conference-abstract-adaptor

Adapt abstracts to meet specific conference word limits and formats

Install with Tessl CLI

npx tessl i github:aipoch/medical-research-skills --skill conference-abstract-adaptor

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61%

Review — 49%

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Validation — 10 / 11 Passed

Validation for skill structure

SKILL.md

Review

Evals

Conference Abstract Adaptor

Conference-specific abstract formatting.

Use Cases

Multi-conference submissions
Word count compliance
Format standardization
Deadline management

Parameters

Parameter	Type	Default	Required	Description
`--abstract`, `-a`	string	-	Yes	Abstract text file path
`--conference`, `-c`	string	-	Yes	Target conference (ASGCT, ASCO, SfN, AACR, ASM)
`--output`, `-o`	string	-	No	Output file path
`--list-conferences`, `-l`	flag	-	No	List supported conferences

Usage

# Adapt abstract for ASCO
python scripts/main.py --abstract my_abstract.txt --conference ASCO

# Save adapted abstract to file
python scripts/main.py --abstract my_abstract.txt --conference ASGCT --output adapted.txt

# List all supported conferences
python scripts/main.py --list-conferences

Supported Conferences

Conference	Word Limit	Format
ASGCT	250 words	Structured (Background/Methods/Results/Conclusion)
ASCO	260 words	Structured (Background/Methods/Results/Conclusion)
SfN	2000 chars	Single abstract
AACR	300 words	Structured (Background/Methods/Results/Conclusion)
ASM	300 words	Single abstract

Returns

Reformatted abstract
Word count verification
Required sections checklist
Submission-ready text

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	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 (no ../ traversal)
Output directory restricted to workspace
Script execution in sandboxed environment
Error messages sanitized (no stack traces exposed)
Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

Successfully executes main functionality
Output meets quality standards
Handles edge cases gracefully
Performance is acceptable

Test Cases

Basic Functionality: Standard input → Expected output
Edge Case: Invalid input → Graceful error handling
Performance: Large dataset → Acceptable processing time

Lifecycle Status

Current Stage: Draft
Next Review Date: 2026-03-06
Known Issues: None
Planned Improvements:
- Performance optimization
- Additional feature support

Repository: github.com/aipoch/medical-research-skills
Last updated: 3 days ago
Created: 3 days ago

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