Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
When to Use
Use this skill when the task needs Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.
Key Features
See ## Features above for related details.
Scope-focused workflow aligned to: Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
Packaged executable path(s): scripts/main.py.
Reference material available in references/ for task-specific guidance.
Related Skills
Structured execution path designed to keep outputs consistent and reviewable.
Dependencies
Example Usage
See ## Usage above for related details.
cd "20260318/scientific-skills/Academic Writing/abstract-trimmer"
python -m py_compile scripts/main.py
python scripts/main.py --help
Example run plan:
Confirm the user input, output path, and any required config values.
Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
Run python scripts/main.py with the validated inputs.
Review the generated output and return the final artifact with any assumptions called out.
Implementation Details
See ## Workflow above for related details.
Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
Primary implementation surface: scripts/main.py.
Reference guidance: references/ contains supporting rules, prompts, or checklists.
Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.
Quick Check
Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
Audit-Ready Commands
Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --text "Background Methods Results Conclusion blood pressure improved with lifestyle coaching over 12 weeks." --target 40
python scripts/main.py --text "Brief abstract for audit validation with measurable endpoints." --target 20 --check-only
Workflow
Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.
⚠️ AI independent acceptance status: manual inspection required
This skill requires:
Python 3.7+ environment
No external 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
Basic Trimming: Input abstract → Trimed to target word count
Check Mode: --check-only flag → Reports word count statistics
File I/O: Read from file, write to file → Correct file handling
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.
Output Requirements
Every final response should make these items explicit when they are relevant:
Objective or requested deliverable
Inputs used and assumptions introduced
Workflow or decision path
Core result, recommendation, or artifact
Constraints, risks, caveats, or validation needs
Unresolved items and next-step checks
Error Handling
If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
Do not fabricate files, citations, data, search results, or execution outcomes.
Input Validation
This skill accepts requests that match the documented purpose of abstract-trimmer and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
abstract-trimmer only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
Response Template
Use the following fixed structure for non-trivial requests:
Objective
Inputs Received
Assumptions
Workflow
Deliverable
Risks and Limits
Next Checks
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.