data Analysis is instructions for the agent only.
Does Newms Data Analysis 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 Newms Data Analysis 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 Newms Data Analysis use?
Newms Data Analysis 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 Newms Data Analysis use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Newms Data Analysis?
Skills that share tags, products or a category with Newms Data Analysis: Exploratory Data Analysis (spacering-net/codeg, 3.8k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 83k stars), Exploratory Data Analysis (Oleafly/Oleafly, 206 stars) and Python Executor (cortega26/chile-hub, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Newms Data Analysis?
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.