Agent skill

Topic Model Consolidation

Consolidates BERTopic, LDA or NMF topic output into a theory-driven classification framework and writes the final labels back to an Excel file.

MITAuto-check passedResearch & Science

Install Topic Model Consolidation

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-tf -a claude-code

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

GitHub CLI

$ gh skill install TyrealQ/q-skills q-tf --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/TyrealQ/q-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/q-scholar/q-tf .claude/skills/q-tf && 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/

Paste into Claude Code

Install the "q-tf" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-tf into .claude/skills/q-tf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-tf", 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

$skill-installer install https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-tf

Type 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.

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-tf -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-tf --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/q-scholar/q-tf .agents/skills/q-tf && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "q-tf" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-tf into .agents/skills/q-tf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-tf", 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.

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-tf -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-tf --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/q-scholar/q-tf .cursor/skills/q-tf && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "q-tf" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-tf into .cursor/skills/q-tf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-tf", 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

$ gemini skills install https://github.com/TyrealQ/q-skills.git --path skills/q-scholar/q-tf

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-tf -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-tf --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/q-scholar/q-tf .gemini/skills/q-tf && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "q-tf" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-tf into .gemini/skills/q-tf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-tf", 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.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-tf

Installs 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).

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-tf -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/q-scholar/q-tf .github/skills/q-tf && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "q-tf" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-tf into .github/skills/q-tf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-tf", 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.

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-tf -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-tf --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/q-scholar/q-tf .opencode/skills/q-tf && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "q-tf" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-tf into .opencode/skills/q-tf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-tf", 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: q-tf
GitHub stars: 108
Token cost: ~1k tokens
SKILL.md length: 324 words
Files: 9 (incl. scripts, references)
Skills in repo: 16
Repo updated: Sep 23, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Consolidates BERTopic, LDA or NMF topic output into a theory-driven classification framework and writes the final labels back to an Excel file.

Works in 3 steps: Determine this SKILL.md file's directory… → Script path = ${SKILL_DIR}/scripts/. → Reference path = ${SKILL_DIR}/references/.
Merging many raw topics from a topic model into a smaller theory-based set
SKILL.md covers Script Directory, Dependencies, References and Core Principles, plus 5 more sections
Runs Python scripts from its folder; calls python and pip; needs GEMINI_API_KEY

What it does

The skill follows a six-step workflow for academic manuscripts: load the topics and find overlaps and unassigned ones, define the final topic structure in a `FINAL_TOPICS` dictionary, classify each topic against a theoretical framework, generate an implementation plan in Markdown, update the source Excel data with labels, and reclassify outliers with a foundation model. Scripts handle the plan, the Excel update and the outlier classification.

Core rules keep domain distinctions such as entity, event, geography and stakeholder intact, track topics that sit in several categories and calculate their overlap, and require every non-outlier topic to land in at least one category. Reference notes cover the preservation rules, code patterns, the outlier workflow with its prompt template and a worked esports example. Outlier classification calls Gemini and needs a `GEMINI_API_KEY`; `GEMINI_MODEL` is optional.

When your agent uses it

Merging many raw topics from a topic model into a smaller theory-based set
Reclassifying outlier documents after a BERTopic run
Updating Excel topic labels once the final categories are decided

Example prompts

“Consolidate my BERTopic output in ./topics.xlsx into a handful of theory-based categories.”
“Reassign the outlier documents from my LDA run using a foundation model.”
“Update the Excel sheet with the final topic labels and generate the implementation plan.”

Requirements

Python with pandas, openpyxl and google-genai installed
A GEMINI_API_KEY for outlier classification

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

Determine this SKILL.md file's directory path as SKILL_DIR.
Script path = ${SKILL_DIR}/scripts/.
Reference path = ${SKILL_DIR}/references/.

What it can do on your machine

Read from SKILL.md and the folder at commit d8aaee7. 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
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- GEMINI_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Topic Model Consolidation loads about 1k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 324 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~58
When it runs · the whole SKILL.md, loaded when a task matches: ~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~3k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from TyrealQ/q-skills at commit d8aaee7, republished under its MIT licence (© TyrealQ). 324 words, ~1,015 tokens.

Download SKILL.mdSave it as .claude/skills/q-tf/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.

name: q-tf
description: Consolidate topic modeling outputs (BERTopic, LDA, NMF) into theory-driven classification frameworks. Use for topic finetuning, topic consolidation, reclassification, outlier handling, or updating Excel labels from topic models.

Q-TF

Fine-tune topic modeling outputs into consolidated, theory-driven topic frameworks for academic manuscripts.

If in plan mode: write a brief plan — "Run q-tf skill: load topic model output, define final topic structure with theoretical framework, generate implementation plan, update Excel with labels." — then exit plan mode immediately. Do NOT attempt topic analysis, script execution, or Excel updates while plan mode is active.

Script Directory

Agent execution instructions:

Determine this SKILL.md file's directory path as SKILL_DIR.
Script path = ${SKILL_DIR}/scripts/<script-name>.
Reference path = ${SKILL_DIR}/references/<ref-name>.

Dependencies

pandas
openpyxl          # required for .xlsx input/output
google-genai      # required for outlier classification via Gemini

Install: pip install pandas openpyxl google-genai

Environment variables: GEMINI_API_KEY (for outlier classification only), GEMINI_MODEL (optional model override).

References

references/preservation_rules.md — domain preservation rules, theoretical framework template, multi-category handling
references/code_patterns.md — four Python patterns: topic definition, assignment mapping, overlap calculation, Excel update
references/outlier_workflow.md — foundation model outlier classification workflow
references/esports_ugc_example.md — worked example
references/SP_OUTLIER_TEMPLATE.txt — outlier classification prompt template

Core Principles

Preserve domain-specific distinctions (entity, event, geography, stakeholder) — see references/preservation_rules.md
Theory-driven classification using a customizable framework template
Track multi-category topics explicitly; calculate overlap for reconciliation
All non-outlier topics must be assigned to at least one category

Workflow

Step	Action	Reference
1	Load & analyze topics — identify overlaps, unassigned	—
2	Define final topic structure (FINAL_TOPICS dictionary)	references/code_patterns.md
3	Apply theoretical framework — classify each topic	references/preservation_rules.md
4	Generate implementation plan (MD)	`scripts/generate_implementation_plan.py`
5	Update source data with labels (Excel)	`scripts/update_excel_with_labels.py`
6	Reclassify outliers via foundation model	references/outlier_workflow.md

Required Inputs

Topic model output (Excel/CSV) — Topic ID, Count, Name/Label, Keywords, Representative_Docs (optional)
Merge recommendations (optional) — Sheets: MERGE_GROUPS, INDEPENDENT_TOPICS
Document data (for label updates) — individual documents with Topic ID column

Script Invocation

bash

python "${SKILL_DIR}/scripts/generate_implementation_plan.py" --input topic_model_output.xlsx --output implementation_plan.md
python "${SKILL_DIR}/scripts/update_excel_with_labels.py" --input document_data.xlsx --output document_data_labeled.xlsx

Adapt scripts by updating FINAL_TOPICS, FINAL_LABELS, and theme categories. See references/code_patterns.md. For a worked example, see references/esports_ugc_example.md.

Expected Outputs

Output	Description
`implementation_plan.md`	Full classification plan with topic mappings and reconciliation
`*_labeled.xlsx`	Source data with `Final_Topic_Code`, `Final_Topic_Label`, `Category_Theme` columns
Outlier results (optional)	Updated `Final_Topic_Label`, `classification_confidence`, `key_phrases` columns

Scope

Include: Topic consolidation, theoretical classification, Excel label updates, outlier reclassification. Exclude: Topic modeling itself (BERTopic/LDA/NMF execution), visualization, statistical analysis.

Files

SKILL.md and 8 other files (scripts, references) in skills/q-scholar/q-tf of TyrealQ/q-skills.

SKILL.md
references/SP_OUTLIER_TEMPLATE.txt
references/code_patterns.md
references/esports_ugc_example.md
references/outlier_workflow.md
references/preservation_rules.md
scripts/classify_outliers.py
scripts/generate_implementation_plan.py
scripts/update_excel_with_labels.py

Open the folder on GitHubat commit d8aaee7

Compare with similar skills

Topic Model Consolidation 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.

Topic Model Consolidation compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Topic Model Consolidation this skillTyrealQ/q-skills	108	—	~1k	Automated safety check: Pass	MIT	16 days ago
Excel Spreadsheet Creation and Editinganthropics/skills	180k	4 repos	~2.1k	Automated safety check: Pass	Proprietary	yesterday
XLSX Spreadsheet ToolkitXiaomiMiMo/MiMo-Code	14k	—	~2.9k	Automated safety check: Pass	Apache-2.0	today
BiSheng XLSX Workbook Builderdataelement/bisheng	12k	—	~1.7k	Automated safety check: Pass	Apache-2.0	today
Kimi XLSXthvroyal/kimi-skills	238	—	~9.5k	Automated safety check: Pass	None	6 mo ago
Excel Spreadsheet Builderagentscope-ai/QwenPaw	36k	—	~1.8k	Automated safety check: Pass	Proprietary	today

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Works with

Questions about Topic Model Consolidation

What does Topic Model Consolidation do?

Consolidates BERTopic, LDA or NMF topic output into a theory-driven classification framework and writes the final labels back to an Excel file. The skill follows a six-step workflow for academic manuscripts: load the topics and find overlaps and unassigned ones, define the final topic structure in a `FINAL_TOPICS` dictionary, classify each topic against a theoretical framework, generate an implementation plan in Markdown, update the source Excel data with labels, and reclassify outliers with a foundation model. Scripts handle the plan, the Excel update and the outlier classification.

When should I use Topic Model Consolidation?

Topic Model Consolidation fits situations like: merging many raw topics from a topic model into a smaller theory-based set; reclassifying outlier documents after a BERTopic run; updating Excel topic labels once the final categories are decided.

How do I install Topic Model Consolidation in Claude Code?

Run `npx skills add TyrealQ/q-skills --skill q-tf -a claude-code`. Or copy the skill folder (skills/q-scholar/q-tf in TyrealQ/q-skills) into .claude/skills/q-tf in your project. Claude Code loads it when a task matches its description.

How do I install Topic Model Consolidation in Codex?

Run `npx skills add TyrealQ/q-skills --skill q-tf -a codex`. Or copy the skill folder (skills/q-scholar/q-tf in TyrealQ/q-skills) into .agents/skills/q-tf in your project. Codex loads it when a task matches its description.

Can I use Topic Model Consolidation 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 TyrealQ/q-skills --skill q-tf -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/q-tf, .gemini/skills/q-tf, .github/skills/q-tf and .opencode/skills/q-tf in your project.

What does Topic Model Consolidation need to run?

Going by SKILL.md and its folder, Topic Model Consolidation needs Python for the scripts in its folder, the command-line tools its instructions call (python and pip) and credentials named GEMINI_API_KEY. Our summary lists: Python with pandas, openpyxl and google-genai installed; A GEMINI_API_KEY for outlier classification.

Does Topic Model Consolidation access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Topic Model Consolidation 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Topic Model Consolidation use?

Topic Model Consolidation 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 Topic Model Consolidation use?

About 1k tokens (SKILL.md is roughly 4.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.

What are the alternatives to Topic Model Consolidation?

Skills that share tags, products or a category with Topic Model Consolidation: Excel Spreadsheet Creation and Editing (anthropics/skills, 180k stars), XLSX Spreadsheet Toolkit (XiaomiMiMo/MiMo-Code, 14k stars), BiSheng XLSX Workbook Builder (dataelement/bisheng, 12k stars) and Kimi XLSX (thvroyal/kimi-skills, 238 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Topic Model Consolidation?

TyrealQ (a GitHub user) maintains it in TyrealQ/q-skills, which has 108 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on September 23, 2026.

Source: TyrealQ/q-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Topic Model Consolidation

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Q-TF

Script Directory

Dependencies

References

Core Principles

Workflow

Required Inputs

Script Invocation

Expected Outputs

Scope

Files

Compare with similar skills

Similar skills

More from TyrealQ/q-skills

Related

Questions about Topic Model Consolidation