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Agent skill

Algo NLP Lda

by asgard-ai-platform in asgard-ai-platform/skills

Implement LDA topic modeling to discover latent topics in document collections.

MITAuto-check passedAI & LLM Engineering

Install Algo NLP Lda

skills CLI
$ npx skills add asgard-ai-platform/skills --skill algo-nlp-lda -a claude-code

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

GitHub CLI
$ gh skill install asgard-ai-platform/skills algo-nlp-lda --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/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-nlp-lda .claude/skills/algo-nlp-lda && 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
algo-nlp-lda
GitHub stars
242
Token cost
~1.1k tokens
SKILL.md length
411 words
Files
4 (incl. references)
Skills in repo
207
Repo updated
First seen
Licence
MIT

At a glance

Implement LDA topic modeling to discover latent topics in document collections.

  • Works in 4 steps: Input Validation → Core Algorithm → Verification → …
  • The user needs to extract topics from a text corpus
  • SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Algo NLP Lda is an agent skill from asgard-ai-platform/skills. Implement LDA topic modeling to discover latent topics in document collections. Use this skill when the user needs to extract topics from a text corpus, categorize documents by theme, or explore thematic structure — even if they say 'what are the main topics', 'topic extraction', or 'document clustering by theme'.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/advanced-lda.md` and `references/topic-evaluation.md`).

It sits in AI & LLM Engineering, covering Natural language processing. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.

When your agent uses it

  • The user needs to extract topics from a text corpus
  • Categorize documents by theme
  • Explore thematic structure — even if they say what are the main topics
  • Topic extraction

Example prompts

  • “what are the main topics”
  • “topic extraction”
  • “document clustering by theme”
  • “/algo-nlp-lda”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Input Validation
  2. Core Algorithm
  3. Verification
  4. Output

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. 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 (its code samples are json).

    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

Algo NLP Lda loads about 1.1k tokens when it runs, and up to ~6.5k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 411 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.5k

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 asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 411 words, ~1,068 tokens.

Download SKILL.mdSave it as .claude/skills/algo-nlp-lda/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
algo-nlp-lda
description
Implement LDA topic modeling to discover latent topics in document collections. Use this skill when the user needs to extract topics from a text corpus, categorize documents by theme, or explore thematic structure — even if they say 'what are the main topics', 'topic extraction', or 'document clustering by theme'.
metadata.category
WP-45 NLP 演算法
metadata.tags
nlp, lda, topic-modeling, text-mining

LDA Topic Modeling

Overview

Latent Dirichlet Allocation models each document as a mixture of topics and each topic as a distribution over words. Discovers K latent topics from a corpus without supervision. Uses Gibbs sampling or variational inference. Complexity: O(N × K × iterations) where N = total word tokens.

When to Use

Trigger conditions:

  • Discovering latent themes in a large document collection
  • Organizing/categorizing documents by automatically discovered topics
  • Exploratory text analysis when categories are unknown

When NOT to use:

  • When categories are known (use supervised classification)
  • For short texts (tweets, titles) — too few words per document for reliable topic assignment
  • When you need semantic understanding (use embeddings)

Algorithm

IRON LAW: The Number of Topics K Must Be Chosen, Not Discovered
LDA does NOT tell you how many topics exist. K is a hyperparameter.
Too few topics: overly broad, mixed themes. Too many: fragmented,
redundant topics. Use coherence score (C_v) to compare K values,
but the final choice requires human judgment on topic interpretability.
Phase 1: Input Validation

Preprocess: tokenize, remove stop words, apply lemmatization. Build document-term matrix. Filter: remove terms appearing in <5 or >50% of documents. Gate: Clean DTM, vocabulary size reasonable (1K-50K terms).

Phase 2: Core Algorithm
  1. Choose K (start with √(N/2), try range K=5,10,15,20,...)
  2. Set hyperparameters: α = 50/K (document-topic density), β = 0.01 (topic-word density)
  3. Run LDA (Gibbs sampling: 1000+ iterations, or variational inference)
  4. Extract: topic-word distributions (top 10-20 words per topic) and document-topic distributions
Phase 3: Verification

Evaluate: topic coherence (C_v score, higher is better), manual inspection of top words per topic, check for "junk" topics (mixed incoherent words). Gate: Coherence score acceptable, topics are humanly interpretable.

Phase 4: Output

Return topics with top words and document assignments.

Output Format

json
{
  "topics": [{"id": 0, "label": "finance", "top_words": ["revenue", "profit", "quarter", "growth"], "coherence": 0.55}],
  "doc_topics": [{"doc_id": "d1", "dominant_topic": 0, "topic_distribution": [0.7, 0.1, 0.2]}],
  "metadata": {"K": 10, "coherence_avg": 0.48, "documents": 5000, "vocabulary": 8000}
}

Examples

Sample I/O

Input: 1000 news articles, K=5 Expected: Topics like: {politics, sports, technology, business, entertainment} with coherent top words per topic.

Show full SKILL.md (159 more words)Show less
Edge Cases
InputExpectedWhy
Very short documentsPoor topic assignmentToo few words for reliable mixture estimation
Homogeneous corpus1-2 topics dominateAll documents are similar, limited topic diversity
K=1Single topic = corpus vocabularyDegenerate case, no discrimination

Gotchas

  • Stop words MUST be removed: LDA will create "junk" topics dominated by common words ("the", "is", "and") if stop words remain.
  • Topic labeling is manual: LDA gives word distributions, NOT topic names. You must interpret and label topics based on top words.
  • Reproducibility: Gibbs sampling is stochastic. Different random seeds give different topics. Run multiple times and check stability.
  • Dynamic topics: Standard LDA assumes topics are static. For evolving corpora (news over years), use Dynamic Topic Models.
  • Hyperparameter sensitivity: Low α produces documents with fewer, more distinct topics. Low β produces topics with fewer, more specific words. Tune or use automatic methods.

References

  • For coherence metrics and K selection, see references/topic-evaluation.md
  • For dynamic and correlated topic models, see references/advanced-lda.md

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in algo-nlp-lda of asgard-ai-platform/skills.

  • SKILL.md
  • examples/sample_scenario.md
  • references/advanced-lda.md
  • references/topic-evaluation.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

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Categories

Questions about Algo NLP Lda

What does Algo NLP Lda do?

Implement LDA topic modeling to discover latent topics in document collections. Algo NLP Lda is an agent skill from asgard-ai-platform/skills. Implement LDA topic modeling to discover latent topics in document collections.

When should I use Algo NLP Lda?

Algo NLP Lda fits situations like: the user needs to extract topics from a text corpus; categorize documents by theme; explore thematic structure — even if they say what are the main topics; topic extraction.

How do I install Algo NLP Lda in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill algo-nlp-lda -a claude-code`. Or copy the skill folder (algo-nlp-lda in asgard-ai-platform/skills) into .claude/skills/algo-nlp-lda in your project. Claude Code loads it when a task matches its description.

How do I install Algo NLP Lda in Codex?

Run `npx skills add asgard-ai-platform/skills --skill algo-nlp-lda -a codex`. Or copy the skill folder (algo-nlp-lda in asgard-ai-platform/skills) into .agents/skills/algo-nlp-lda in your project. Codex loads it when a task matches its description.

Can I use Algo NLP Lda 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 asgard-ai-platform/skills --skill algo-nlp-lda -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-nlp-lda, .gemini/skills/algo-nlp-lda, .github/skills/algo-nlp-lda and .opencode/skills/algo-nlp-lda in your project.

What does Algo NLP Lda need to run?

SKILL.md names no scripts, command-line tools or credentials: Algo NLP Lda is instructions for the agent only.

Does Algo NLP Lda 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 Algo NLP Lda 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 Algo NLP Lda use?

Algo NLP Lda 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 Algo NLP Lda use?

About 1.1k tokens (SKILL.md is roughly 4.3k 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 5.5k tokens, read only when the agent opens those files.

What are the alternatives to Algo NLP Lda?

Skills that share tags, products or a category with Algo NLP Lda: Hugging Face Tokenizers (Orchestra-Research/AI-Research-SKILLs, 13k stars), OpenMed Model Card Writer (maziyarpanahi/openmed, 5.5k stars), Gptqmodel Tokenizer Normalization (ModelCloud/GPTQModel, 1.3k stars) and Andrej Karpathy (K-Dense-AI/mimeo, 282 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algo NLP Lda?

asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.

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