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Skills conference-abstract-adaptor

Adapt abstracts to meet specific conference word limits and formats

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/aipoch-ai/conference-abstract-adaptor" ~/.claude/skills/openclaw-skills-conference-abstract-adaptor && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/aipoch-ai/conference-abstract-adaptor" ~/.openclaw/skills/openclaw-skills-conference-abstract-adaptor && rm -rf "$T"
manifest: skills/aipoch-ai/conference-abstract-adaptor/SKILL.md
tags
#text-formatting#word-count#conference#abstract#automation
source content

Conference Abstract Adaptor

Conference-specific abstract formatting.

Use Cases

  • Multi-conference submissions
  • Word count compliance
  • Format standardization
  • Deadline management

Parameters

ParameterTypeDefaultRequiredDescription
--abstract
, 
-a
string-YesAbstract text file path
--conference
, 
-c
string-YesTarget conference (ASGCT, ASCO, SfN, AACR, ASM)
--output
, 
-o
string-NoOutput file path
--list-conferences
, 
-l
flag-NoList 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

ConferenceWord LimitFormat
ASGCT250 wordsStructured (Background/Methods/Results/Conclusion)
ASCO260 wordsStructured (Background/Methods/Results/Conclusion)
SfN2000 charsSingle abstract
AACR300 wordsStructured (Background/Methods/Results/Conclusion)
ASM300 wordsSingle abstract

Returns

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

Risk Assessment

Risk IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

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

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. 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