A skill your agent uses when defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork, corpus construction, phonetic measurement…

MITAuto-check passed

Install Lang Research Design

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill lang-research-design -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills lang-research-design --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Language-Linguistic-Society-Skills/skills/lang-research-design .claude/skills/lang-research-design && rm -rf skills-src

Use ~/.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/

Facts

Skill name
lang-research-design
GitHub stars
1.2k
Token cost
~1.6k tokens
SKILL.md length
700 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork, corpus construction, phonetic measurement…

  • Defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork
  • SKILL.md covers When to trigger, Defend the design (by subfield), Match design to claim and Referee-pushback patterns by…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Corpus construction

What it does

Lang Research Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork, corpus construction, phonetic measurement, experiment, or the diachronic/typological sample. Language judges each kind of evidence by its own standards, and the design must support the theoretical claim. Defends the design; it does not run the analysis.

Its SKILL.md is about 1.6k 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

  • Defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork
  • Corpus construction
  • Phonetic measurement
  • The diachronic/typological sample

Example prompts

  • “/lang-research-design”

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

Lang Research Design loads about 1.6k tokens when it runs. Until then it costs about 101 tokens; SKILL.md has 700 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~101
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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). 700 words, ~1,629 tokens.

Download SKILL.mdSave it as .claude/skills/lang-research-design/SKILL.md (or your agent's skills folder).
name
lang-research-design
description
Use when defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork, corpus construction, phonetic measurement, experiment, or the diachronic/typological sample. Language judges each kind of evidence by its own standards, and the design must support the theoretical claim. Defends the design; it does not run the analysis.

Research Design (lang-research-design)

Language is method-pluralist: it publishes elicited fieldwork, corpus studies, phonetic and experimental work, computational modeling, and diachronic/typological comparison, and it judges each by the standards of its own subfield. The job here is to make the design defensible to a general, possibly cross-subfield, double-anonymous reviewer — and to show the evidence actually supports the theoretical claim from lang-theory-building.

When to trigger

  • Choosing or justifying the design before data collection or analysis
  • A reader questioned the elicitation, the consultant sample, corpus coverage, measurement, or the typological sample
  • Aligning the evidence with the analysis's predictions
  • Mixed-evidence work (e.g., corpus + experiment) that must defend each component

Defend the design (by subfield)

Elicited / fieldwork data
  • Describe consultant number and background, elicitation method, and the recording/annotation workflow; distinguish elicited judgments from spontaneous/textual data.
  • Give data in numbered examples with Leipzig interlinear glossing and a source for each token; a reader must be able to see the pattern, not take it on faith.
Corpus / quantitative usage
  • Justify corpus choice, sampling frame, and coding scheme; report inter-annotator agreement for hand-coded variables; state how tokens were extracted and excluded.
Phonetic / experimental
  • Specify participants, stimuli, task, and measurement (e.g., forced alignment, formant/pitch extraction settings); pre-empt confounds; where predictions are directional, say so in advance.
Diachronic / typological
  • Make sample construction and genealogical/areal control explicit; guard against areal or bibliographic bias; keep a clear trail from primary sources to the coded generalization.
Computational / modeling
  • State what the model is a model of; separate the claim about the grammar from the properties of the architecture or training data.

Match design to claim

The single most common Language reviewer objection: the data cannot bear the generalization. Walk the chain: claim → prediction → the observation that would confirm/disconfirm it → the design's leverage on that observation. A three-language convenience sample cannot ground a universal; either narrow the claim or widen the evidence — do not overreach.

Referee-pushback patterns by subfield (the modal Language objection)

Referee writes…SubfieldThe Language-appropriate fix
"Judgments from one speaker."fieldworkadd consultants or scope the claim to the idiolect/variety
"Cherry-picked corpus tokens."corpusreport the full extraction + exclusion rule + agreement
"Confound with speech rate."phoneticscontrol or model it; show the effect survives
"Sample is areally biased."typologicalrebalance the sample or restrict the generalization
Show full SKILL.md (325 more words)Show less

Calibration with a quick example (hedged)

Language judges each subfield by its own standard, not a single template; unlike a purely formal venue that accepts introspective judgments alone, it increasingly expects the evidence base to be visible and checkable. Illustrative: an author claims a word-order universal from four related languages; a referee flags "genealogical non-independence." The fix draws a genealogically stratified sample and restates the claim as a statistical tendency with the mechanism, so the typology can see the pattern fail as well as hold. Confirm current data expectations on the author pages and in lang-data-and-transparency.

Design pass for Language

Treat this skill as an executable review pass, not a prose hint. First lock the empirical generalization, evidence base, warrant, and theoretical payoff; then judge whether the manuscript answers the venue's real reader: linguists across subfields who value grounded analysis, transparent and checkable evidence, and careful, appropriately scoped generalizations.

  • Do the pass: lock the unit (segment / token / speaker / language), the sample, the comparison, the validity threat, and the minimum decisive evidence before recommending collection or submission.
  • Return a ledger: give claim / evidence / risk / manuscript location rows so the next agent can edit rather than rediscover the issue.
  • Sibling guard: compare against Phonology, NLLT, Journal of Semantics, Diachronica, Language Variation and Change; if a sibling owns the contribution, recommend re-routing before polishing.
  • Stop condition: do not give submission-ready advice until resources/official-source-map.md has been checked and the manuscript has one concrete fix for the largest venue-specific risk.

Anti-patterns

  • Grounding a general claim on a convenience sample that cannot support it
  • Judgments from a single consultant presented as facts about the language
  • Corpus tokens hand-picked with no stated extraction or exclusion rule
  • Phonetic effects reported without controlling obvious confounds
  • A typological sample with unacknowledged genealogical or areal dependence
  • A design that probes something adjacent to, but not, the stated prediction

Output format

【Subfield】fieldwork / corpus / phonetic-experimental / typological-diachronic / computational / mixed
【Claim it must support】from theory-building
【Design leverage】how this evidence bears on the prediction
【Key threats】consultant number, sampling, confounds, non-independence, annotation
【Evidentiary trail】data → glossed examples → claim is legible? [Y/N]
【Verdict】supports the claim / needs tightening / overreaches (fix)
【Next】lang-data-analysis

Supplementary resources

© 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 Language-Linguistic-Society-Skills/skills/lang-research-design of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Questions about Lang Research Design

What does Lang Research Design do?

A skill your agent uses when defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork, corpus construction, phonetic measurement…. Lang Research Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork, corpus construction, phonetic measurement, experiment, or the diachronic/typological sample.

When should I use Lang Research Design?

Lang Research Design fits situations like: defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork; corpus construction; phonetic measurement; the diachronic/typological sample.

How do I install Lang Research Design in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill lang-research-design -a claude-code`. Or copy the skill folder (Language-Linguistic-Society-Skills/skills/lang-research-design in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/lang-research-design in your project. Claude Code loads it when a task matches its description.

How do I install Lang Research Design in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill lang-research-design -a codex`. Or copy the skill folder (Language-Linguistic-Society-Skills/skills/lang-research-design in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/lang-research-design in your project. Codex loads it when a task matches its description.

Can I use Lang Research Design 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 lang-research-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lang-research-design, .gemini/skills/lang-research-design, .github/skills/lang-research-design and .opencode/skills/lang-research-design in your project.

What does Lang Research Design need to run?

SKILL.md names no scripts, command-line tools or credentials: Lang Research Design is instructions for the agent only.

Does Lang Research Design 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 Lang Research Design 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 Lang Research Design use?

Lang Research Design 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 Lang Research Design use?

About 1.6k tokens (SKILL.md is roughly 6.5k 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 Lang Research Design?

Skills that share tags, products or a category with Lang Research Design: Securing Azure With Microsoft Defender (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Lang Attribute (thedaviddias/Front-End-Checklist, 74k stars), Lambda Lang (sickn33/agentic-awesome-skills, 47k stars) and Analyzing Powershell Empire Artifacts (mukul975/Anthropic-Cybersecurity-Skills, 34k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Lang Research Design?

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.