text-summarizer

dkyazzentwatwa/chatgpt-skills · updated May 29, 2026

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$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill text-summarizer
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summary

Extractive summarization from long documents with flexible length control and batch processing.

  • Supports three algorithms (TextRank, LSA, frequency-based) with configurable language support
  • Control summary length by ratio, sentence count, or word count; optionally preserve original sentence order
  • Extract key points as bullet-point summaries alongside full-text summaries
  • Batch process multiple documents or entire directories with consistent parameters
  • Available as Python API or
skill.md

Text Summarizer

Create concise summaries from long text documents using extractive summarization. Identifies and extracts the most important sentences while preserving meaning.

Quick Start

from scripts.text_summarizer import TextSummarizer

# Summarize text
summarizer = TextSummarizer()
summary = summarizer.summarize(long_text, ratio=0.2)  # 20% of original
print(summary)

# Summarize file
summary = summarizer.summarize_file("article.txt", num_sentences=5)

Features

  • Extractive Summarization: Selects key sentences from original text
  • Length Control: By ratio, sentence count, or word count
  • Multiple Algorithms: TextRank, LSA, frequency-based
  • Key Points: Extract bullet-point summaries
  • Batch Processing: Summarize multiple documents
  • Preserve Structure: Maintains sentence order option

API Reference

Initialization

summarizer = TextSummarizer(
    method="textrank",    # textrank, lsa, frequency
    language="english"
)

Summarization

# By ratio (20% of original length)
summary = summarizer.summarize(text, ratio=0.2)

# By sentence count
summary = summarizer.summarize(text, num_sentences=5)

# By word count
summary = summarizer.summarize(text, max_words=100)

Key Points Extraction

# Get bullet points
points = summarizer.extract_key_points(text, num_points=5)
for point in points:
    print(f"• {point}")

Batch Processing

# Summarize multiple texts
texts = [text1, text2, text3]
summaries = summarizer.summarize_batch(texts, ratio=0.2)

# Summarize files in directory
summaries = summarizer.summarize_directory("./articles/", ratio=0.3)

Options

# Preserve original sentence order
summary = summarizer.summarize(text, preserve_order=True)

# Include title/first sentence
summary = summarizer.summarize(text, include_first=True)

# Minimum sentence length filter
summarizer.min_sentence_length = 10

CLI Usage

# Summarize text file
python text_summarizer.py --input article.txt --ratio 0.2

# Specific sentence count
python text_summarizer.py --input article.txt --sentences 5

# Extract key points
python text_summarizer.py --input article.txt --points 5

# Batch process
python text_summarizer.py --input-dir ./docs --output-dir ./summaries --ratio 0.3

# Output to file
python text_summarizer.py --input article.txt --output summary.txt --ratio 0.2

CLI Arguments

Argument Description Default
--input Input file path Required
--output Output file path stdout
--input-dir Directory of files -
--output-dir Output directory -
--ratio Summary ratio (0.0-1.0) 0.2
--sentences Number of sentences -
--words Maximum words -
--points Extract N key points -
--method Algorithm to use textrank
--preserve-order Keep sentence order False

Examples

News Article Summary

summarizer = TextSummarizer()

article = """
[Long news article text...]
"""

# Get a 3-sentence summary
summary = summarizer.summarize(article, num_sentences=3)
print("Summary:")
print(summary)

# Get key points
points = summarizer.extract_key_points(article, num_points=5)
print("\nKey Points:")
for i, point in enumerate(points, 1):
    print(f"{i}. {point}")

Research Paper Abstract

summarizer = TextSummarizer(method="lsa")

paper = open("research_paper.txt").read()

# Create abstract-length summary
abstract = summarizer.summarize(paper, max_words=250)
print(abstract)

Meeting Notes Summary

summarizer = TextSummarizer()

notes = """
Meeting started at 2pm. John presented Q3 results showing 15% growth.
Sarah raised concerns about supply chain delays affecting Q4 projections.
The team discussed mitigation strategies including dual-sourcing.
Budget allocation for marketing was approved at $50k.
Next steps include vendor outreach by Friday.
Follow-up meeting scheduled for next Tuesday.
"""

summary = summarizer.summarize(notes, num_sentences=3)
points = summarizer.extract_key_points(notes, num_points=4)

print("Summary:", summary)
print("\nAction Items:")
for point in points:
    print(f"• {point}")

Batch Document Summarization

summarizer = TextSummarizer()

import os
for filename in os.listdir("./documents"):
    if filename.endswith(".txt"):
        text = open(f"./documents/{filename}").read()
        summary = summarizer.summarize(text, ratio=0.2)

        with open(f"./summaries/{filename}", "w") as f:
            f.write(summary)

        print(f"Summarized: {filename}")

Algorithm Comparison

Algorithm Speed Quality Best For
TextRank Medium High General text
LSA Fast Good Technical docs
Frequency Fast Medium Quick summaries

Dependencies

nltk>=3.8.0
numpy>=1.24.0
scikit-learn>=1.2.0

Limitations

  • Extractive only (doesn't paraphrase or generate new text)
  • Works best with well-structured text (paragraphs, clear sentences)
  • Very short texts may not summarize well
  • Doesn't understand context deeply (may miss nuance)
how to use text-summarizer

How to use text-summarizer on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add text-summarizer
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill text-summarizer

The skills CLI fetches text-summarizer from GitHub repository dkyazzentwatwa/chatgpt-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/text-summarizer

Reload or restart Cursor to activate text-summarizer. Access the skill through slash commands (e.g., /text-summarizer) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.559 reviews
  • Xiao Sharma· Dec 28, 2024

    Solid pick for teams standardizing on skills: text-summarizer is focused, and the summary matches what you get after install.

  • Diya Taylor· Dec 16, 2024

    text-summarizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ava Huang· Dec 16, 2024

    We added text-summarizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Benjamin Taylor· Dec 8, 2024

    text-summarizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Hiroshi Ramirez· Dec 4, 2024

    I recommend text-summarizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kiara Khanna· Nov 27, 2024

    Registry listing for text-summarizer matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Yash Thakker· Nov 23, 2024

    text-summarizer has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kofi Ghosh· Nov 23, 2024

    Keeps context tight: text-summarizer is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ava Yang· Nov 7, 2024

    text-summarizer has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Diya Sethi· Nov 7, 2024

    Useful defaults in text-summarizer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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