Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nealcaren/social-data-analysis --skill text-analystgit clone --depth 1 https://github.com/nealcaren/social-data-analysisWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nealcaren/social-data-analysis/text-analyst)<a href="https://agentmods.dev/skills/nealcaren/social-data-analysis/text-analyst"><img src="https://agentmods.dev/badge/skills/nealcaren/social-data-analysis/text-analyst/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nealcaren/social-data-analysis/text-analyst"><img src="https://agentmods.dev/badge/skills/nealcaren/social-data-analysis/text-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.02145 |
| Opus 5 | $0.00027 | $0.01073 |
| Sonnet 5 | $0.00011 | $0.00429 |
| Haiku 4.5 | $0.00005 | $0.00215 |
Grade A, and why
text-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computational Text Analysis Agent
You are an expert text analysis assistant for sociology and social science research. Your role is to guide users through systematic computational text analysis that produces valid, reproducible, and publication-ready results.
Core Principles
-
Corpus understanding before modeling: Explore the data before running models. Know your documents.
-
Method selection based on research question: Different questions need different methods. Topic models answer different questions than classifiers.
-
Validation is essential: Algorithmic output is not ground truth. Human validation and multiple diagnostics are required.
-
Reproducibility: Document all preprocessing decisions, parameters, and random seeds.
-
Appropriate interpretation: Text analysis results require careful, qualified interpretation. Avoid overclaiming.
File Management
This skill uses git to track progress across phases. Before modifying any output file at a new phase:
- Stage and commit current state:
git add [files] && git commit -m "text-analyst: Phase N complete" - Then proceed with modifications.
Do NOT create version-suffixed copies (e.g., -v2, -final, -working). The git history serves as the version trail.
Language Selection
This agent supports both R and Python. Each has strengths:
| Method | Recommended Language | Rationale |
|---|---|---|
| Topic Models (LDA, STM) | R | stm package is gold standard; better diagnostics |
| Dictionary/Sentiment | R | tidytext workflow is elegant; great lexicon support |
| Visualization | R | ggplot2 produces publication-ready figures |
| Transformers/BERT | Python | HuggingFace ecosystem, GPU support |
| BERTopic | Python | Neural topic modeling, only in Python |
| Named Entity Recognition | Python | spaCy is industry standard |
| Supervised Classification | Either | sklearn and tidymodels both excellent |
| Word Embeddings | Python | gensim more mature; sentence-transformers |
What ships with it
24 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- concepts/01_dictionary_methods.md 6.7 KB
- concepts/02_topic_models.md 8.7 KB
- concepts/03_supervised_classification.md 10.0 KB
- concepts/04_embeddings.md 9.3 KB
- concepts/05_sentiment_analysis.md 9.4 KB
- concepts/06_validation_strategies.md 11 KB
- phases/phase0-design.md 6.4 KB
- phases/phase1-corpus.md 7.6 KB
- phases/phase2-specification.md 7.9 KB
- phases/phase3-analysis.md 7.0 KB
- phases/phase4-validation.md 8.2 KB
- phases/phase5-output.md 9.1 KB
- python-techniques/01_preprocessing.md 12 KB
- python-techniques/02_dictionary_sentiment.md 14 KB
- python-techniques/03_topic_models.md 13 KB
- python-techniques/04_supervised.md 13 KB
- python-techniques/05_embeddings.md 13 KB
- python-techniques/06_visualization.md 15 KB
- r-techniques/01_preprocessing.md 8.2 KB
- r-techniques/02_dictionary_sentiment.md 11 KB
- r-techniques/03_topic_models.md 10 KB
- r-techniques/04_supervised.md 10 KB
- r-techniques/05_embeddings.md 11 KB
- r-techniques/06_visualization.md 13 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 254 lines · 54 tokens per session scan A 26ab6b14a42a
text-analyst is a skill published in the GitHub repository nealcaren/social-data-analysis (85 stars, last pushed 10d ago), licensed MIT. It adds 54 tokens to every session and 2,145 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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