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aipoch-ai

Abstract Trimmer

by AIpoch · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install abstract-trimmer
Description
Compress academic abstracts to meet strict word limits while preserving key information, scientific accuracy, and readability. Supports multiple compression...
README (SKILL.md)

Abstract Trimmer

Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.

Features

  • Smart Compression: Multiple strategies (aggressive, conservative, balanced)
  • Key Information Preservation: Retains critical findings and statistics
  • Structural Integrity: Maintains Background-Methods-Results-Conclusion flow
  • Quantitative Safety: Protects numbers, P-values, and confidence intervals
  • Batch Processing: Trim multiple abstracts efficiently
  • Quality Validation: Post-trim readability and accuracy checks

Usage

Basic Usage

# Trim abstract from file
python scripts/main.py --input abstract.txt --target 250

# Trim abstract from command line
python scripts/main.py --text "Your abstract here..." --target 200

# Check word count only
python scripts/main.py --input abstract.txt --target 250 --check-only

Parameters

Parameter Type Default Required Description
--input, -i str None No Input file containing abstract
--text, -t str None No Abstract text (alternative to --input)
--target, -T int 250 No Target word count
--strategy, -s str balanced No Trimming strategy (conservative/balanced/aggressive)
--output, -o str None No Output file path
--check-only, -c flag False No Only check word count without trimming
--format str json No Output format (json/text)

Advanced Usage

# Aggressive trimming with text output
python scripts/main.py \
  --input abstract.txt \
  --target 200 \
  --strategy aggressive \
  --format text \
  --output trimmed.txt

# Batch check multiple abstracts
for file in *.txt; do
  python scripts/main.py --input "$file" --target 250 --check-only
done

Trimming Strategies

Strategy Approach Best For
Conservative Remove filler words, simplify sentences Minor trims (10-20 words)
Balanced Condense phrases, merge sentences Moderate trims (20-50 words)
Aggressive Remove secondary details, abbreviate Major trims (50+ words)

Output Format

JSON Output

{
  "trimmed_abstract": "Compressed abstract text...",
  "original_words": 320,
  "final_words": 248,
  "reduction_percent": 22.5
}

Text Output

Compressed abstract text...

Technical Difficulty: LOW

⚠️ AI自主验收状态: 需人工检查

This skill requires:

  • Python 3.7+ environment
  • No external dependencies

Dependencies

Required Python Packages

pip install -r requirements.txt

Requirements File

No external dependencies required (uses only Python standard library).

Risk Assessment

Risk Indicator Assessment Level
Code Execution Python scripts executed locally Low
Network Access No network access Low
File System Access Read/write text files only Low
Instruction Tampering Standard prompt guidelines Low
Data Exposure No sensitive data exposure Low

Security Checklist

  • No hardcoded credentials or API keys
  • No unauthorized file system access (../)
  • Output does not expose sensitive information
  • Prompt injection protections in place
  • Input file paths validated
  • Output directory restricted to workspace
  • Script execution in sandboxed environment
  • Error messages sanitized
  • Dependencies audited

Prerequisites

# No dependencies required
python scripts/main.py --help

Evaluation Criteria

Success Metrics

  • Successfully trims abstracts to target word count
  • Preserves key scientific information
  • Maintains grammatical correctness
  • Handles edge cases gracefully

Test Cases

  1. Basic Trimming: Input abstract → Trimed to target word count
  2. Check Mode: --check-only flag → Reports word count statistics
  3. File I/O: Read from file, write to file → Correct file handling
  4. Different Strategies: All three strategies work → Different compression levels

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-15
  • Known Issues: None
  • Planned Improvements:
    • Enhanced protection for quantitative data
    • Support for structured abstracts
    • Batch processing mode

References

See references/ for:

  • Compression strategies documentation
  • Protected elements guidelines
  • Journal word limits by publisher

Limitations

  • Language: Optimized for English academic abstracts
  • Content Type: Designed for structured abstracts (BMRC format)
  • No Rewriting: Only removes/compresses; doesn't rephrase
  • Final Review: Automated trimming requires human validation

✂️ Remember: This tool helps meet word limits, but never sacrifice scientific accuracy. Always validate that trimmed abstracts maintain the integrity of your findings.

Usage Guidance
This skill appears to do what it claims: a small local Python script that trims abstracts. Before installing/using: - Review the source (scripts/main.py) yourself if you will process sensitive or unpublished abstracts — the script will open any file path you provide and write output to the path you specify. It does not enforce sandboxing or extra path sanitization. - SKILL.md contains a minor contradiction: it shows pip install -r requirements.txt but no requirements.txt is included and the script uses only the Python standard library. Confirm there are no hidden dependencies before running pip commands. - The trimming is regex- and heuristic-based. Test thoroughly: automated removals can accidentally drop nuance or important phrasing (especially in Methods/Results). Always perform a human review of any trimmed abstract before submission. - If you need stronger guarantees (no network I/O, stronger path validation, or stricter preservation of numeric/statistical content), run the script in an isolated environment (VM/container) and inspect or modify the code to add explicit validations/logging. Overall risk is low and coherent with the stated purpose, but verify the few documentation claims and run human validation on outputs.
Capability Analysis
Type: OpenClaw Skill Name: abstract-trimmer Version: 0.1.0 The skill is a legitimate text processing tool designed to compress academic abstracts using regex-based phrase removal and word counting. Analysis of the Python script (scripts/main.py) and documentation (SKILL.md) shows no evidence of malicious intent, network activity, data exfiltration, or dangerous execution patterns; it operates entirely on local text input and output as described.
Capability Assessment
Purpose & Capability
Name/description (abstract trimming) matches the included Python script and usage examples. The script implements regex-based trimming strategies and file/stdin I/O consistent with the stated features (conservative/balanced/aggressive, check-only, JSON/text output).
Instruction Scope
Runtime instructions direct running the local Python script with input via file, stdin, or a CLI flag and writing output to a file or stdout — scope remains limited to user-provided text and files. However, the SKILL.md asserts protections (e.g., input validation, sandboxing, no network access) that are not enforced or implemented in the code; the code performs no additional validation beyond opening the provided input path and could read any file path the user supplies.
Install Mechanism
No install spec or third-party downloads are present; the skill is instruction-only plus a local script. Nothing is downloaded or executed from external URLs. This is a low-risk install surface.
Credentials
The skill requests no environment variables, credentials, or config paths. The code does not access network, secrets, or other environment variables. Required privileges are limited to standard file read/write operations as directed by the user.
Persistence & Privilege
The skill is not always-enabled and does not request persistent privileges or modify other skills or global agent configuration. It runs only when invoked and does not autonomously persist state.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install abstract-trimmer
  3. After installation, invoke the skill by name or use /abstract-trimmer
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release of Abstract Trimmer – an academic abstract compression tool. - Compresses abstracts to specific word limits while preserving key information and scientific accuracy. - Supports multiple trimming strategies (conservative, balanced, aggressive) for flexible compression. - Maintains abstract structure (Background-Methods-Results-Conclusion) and safeguards quantitative data. - Batch processing and quality validation features included. - Works via command line with no external Python dependencies.
Metadata
Slug abstract-trimmer
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Abstract Trimmer?

Compress academic abstracts to meet strict word limits while preserving key information, scientific accuracy, and readability. Supports multiple compression... It is an AI Agent Skill for Claude Code / OpenClaw, with 218 downloads so far.

How do I install Abstract Trimmer?

Run "/install abstract-trimmer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Abstract Trimmer free?

Yes, Abstract Trimmer is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Abstract Trimmer support?

Abstract Trimmer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Abstract Trimmer?

It is built and maintained by AIpoch (@aipoch-ai); the current version is v0.1.0.

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