topic-modeling SKILL.md skillA
topic-modeling from scdenney/open-science-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add scdenney/open-science-skills/topic-modeling@git:20261008.697c89fPins git:20261008.697c89f in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- scdenney/open-science-skills
- Path
codex/topic-modeling/SKILL.md- Versions recorded
- 3
- Latest version
- git:20261008.697c89f, committed Oct 8, 2026
- First version recorded
- Jul 2, 2026
- Size
- 12,417 bytes
- Repository stars
- 69
- License
- a license file GitHub does not identify
Advises on setting up and validating topic models for survey and experimental text: discovery vs. measurement, STM/LDA/BERTopic choice, preprocessing, covariate formulas, choosing K across coherence, FREX, held-out likelihood, and residuals, validating topics against human coding, robustness, and reporting. Coding against a fixed codebook goes to text-classification.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Topic Modeling for Survey and Experimental Text Data ## Instructions Help the user design the analysis before fitting anything. Work through the decisions below in order, explain what each choice changes and which standard governs it, and record the choice with its rationale. Every one of them — model, preprocessing, covariates, K, labels — shapes which topics appear, so each belongs in the methods section. ### 1. Discovery or Measurement? Settle this first; it decides the rest. - Topic models are discovery tools: they show what respondents *discussed*. If the user already has categories and wants each document coded against them, that is classification — use `$text-classification`. - If the goal is to infer latent attitudes rather than categorize surface content, topics alone will not do it; complement them with an embeddings-based scaling of contextually common words or a supervised classifier (Hobbs & Green 2025). - A common sound design is two-stage: discover categories with a topic model, then write those categories into a codebook and measure them with validated supervised classification for confirmatory tests (Grimmer & Stewart 2013). …
Read the whole file at its exact version.
How to install
mdr add scdenney/open-science-skills/topic-modeling@sha256:c4ac93c81faf88c3The line at the top of this page pins the label git:20261008.697c89f, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_ngqehyoehtkf25cn)
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Versions
The registry has recorded 3 versions of this file, between Jul 2, 2026 and Oct 8, 2026, about one every 49 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20261008.697c89f latest | 2026-10-08 | 697c89f | 12,417 B | A | view · diff |
| git:20260902.da5a263 | 2026-09-02 | da5a263 | 15,461 B | A | view · diff |
| git:20260702.0d142fe | 2026-07-02 | 0d142fe | 14,820 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (12417 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
scdenney/open-science-skills · 69 stars · license a license file GitHub does not identify · pushed 2026-11-05 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ngqehyoehtkf25cn GET https://markdownregistry.com/api/v1/resolve?ref=scdenney/open-science-skills/topic-modeling GET https://markdownregistry.com/api/v1/blob/c4ac93c81faf88c35d71c5006c0e3d8f31c1266adebaacd0e2556cfbd899928f
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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