Topic Modeling for Survey and Experimental Text Data Agent Skill

Structural topic modeling: STM spec, topic count, coherence-exclusivity.

Context cost
3.7k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 14.7 kBtext throughout, nothing executable
Licence
Source-availablethe author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Structural topic modeling: STM spec, topic count, coherence-exclusivity.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

data science

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md14.7 kB · preview
--- name: topic-modeling argument-hint: "[describe your corpus and research question]" ---
7# Topic Modeling for Survey and Experimental Text Data
8
9## Instructions
10
11### 1. Model Selection
12
13- Default to Structural Topic Models (STM) when analyzing text from surveys or experiments. STM incorporates document-level metadata — treatment conditions, respondent demographics, country — directly into estimation, allowing prevalence and content to vary with covariates (Roberts et al. 2014).
14- Use standard LDA only when no document-level covariates are needed and the corpus is large enough for unsupervised discovery (Blei, Ng & Jordan 2003).
15- Consider BERTopic when working with short texts where word co-occurrence is sparse, or when multilingual embedding-based similarity is required (Grootendorst 2022). BERTopic clusters document embeddings via HDBSCAN, which yields hard single-topic-per-document assignments (unlike STM/LDA mixed membership), c-TF-IDF topic words that can be unstable on small corpora, and no native covariate framework. Use it when embedding-based semantic similarity matters more than mixed-membership structure or covariate estimation.
16- Recognize the distinction between topic categorization and attitude inference: topic models reveal what respondents *discussed*, not what they *believe*. If the goal is inferring latent attitudes rather than categorizing surface content, complement topic modeling with an embeddings-based scaling of contextually common words or a separate supervised classifier (Hobbs & Green 2025; see also the companion text-classification skill).
17
18### 2. Preprocessing
19
20- Make preprocessing decisions explicit and justify each choice. Preprocessing is not neutral — stemming, stopword removal, and term-frequency thresholds all affect which topics emerge (Denny & Spirling 2018).
21- Lowercase all text. Remove punctuation and numbers unless they carry substantive meaning in the domain.
22- Remove stopwords using a standard list, but inspect the list for domain-relevant terms that should be retained (e.g., "foreign" in immigration research).
23- Apply stemming only after checking that it does not collapse substantively distinct terms. Compare results with and without stemming as a robustness check (Denny & Spirling 2018).
24- Set a lower-frequency threshold to prune very rare terms, and express it as a fraction of documents rather than an absolute count so it scales with corpus size. For small open-ended corpora a fixed count of 2–5 documents is often appropriate; for larger corpora, pruning terms that appear in fewer than roughly 0.5%–1% of documents is a common starting point (Grimmer & Stewart 2013). In stm, use plotRemoved() to visualize how many documents and terms are dropped across candidate thresholds before committing to one, then pass it to prepDocuments(). Report the threshold chosen and the counts of terms and documents retained.
25- For translated text, preprocess after translation. Note that translation artifacts (e.g., inconsistent phrasing across translators) may affect topic coherence — document any translation pipeline.
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md1,994 words
Files1
LicenceSource-available
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈40
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
3,635
on trigger
The instruction body, read only when the skill fires.
1.8%
of a 200k window
Ten skills this size would take about 18% of the window before you open a file.
050k100k150k200k context window

3.7k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Middling. Fine to keep on in a project where you use it weekly, worth unloading in one where you never do.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

1 file, 14.7 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md14.7 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 1 file you can review in full before installing. The Source-available licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Topic Modeling for Survey and Experimental Text Data · 3.7k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/topic-modeling-for-survey-and-experimental-text-d/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/topic-modeling-for-survey-and-experimental-text-d" ] && { [ ! -e .claude/skills/topic-modeling-for-survey-and-experimental-text-d ] || { mv .claude/skills/topic-modeling-for-survey-and-experimental-text-d "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/topic-modeling-for-survey-and-experimental-text-d" .claude/skills/ && echo "Installed .claude/skills/topic-modeling-for-survey-and-experimental-text-d"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add brycewang-stanford/topic-modeling-for-survey-and-experimental-text-d

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/topic-modeling-for-survey-and-experimental-text-d/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/topic-modeling-for-survey-and-experimental-text-d/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/topic-modeling-for-survey-and-experimental-text-d" ] && { [ ! -e ~/.claude/skills/topic-modeling-for-survey-and-experimental-text-d ] || { mv ~/.claude/skills/topic-modeling-for-survey-and-experimental-text-d "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/topic-modeling-for-survey-and-experimental-text-d" ~/.claude/skills/ && echo "Installed ~/.claude/skills/topic-modeling-for-survey-and-experimental-text-d" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/topic-modeling-for-survey-and-experimental-text-d/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. It appears as topic-modeling in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, topic-modeling-for-survey-and-experimental-text-d.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencebrycewang-stanford/topic-modeling-for-survey-and-experimental-text-d

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

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