Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
A skill your agent uses when a Journal of Urban Economics (JUE) manuscript's headline spatial estimate must be shown to survive specification, spatial-scale, sorting, spillover, and inference…
Install Jue Robustness
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jue-robustness -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jue-robustness --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-Urban-Economics-Skills/skills/jue-robustness .claude/skills/jue-robustness && 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 "jue-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Urban-Economics-Skills/skills/jue-robustness into .claude/skills/jue-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jue-robustness", 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-Urban-Economics-Skills/skills/jue-robustnessType 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 jue-robustness -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jue-robustness --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-Urban-Economics-Skills/skills/jue-robustness .agents/skills/jue-robustness && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "jue-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Urban-Economics-Skills/skills/jue-robustness into .agents/skills/jue-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jue-robustness", 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 jue-robustness -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jue-robustness --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-Urban-Economics-Skills/skills/jue-robustness .cursor/skills/jue-robustness && 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 "jue-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Urban-Economics-Skills/skills/jue-robustness into .cursor/skills/jue-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jue-robustness", 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-Urban-Economics-Skills/skills/jue-robustness--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 jue-robustness -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jue-robustness --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-Urban-Economics-Skills/skills/jue-robustness .gemini/skills/jue-robustness && 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 "jue-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Urban-Economics-Skills/skills/jue-robustness into .gemini/skills/jue-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jue-robustness", 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 jue-robustnessInstalls 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 jue-robustness -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-Urban-Economics-Skills/skills/jue-robustness .github/skills/jue-robustness && 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 "jue-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Urban-Economics-Skills/skills/jue-robustness into .github/skills/jue-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jue-robustness", 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 jue-robustness -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 jue-robustness --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-Urban-Economics-Skills/skills/jue-robustness .opencode/skills/jue-robustness && 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 "jue-robustness" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Urban-Economics-Skills/skills/jue-robustness into .opencode/skills/jue-robustness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jue-robustness", 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
jue-robustness- GitHub stars
- 1.2k
- Token cost
- ~2.2k tokens
- SKILL.md length
- 1,020 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when a Journal of Urban Economics (JUE) manuscript's headline spatial estimate must be shown to survive specification, spatial-scale, sorting, spillover, and inference…
- Works in 5 steps: Lock the primary spatial specification… → One spatial threat → one check. Each… → Show stability of the point estimate,… → …
- A Journal of Urban Economics (JUE) manuscripts headline spatial estimate must be shown to survive specification
- SKILL.md covers When to trigger, The JUE robustness bar, Robustness craft and Execution bridge (StatsPAI /…, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Jue Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when a Journal of Urban Economics (JUE) manuscript's headline spatial estimate must be shown to survive specification, spatial-scale, sorting, spillover, and inference choices before submission or in an R&R. Builds the spatially-aware robustness suite a JUE referee expects; it does not establish the identification (jue-identification) or format the maps (jue-tables-figures).
Its SKILL.md is about 2.2k 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. 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
- A Journal of Urban Economics (JUE) manuscripts headline spatial estimate must be shown to survive specification
- Inference choices before submission
Example prompts
- “/jue-robustness”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Lock the primary spatial specification first — the scale, FE geography, and bandwidth you prefer — then perturb around it. Do not present…
- One spatial threat → one check. Each robustness exhibit reads "here is the worry (MAUP / spillover / sorting / spatial SEs), here is the…
- Show stability of the point estimate, not just that significance survives — across scales, radii, and FE geographies the coefficient…
- Stress-test inference for spatial dependence. Report Conley SEs at several distance cutoffs; wrong (non-spatial) SEs are the most common…
- Report honestly where it weakens. A check that shifts the estimate is information — bound the implication rather than hiding the…
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
Jue Robustness loads about 2.2k tokens when it runs. Until then it costs about 99 tokens; SKILL.md has 1,020 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). 1,020 words, ~2,156 tokens.
Download SKILL.mdSave it as
.claude/skills/jue-robustness/SKILL.md (or your agent's skills folder).- name
- jue-robustness
- description
- Use when a Journal of Urban Economics (JUE) manuscript's headline spatial estimate must be shown to survive specification, spatial-scale, sorting, spillover, and inference choices before submission or in an R&R. Builds the spatially-aware robustness suite a JUE referee expects; it does not establish the identification (jue-identification) or format the maps (jue-tables-figures).
Spatial Robustness Suite (jue-robustness)
When to trigger
- The main spatial estimate is in hand and must be shown not to be an artifact of one specification
- A referee asks "is this robust to the spatial scale / the buffer / the geography you chose?"
- The result depends on a bandwidth, a ring radius, a fixed-effects geography, or a clustering choice
- You need to rule out that spatial sorting or MAUP (modifiable areal unit problem) drives the result
- The estimate could move under a spatial-spillover or boundary-definition change
The JUE robustness bar
JUE referees probe whether the spatial estimate is stable across the spatial choices the researcher made — the scale of the units, the boundaries, the buffer/ring radii, the fixed-effects geography — and whether inference accounts for spatial dependence. Robustness here is not a wall of regressions; it is a targeted set of checks, each tied to a spatial threat, reported so the reader sees the point estimate barely moves.
| Spatial threat to the result | The check that answers it |
|---|---|
| Modifiable areal unit problem (MAUP) | re-estimate at multiple spatial scales (tract / block-group / zip); show the estimate is scale-stable |
| Boundary/buffer arbitrariness | vary ring radii and donut widths; show insensitivity to the cut |
| Spatial sorting / selection | balance on pre-period composition; control for or model sorting; placebo on pre-trends |
| Spillovers / SUTVA | estimate the spillover ring; show controls outside the spillover zone give the same answer |
| Spatial autocorrelation in inference | Conley SEs across distance cutoffs; spatial cluster vs naive |
| Geographic confounders | add finer geographic fixed effects (commuting zone, grid cell) and show stability |
| Omitted local trends | region-specific linear trends; pre-trend leads flat |
| Specification search | a specification curve over scale × FE × bandwidth; declare the primary spec |
Robustness craft
- Lock the primary spatial specification first — the scale, FE geography, and bandwidth you prefer — then perturb around it. Do not present five co-equal spatial specs.
- One spatial threat → one check. Each robustness exhibit reads "here is the worry (MAUP / spillover / sorting / spatial SEs), here is the evidence it is not the story."
- Show stability of the point estimate, not just that significance survives — across scales, radii, and FE geographies the coefficient should barely move.
- Stress-test inference for spatial dependence. Report Conley SEs at several distance cutoffs; wrong (non-spatial) SEs are the most common JUE robustness failure.
- Report honestly where it weakens. A check that shifts the estimate is information — bound the implication rather than hiding the specification.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JUE is urban/spatial economics — sorting and spatial dependence; identification + Conley/spatial-robust inference.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists missing checks + the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Checklist
- Primary spatial spec declared (scale, FE geography, bandwidth) before perturbations
- MAUP addressed: estimate stable across at least two spatial scales
- Boundary/ring choices varied; result insensitive to radius and donut width
- Sorting/selection check (pre-period balance / placebo pre-trends)
- Spillover ring estimated; controls outside the spillover zone give the same answer
- Conley/spatial-cluster SEs reported across distance cutoffs, vs naive
- Finer geographic FE / local trends show the point estimate barely moves
- A spatial placebo (fake boundary / pre-period / unaffected outcome) is shown to be null
- For a QSM, counterfactual sensitivity to the least-identified elasticity is reported
- Any check that moves the estimate is reported and bounded honestly
Show full SKILL.md (437 more words)Show less
Anti-patterns
- A 20-column robustness table with no map from check to spatial threat (kitchen-sink robustness)
- Reporting one spatial scale only, leaving MAUP unaddressed
- Naive standard errors on geographically clustered data, then claiming robustness
- Hand-picking the ring radius or bandwidth that maximizes significance
- Reporting that significance survives while the point estimate wanders across scales
- Hiding the FE geography or boundary definition that breaks the result
Referee pushback mapped to the robustness fix
- "This is an artifact of your spatial scale." → Re-estimate at tract / block-group / commuting-zone; show the coefficient is scale-stable (MAUP not driving it).
- "Your boundary/ring radius is arbitrary." → Vary radii and donut widths; show insensitivity across the grid of cuts.
- "Did you cluster for spatial dependence?" → Conley SEs at several distance cutoffs (and a spatial-cluster alternative), contrasted with naive SEs.
- "This is specification search." → Declare the primary spatial spec; show a specification curve over scale × FE × bandwidth in which the point estimate barely moves.
Robustness for a structural / QSM paper
When the JUE paper is a quantitative spatial model, robustness shifts from specification perturbations to parameter sensitivity and counterfactual stability. Report how the headline counterfactual moves as the least-identified elasticities (migration, commuting, agglomeration) are varied across plausible ranges from the literature; show that the qualitative conclusion and the order of magnitude survive. A counterfactual that is fragile to one elasticity is a finding to bound and disclose, not to bury — the same honesty norm as reduced-form stability.
Worked vignette (illustrative)
A density-wage elasticity is 0.045 (Conley s.e. 0.012). The spatial robustness suite: (i) re-estimated at tract, block-group, and commuting-zone scale, the elasticity stays in [0.041, 0.049] — MAUP is not driving it; (ii) Conley SEs at 50/100/200 km cutoffs keep the CI away from zero; (iii) adding commuting-zone fixed effects moves it to 0.043; (iv) a placebo on pre-period wage growth is flat, arguing against sorting on trends; (v) the spillover specification shows neighboring-area contamination is small. The point estimate barely moves — the JUE target.
Spatial placebo and falsification
Beyond perturbing the main spec, the most persuasive JUE robustness evidence is a placebo that should show nothing and does. Useful spatial placebos: assign the treatment to a pre-period and show no effect (rules out pre-trends/sorting on trends); apply the design to an outcome that the mechanism should not move (rules out a generic local shock); shift the boundary or corridor to a fake location and show the discontinuity vanishes. A clean placebo is often worth more to a referee than another robustness column, because it tests the design rather than re-running it — and a placebo that unexpectedly does fire is critical information to report, not suppress.
Output format
【Primary spatial spec】scale / FE geography / bandwidth — estimate: ___ (Conley s.e. ___)
【MAUP】scales tested: ___ ; range: [___, ___]
【Boundary/ring】radii/donut varied? result stable? [Y/N]
【Sorting check】pre-period balance / placebo pre-trend: ___
【Spillover】ring estimated; controls-outside-zone consistent? [Y/N]
【Spatial inference】Conley cutoffs: ___ ; vs naive: ___
【Estimate stability】range across checks: [___, ___]; checks that move it: ___
【Next skill】jue-tables-figures© 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-Urban-Economics-Skills/skills/jue-robustness of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Jue Robustness 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 |
|---|---|---|---|---|---|---|
| Jue Robustness this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~2.2k | Automated safety check: Pass | MIT | |
| Hypothesis Generationspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Last30daysmvanhorn/last30days-skill | 64k | — | ~7.9k | Automated safety check: Notes | MIT |
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Categories
Questions about Jue Robustness
What does Jue Robustness do?
A skill your agent uses when a Journal of Urban Economics (JUE) manuscript's headline spatial estimate must be shown to survive specification, spatial-scale, sorting, spillover, and inference…. Jue Robustness is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when a Journal of Urban Economics (JUE) manuscript's headline spatial estimate must be shown to survive specification, spatial-scale, sorting, spillover, and inference choices before submission or in an R&R.
When should I use Jue Robustness?
Jue Robustness fits situations like: A Journal of Urban Economics (JUE) manuscripts headline spatial estimate must be shown to survive specification; inference choices before submission.
How do I install Jue Robustness in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jue-robustness -a claude-code`. Or copy the skill folder (Journal-of-Urban-Economics-Skills/skills/jue-robustness in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jue-robustness in your project. Claude Code loads it when a task matches its description.
How do I install Jue Robustness in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jue-robustness -a codex`. Or copy the skill folder (Journal-of-Urban-Economics-Skills/skills/jue-robustness in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jue-robustness in your project. Codex loads it when a task matches its description.
Can I use Jue Robustness 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 jue-robustness -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jue-robustness, .gemini/skills/jue-robustness, .github/skills/jue-robustness and .opencode/skills/jue-robustness in your project.
What does Jue Robustness need to run?
SKILL.md names no scripts, command-line tools or credentials: Jue Robustness is instructions for the agent only.
Does Jue Robustness 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 Jue Robustness 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 Jue Robustness use?
Jue Robustness 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 Jue Robustness use?
About 2.2k tokens (SKILL.md is roughly 8.6k 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 Jue Robustness?
Skills that share tags, products or a category with Jue Robustness: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Jue Robustness?
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.