Joe Data Analysis
franklee16/academic-research-skills
A skill your agent uses when designing the Monte Carlo study and empirical illustration that demonstrate a Journal of Econometrics (JoE) method works in finite samples.
A skill your agent uses when the estimation, identification, construct validity, or modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on…
Install Jmis Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmis-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jmis-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/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis .claude/skills/jmis-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 "jmis-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis into .claude/skills/jmis-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jmis-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/Journal-of-Management-Information-Systems-Skills/skills/jmis-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 jmis-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jmis-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/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis .agents/skills/jmis-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 "jmis-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis into .agents/skills/jmis-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jmis-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 jmis-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jmis-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/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis .cursor/skills/jmis-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 "jmis-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis into .cursor/skills/jmis-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jmis-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 Journal-of-Management-Information-Systems-Skills/skills/jmis-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 jmis-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jmis-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/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis .gemini/skills/jmis-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 "jmis-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis into .gemini/skills/jmis-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jmis-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 jmis-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 jmis-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/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis .github/skills/jmis-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 "jmis-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis into .github/skills/jmis-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jmis-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 jmis-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 jmis-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/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis .opencode/skills/jmis-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 "jmis-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis into .opencode/skills/jmis-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jmis-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
jmis-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~2.5k tokens
- SKILL.md length
- 1,161 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when the estimation, identification, construct validity, or modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on…
- Construct validity
- SKILL.md covers When to trigger, Run the analysis to the JMIS…, Report effects so a manager… and Worked vignette: making…, plus 8 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on firm/platform data
What it does
Jmis Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the estimation, identification, construct validity, or modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on firm/platform data, SEM/PLS for behavioral constructs, analytical-model derivations, or ML/analytics evaluation. Executes and stress-tests the analysis the design (jmis-methods) chose; it does not redesign the study.
Its SKILL.md is about 2.5k 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 Research & Science, covering Econometrics and empirical research, Data analysis and Load testing. 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
- Construct validity
- Modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on firm/platform data
- SEM/PLS for behavioral constructs
- Analytical-model derivations
Example prompts
- “/jmis-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
Jmis Data Analysis loads about 2.5k tokens when it runs. Until then it costs about 103 tokens; SKILL.md has 1,161 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). 1,161 words, ~2,457 tokens.
Download SKILL.mdSave it as
.claude/skills/jmis-data-analysis/SKILL.md (or your agent's skills folder).- name
- jmis-data-analysis
- description
- Use when the estimation, identification, construct validity, or modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on firm/platform data, SEM/PLS for behavioral constructs, analytical-model derivations, or ML/analytics evaluation. Executes and stress-tests the analysis the design (jmis-methods) chose; it does not redesign the study.
Data Analysis (jmis-data-analysis)
When to trigger
- A regression "works" but the identifying variation and threats are not pinned down
- A survey model is run but reliability, validity, and common-method bias are not established
- An ML/analytics artifact reports accuracy but no credible-baseline comparison or robustness
- A reviewer says results are "not robust," "not identified," or "could be reverse causality"
Run the analysis to the JMIS standard for your evidence type
JMIS reviewers are method-literate across econometrics, psychometrics, analytical modeling, and data science. The bar is that the analysis credibly supports the verb in your claim.
Empirical IT-value / platform econometrics
- Identification, made visible. Show the source of variation (shock/rollout/breach), not just the regression. With staggered timing, use Callaway–Sant'Anna / Sun–Abraham / de Chaisemartin–D'Haultfœuille and report a clean event study with leads; for naive TWFE, run a Goodman-Bacon decomposition to expose contamination.
- Endogeneity head-on. For IT investment and platform participation, address reverse causality and selection: IV with a defended exclusion restriction and a strong first stage (weak-IV-robust inference if not), or a credible quasi-experiment; do not lean on "we control for X."
- Inference. Cluster at the assignment/treatment level; handle few clusters (wild-cluster bootstrap); report robustness to specification, sample, and functional form.
Behavioral IS (survey / experiment)
- Measurement first. Report reliability (composite reliability / α), convergent and discriminant validity (AVE, HTMT), and confirm the measurement model before the structural model.
- Common-method bias. Go beyond Harman's single-factor test — use a marker/measured latent-method variable and report whether structural paths survive.
- Structural model. SEM (CB-SEM) or PLS as the theory and sample justify; for moderation/mediation use proper interaction or indirect-effect tests (bootstrapped indirect effects), not a chain of separate regressions.
Analytical / economic models
- State assumptions; prove the propositions; present comparative statics as the result. Sketch proofs in the body, full proofs in an appendix. Show the insight is not an artifact of a knife-edge assumption (robustness to functional form / parameter ranges).
Design-science / ML artifacts
- Benchmark against credible baselines (strong incumbents, not strawmen); report appropriate metrics with variance/uncertainty; validate out-of-sample; tie performance back to the managerial decision the artifact serves.
Report effects so a manager can read them
JMIS values managerial relevance: report economic magnitude (elasticities, marginal effects, dollar value, lift), not only significance. Translate the headline coefficient into what it means for a firm, platform, or decision.
Worked vignette: making robustness answer a threat (illustrative)
A referee writes "the result is not robust." A weak reply adds ten specifications and reports they are "all significant." A JMIS-grade reply names the threat each check defends against: a placebo on a period before the platform change rules out a secular trend; an alternative control group rules out a coincident shock; a sensitivity analysis (e.g., Oster-style bounds on selection) shows the estimate survives plausible unobserved confounding; an alternative measure of the construct rules out operationalization artifacts. Robustness is not a quantity of regressions; it is a mapping from each surviving threat to the check that kills it.
Translate the headline into a managerial number
Suppose the main coefficient implies the ranking redesign cut marginal-seller retention by 6 percentage points. State it that way, then carry it to the platform decision: at the observed seller base that is roughly N exits per quarter and a Y% variety reduction (illustrative). A JMIS reader wants the economic magnitude and its managerial reading, not just p < 0.01.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JMIS is empirical IS — survey-based SEM and econometric panels; the chain below serves causal / quasi-experimental designs and many-outcome corrections.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.
Show full SKILL.md (502 more words)Show less
Checklist
- Empirical: identifying variation named; modern estimator where TWFE would bias; endogeneity addressed, not just controlled
- Behavioral: reliability + convergent/discriminant validity reported; CMB tested beyond Harman's
- Mediation/moderation tested with proper indirect-effect / interaction methods
- Analytical: assumptions stated, propositions proven, robustness to functional form shown
- ML: credible baselines, uncertainty reported, out-of-sample validation
- Inference: clustering correct; robustness to spec/sample shown
- Magnitudes, not just significance, are reported and interpreted for managers
- Reverse causality/selection pre-empted (timing, within-unit, sensitivity bounds)
- Body is self-contained; sample construction, estimator, and inference are traceable
Pre-empt the reverse-causality reflex
On IT-value and platform papers, the most common first-round attack is "this could be reverse causality or selection." Do not wait for it — pre-empt it in the analysis. Show the timing (the cause precedes the effect), use within-unit variation that differences out fixed selection, lean on a quasi-experimental shock where you have one, and where you must use an instrument, defend the exclusion restriction on institutional grounds and report weak-IV-robust inference if the first stage is not strong. Then bound what remains: a selection-sensitivity analysis (e.g., Oster-style δ/bounds) tells a referee how much unobserved confounding it would take to overturn the result. Anticipating the endogeneity objection inside the paper is worth more than answering it in a rebuttal.
Anti-patterns
- "The coefficient is significant" presented as if it were identification
- Endogenous IT/platform variables left to control variables
- Reporting only Harman's single-factor test as the CMB defense
- Discriminant validity skipped; constructs that empirically overlap treated as distinct
- ML accuracy against strawman baselines, or with no out-of-sample check
- Significance stars with no economic magnitude or managerial interpretation
- Waiting for the reverse-causality objection instead of pre-empting it with timing/within-unit/sensitivity evidence
- A robustness section that adds specifications without naming the threat each one defends against
- A load-bearing analysis that exists only in the online appendix, not the body
Keep the analysis reproducible and self-contained
JMIS submissions are double-anonymized and capped at 50 pages, which shapes how you present analysis. Online appendixes are permitted, but the body must stand on its own: a reviewer should be able to follow the identification and the headline result without the appendix, and should never find that a load-bearing robustness check exists only there. Document the sample construction, the estimator, the software/version, and the inference choices clearly enough that the result is traceable; if you reference your own prior code or data, phrase it so it does not de-anonymize you. Tighten the analysis narrative — full diagnostic batteries and secondary specifications belong in the appendix, while the body carries the chain that establishes the contribution.
Hand a stable headline to the next stage
Do not move to jmis-contribution-framing while the headline number is still drifting across specifications. The contribution sentence and the exhibits both depend on a settled effect size and a settled inference, so lock the preferred specification, confirm it survives the robustness battery, and fix the economic-magnitude interpretation before framing the claim. A contribution built on a coefficient that later moves forces a rewrite of the intro, the discussion, and the abstract.
Output format
【Evidence type】firm/platform econometrics / survey-SEM / experiment / analytical / ML
【Identification or validity】variation source + diagnostics / measurement model + CMB test / proof + robustness
【Inference】clustering / weak-IV-robust / bootstrap as applicable
【Magnitude】economic/managerial interpretation of the headline effect
【Robustness done】spec / sample / functional-form / out-of-sample
【Next step】jmis-contribution-framing© 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 Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Jmis 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 |
|---|---|---|---|---|---|---|
| Jmis Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~2.5k | Automated safety check: Pass | MIT | |
| Joe Data Analysisfranklee16/academic-research-skills | 223 | 1 repos | ~933 | Automated safety check: Pass | None | |
| Ectheory Data Analysisfranklee16/academic-research-skills | 223 | 1 repos | ~905 | Automated safety check: Pass | None | |
| Mgsci Methodsfranklee16/academic-research-skills | 223 | 1 repos | ~1.1k | Automated safety check: Pass | None | |
| Mksc Methodsfranklee16/academic-research-skills | 223 | 1 repos | ~1.1k | Automated safety check: Pass | None | |
| Mksc Theory Developmentfranklee16/academic-research-skills | 223 | 1 repos | ~932 | Automated safety check: Pass | None |
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Questions about Jmis Data Analysis
What does Jmis Data Analysis do?
A skill your agent uses when the estimation, identification, construct validity, or modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on…. Jmis Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the estimation, identification, construct validity, or modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on firm/platform data, SEM/PLS for behavioral constructs, analytical-model derivations, or ML/analytics evaluation.
When should I use Jmis Data Analysis?
Jmis Data Analysis fits situations like: construct validity; modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on firm/platform data; SEM/PLS for behavioral constructs; analytical-model derivations.
How do I install Jmis Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmis-data-analysis -a claude-code`. Or copy the skill folder (Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jmis-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Jmis Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmis-data-analysis -a codex`. Or copy the skill folder (Journal-of-Management-Information-Systems-Skills/skills/jmis-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jmis-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Jmis 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 jmis-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/jmis-data-analysis, .gemini/skills/jmis-data-analysis, .github/skills/jmis-data-analysis and .opencode/skills/jmis-data-analysis in your project.
What does Jmis Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Jmis Data Analysis is instructions for the agent only.
Does Jmis 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 Jmis 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 Jmis Data Analysis use?
Jmis 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 Jmis Data Analysis use?
About 2.5k tokens (SKILL.md is roughly 9.8k 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 Jmis Data Analysis?
Skills that share tags, products or a category with Jmis Data Analysis: Joe Data Analysis (franklee16/academic-research-skills, 223 stars), Ectheory Data Analysis (franklee16/academic-research-skills, 223 stars), Mgsci Methods (franklee16/academic-research-skills, 223 stars) and Mksc Methods (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Jmis 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.