Transformers
ynulihao/AgentSkillOS
Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers.
Text processing pipeline: an agent team collaborates to perform preprocessing, classification, entity/keyword extraction, sentiment analysis, summarization, structured data conversion, and report…
Install Text Processor
$ npx skills add revfactory/harness-100 --skill text-processor -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install revfactory/harness-100 text-processor --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .claude/skills && cp -r skills-src/en/33-text-processor/.claude/skills/text-processor .claude/skills/text-processor && rm -rf skills-srcUse ~/.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/
Install the "text-processor" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/text-processor into .claude/skills/text-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "text-processor", 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 install https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/text-processorType 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.
$ npx skills add revfactory/harness-100 --skill text-processor -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install revfactory/harness-100 text-processor --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .agents/skills && cp -r skills-src/en/33-text-processor/.claude/skills/text-processor .agents/skills/text-processor && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "text-processor" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/text-processor into .agents/skills/text-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "text-processor", 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.
$ npx skills add revfactory/harness-100 --skill text-processor -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install revfactory/harness-100 text-processor --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/en/33-text-processor/.claude/skills/text-processor .cursor/skills/text-processor && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "text-processor" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/text-processor into .cursor/skills/text-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "text-processor", 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 https://github.com/revfactory/harness-100.git --path en/33-text-processor/.claude/skills/text-processor--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add revfactory/harness-100 --skill text-processor -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install revfactory/harness-100 text-processor --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/en/33-text-processor/.claude/skills/text-processor .gemini/skills/text-processor && rm -rf skills-srcUse ~/.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/
Install the "text-processor" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/text-processor into .gemini/skills/text-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "text-processor", 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.
$ gh skill install revfactory/harness-100 text-processorInstalls 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).
$ npx skills add revfactory/harness-100 --skill text-processor -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .github/skills && cp -r skills-src/en/33-text-processor/.claude/skills/text-processor .github/skills/text-processor && rm -rf skills-srcUse ~/.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/
Install the "text-processor" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/text-processor into .github/skills/text-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "text-processor", 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.
$ npx skills add revfactory/harness-100 --skill text-processor -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install revfactory/harness-100 text-processor --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/en/33-text-processor/.claude/skills/text-processor .opencode/skills/text-processor && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "text-processor" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/text-processor into .opencode/skills/text-processor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "text-processor", 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
text-processor- GitHub stars
- 1.3k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 714 words
- Files
- 1
- Skills in repo
- 464
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Text processing pipeline: an agent team collaborates to perform preprocessing, classification, entity/keyword extraction, sentiment analysis, summarization, structured data conversion, and report…
- Works in 3 steps: Preparation (performed directly by the… → Team Assembly and Execution → Integration and Final Deliverables
- Requests like analyze this text
- SKILL.md covers Execution Mode, Agent Composition, Workflow and Execution Modes by Request Scope, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Text Processor is an agent skill from revfactory/harness-100. Text processing pipeline: an agent team collaborates to perform preprocessing, classification, entity/keyword extraction, sentiment analysis, summarization, structured data conversion, and report generation on bulk text. Use this skill for requests like 'analyze this text', 'text processing', 'classify documents', 'run sentiment analysis', 'extract keywords', 'named entity recognition', 'NER', 'text summarization', 'review analysis', 'survey text analysis', 'comment analysis', and other general text NLP tasks…
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Customer feedback analysis, Natural language processing and Summarization. The licence is Apache-2.0.
When your agent uses it
- Requests like analyze this text
- Text processing
- Classify documents
- Run sentiment analysis
Example prompts
- “analyze this text”
- “text processing”
- “classify documents”
- “/text-processor”
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 8e8d35c. 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.
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
Text Processor loads about 1.9k tokens when it runs. Until then it costs about 167 tokens; SKILL.md has 714 words of instructions outside code blocks.
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 revfactory/harness-100 at commit 8e8d35c, republished under its Apache-2.0 licence (© revfactory). 714 words, ~1,940 tokens.
Download SKILL.mdSave it as
.claude/skills/text-processor/SKILL.md (or your agent's skills folder).- name
- text-processor
- description
- Text processing pipeline: an agent team collaborates to perform preprocessing, classification, entity/keyword extraction, sentiment analysis, summarization, structured data conversion, and report generation on bulk text. Use this skill for requests like 'analyze this text', 'text processing', 'classify documents', 'run sentiment analysis', 'extract keywords', 'named entity recognition', 'NER', 'text summarization', 'review analysis', 'survey text analysis', 'comment analysis', and other general text NLP tasks. Note: speech recognition (STT), machine translation, chatbot dialogue management, and LLM fine-tuning are outside the scope of this skill.
Text Processor — Full Text Processing Pipeline
An agent team collaborates to perform bulk text preprocessing, classification, extraction, sentiment analysis, structuring, and report generation.
Execution Mode
Agent Team — Five agents communicate directly via SendMessage and perform cross-validation.
Agent Composition
| Agent | File | Role | Type |
|---|---|---|---|
| preprocessor | .claude/agents/preprocessor.md | Text preprocessing, noise removal | general-purpose |
| classifier | .claude/agents/classifier.md | Topic/intent classification, tagging | general-purpose |
| extractor | .claude/agents/extractor.md | Entity, keyword, relation, summary extraction | general-purpose |
| sentiment-analyzer | .claude/agents/sentiment-analyzer.md | Sentiment/emotion/opinion analysis | general-purpose |
| report-writer | .claude/agents/report-writer.md | Final report, quality assurance | general-purpose |
Workflow
Phase 1: Preparation (performed directly by the orchestrator)
- Extract the following from user input:
- Text source: File path, format, document count, language
- Analysis objective: What the user wants to learn (classification, sentiment, keywords, etc.)
- Domain information (optional): Industry, text type (reviews/news/social media/documents)
- Classification taxonomy (optional): User-defined classification categories
- Create the
_workspace/directory and the_workspace/structured_data/subdirectory - Organize the input and save it to
_workspace/00_input.md - If pre-existing files are available, copy them to
_workspace/and skip the corresponding phase - Determine the execution mode based on the scope of the request
Phase 2: Team Assembly and Execution
| Order | Task | Owner | Dependencies | Deliverable |
|---|---|---|---|---|
| 1 | Preprocessing | preprocessor | None | 01_preprocessing_result.md |
| 2a | Classification | classifier | Task 1 | 02_classification_result.md |
| 2b | Extraction | extractor | Task 1 | 03_extraction_result.md |
| 3 | Sentiment analysis | sentiment-analyzer | Tasks 1, 2a, 2b | 04_sentiment_result.md |
| 4 | Report | report-writer | Tasks 2a, 2b, 3 | 05_final_report.md |
Tasks 2a (classification) and 2b (extraction) run in parallel. Sentiment analysis leverages classification and extraction results to improve aspect-level analysis accuracy.
Inter-agent communication flow:
- preprocessor completes > passes cleaned text and metadata to classifier, extractor, and sentiment-analyzer
- classifier completes > passes topic classification results to extractor (for topic-specific extraction) and sentiment-analyzer
- extractor completes > passes entity lists to sentiment-analyzer (for entity-level sentiment analysis)
- sentiment-analyzer completes > passes results to report-writer
- report-writer cross-validates all deliverables; requests corrections from the relevant agent if discrepancies are found (up to 2 rounds)
Phase 3: Integration and Final Deliverables
- Verify all files in
_workspace/and thestructured_data/directory - Confirm that all required corrections have been incorporated into the report
- Present the final summary to the user
Execution Modes by Request Scope
| User Request Pattern | Execution Mode | Agents Deployed |
|---|---|---|
| "Analyze this text", "full pipeline" | Full pipeline | All 5 agents |
| "Just classify", "categorize" | Classification mode | preprocessor + classifier |
| "Sentiment analysis only", "review sentiment" | Sentiment mode | preprocessor + sentiment-analyzer |
| "Extract keywords", "named entity recognition" | Extraction mode | preprocessor + extractor |
| "Summarize", "text summary" | Summary mode | preprocessor + extractor (summary function) |
| "Write a report" (existing analyses available) | Report mode | report-writer only |
Reusing existing files: If the user provides pre-processed text or existing classification results, copy those files to the appropriate location in _workspace/ and skip the corresponding agent.
Show full SKILL.md (286 more words)Show less
Data Transfer Protocol
| Strategy | Method | Purpose |
|---|---|---|
| File-based | _workspace/ directory | Markdown deliverables |
| Structured data | _workspace/structured_data/ | JSON/CSV data for programmatic use |
| Message-based | SendMessage | Key information transfer, correction requests |
Error Handling
| Error Type | Strategy |
|---|---|
| Encoding errors | Auto-detect with chardet > force UTF-8 conversion, log losses |
| Large text volumes (>100K documents) | Batch processing; analyze a sample first, then apply to full dataset |
| Mixed languages | Separate into language-specific segments and process individually |
| NER domain mismatch | Supplement with pattern-based extraction; propose custom dictionary creation |
| Agent failure | Retry once; if still failing, proceed without that deliverable |
| Report discrepancy found | Request correction from the relevant agent (up to 2 rounds) |
Test Scenarios
Normal Flow
Prompt: "Analyze 1,000 customer reviews and extract product-level satisfaction and complaints" Expected result:
- Preprocessing: Normalize review text, remove duplicates, compute statistics
- Classification: Classify by product category and review intent (praise/complaint/inquiry/suggestion)
- Extraction: Product names, feature names, key keywords, per-review summaries
- Sentiment: Overall sentiment distribution, product- and feature-level sentiment (ABSA), complaint patterns
- Report: Product satisfaction rankings, top 5 complaints, improvement recommendations
Existing File Reuse Flow
Prompt: "I already have preprocessed text data; just run sentiment analysis" + preprocessed file attached Expected result:
- Copy existing preprocessing results to
_workspace/01_preprocessing_result.md - Sentiment mode: Skip preprocessor, deploy only sentiment-analyzer
- Do not deploy classifier, extractor, or report-writer
Error Flow
Prompt: "Analyze the comments in this CSV file" (mixed languages, many emojis, short text) Expected result:
- preprocessor separates text by language, determines emoji handling strategy
- classifier flags reduced confidence for short text classification
- sentiment-analyzer leverages emoji sentiment information
- report-writer documents the analytical limitations of multilingual and short text in the report
Agent Extension Skills
| Skill | Path | Enhanced Agent | Role |
|---|---|---|---|
| nlp-preprocessing-toolkit | .claude/skills/nlp-preprocessing-toolkit/skill.md | preprocessor, extractor | Tokenization, morphological analysis, embedding selection, vectorization |
| sentiment-lexicon-builder | .claude/skills/sentiment-lexicon-builder/skill.md | sentiment-analyzer | Sentiment lexicon construction, ABSA, negation/intensity correction, emoji mapping |
© revfactory, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in en/33-text-processor/.claude/skills/text-processor of revfactory/harness-100.
Open the folder on GitHubat commit 8e8d35c
Compare with similar skills
Text Processor 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Text Processor this skillrevfactory/harness-100 | 1.3k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Transformersynulihao/AgentSkillOS | 617 | — | ~2.9k | Automated safety check: Pass | None | |
| Natural Languagedpearson2699/swift-ios-skills | 1.2k | 1 repos | ~3.5k | Automated safety check: Pass | Custom licence | |
| Lilly Community Researchssaaffaakk/Lilly | 171 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Hugging Face Transformers Usagedavila7/claude-code-templates | 32k | 12 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Running Zeroshot Nermaziyarpanahi/openmed | 5.5k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 |
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Hugging Face Transformers Usage
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Categories
Questions about Text Processor
What does Text Processor do?
Text processing pipeline: an agent team collaborates to perform preprocessing, classification, entity/keyword extraction, sentiment analysis, summarization, structured data conversion, and report…. Text Processor is an agent skill from revfactory/harness-100. Text processing pipeline: an agent team collaborates to perform preprocessing, classification, entity/keyword extraction, sentiment analysis, summarization, structured data conversion, and report generation on bulk text.
When should I use Text Processor?
Text Processor fits situations like: requests like analyze this text; text processing; classify documents; run sentiment analysis.
How do I install Text Processor in Claude Code?
Run `npx skills add revfactory/harness-100 --skill text-processor -a claude-code`. Or copy the skill folder (en/33-text-processor/.claude/skills/text-processor in revfactory/harness-100) into .claude/skills/text-processor in your project. Claude Code loads it when a task matches its description.
How do I install Text Processor in Codex?
Run `npx skills add revfactory/harness-100 --skill text-processor -a codex`. Or copy the skill folder (en/33-text-processor/.claude/skills/text-processor in revfactory/harness-100) into .agents/skills/text-processor in your project. Codex loads it when a task matches its description.
Can I use Text Processor 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 revfactory/harness-100 --skill text-processor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/text-processor, .gemini/skills/text-processor, .github/skills/text-processor and .opencode/skills/text-processor in your project.
What does Text Processor need to run?
SKILL.md names no scripts, command-line tools or credentials: Text Processor is instructions for the agent only.
Does Text Processor 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 Text Processor 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 Text Processor use?
Text Processor is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Text Processor use?
About 1.9k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Text Processor?
Skills that share tags, products or a category with Text Processor: Transformers (ynulihao/AgentSkillOS, 617 stars), Natural Language (dpearson2699/swift-ios-skills, 1.2k stars), Lilly Community Research (ssaaffaakk/Lilly, 171 stars) and Hugging Face Transformers Usage (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Text Processor?
revfactory (a GitHub user) maintains it in revfactory/harness-100, which has 1,290 GitHub stars. The repository holds 464 skills in this directory. The repository was last updated on March 22, 2026.
Source: revfactory/harness-100 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.