Extractive summarization from long documents with flexible length control and batch processing.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontext-summarizerExecute the skills CLI command in your project's root directory to begin installation:
Fetches text-summarizer from dkyazzentwatwa/chatgpt-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate text-summarizer. Access via /text-summarizer in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
2
total installs
2
this week
43
GitHub stars
0
upvotes
Run in your terminal
2
installs
2
this week
43
stars
Create concise summaries from long text documents using extractive summarization. Identifies and extracts the most important sentences while preserving meaning.
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)
summarizer = TextSummarizer(
method="textrank", # textrank, lsa, frequency
language="english"
)
# 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)
# Get bullet points
points = summarizer.extract_key_points(text, num_points=5)
for point in points:
print(f"• {point}")
# 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)
# 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
# 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
| 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 |
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}")
summarizer = TextSummarizer(method="lsa")
paper = open("research_paper.txt").read()
# Create abstract-length summary
abstract = summarizer.summarize(paper, max_words=250)
print(abstract)
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}")
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 | Speed | Quality | Best For |
|---|---|---|---|
| TextRank | Medium | High | General text |
| LSA | Fast | Good | Technical docs |
| Frequency | Fast | Medium | Quick summaries |
nltk>=3.8.0
numpy>=1.24.0
scikit-learn>=1.2.0
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Solid pick for teams standardizing on skills: text-summarizer is focused, and the summary matches what you get after install.
text-summarizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added text-summarizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
text-summarizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend text-summarizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for text-summarizer matched our evaluation — installs cleanly and behaves as described in the markdown.
text-summarizer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: text-summarizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
text-summarizer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in text-summarizer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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