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Skills ihc-if-optimizer

Optimize IHC/IF protocols for specific tissues and antigens

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 "$T/skills/aipoch-ai/ihc-if-optimizer" ~/.claude/skills/openclaw-skills-ihc-if-optimizer && rm -rf "$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 "$T/skills/aipoch-ai/ihc-if-optimizer" ~/.openclaw/skills/openclaw-skills-ihc-if-optimizer && rm -rf "$T"
manifest: skills/aipoch-ai/ihc-if-optimizer/SKILL.md
tags
#wet-lab#protocol-optimization#immunostaining#antibody-dilution#tissue-analysis
source content

IHC/IF Optimizer

Immunostaining protocol optimization.

Use Cases

  • Brain tissue staining
  • Liver antigen retrieval
  • Antibody dilution optimization
  • Fluorescence panel design

Parameters

ParameterTypeDefaultRequiredDescription
--tissue-type
string-YesTissue type (Brain, Liver, Kidney, etc.)
--antigen
string-YesTarget protein/antigen name
--detection-method
stringIHCNoDetection method (IHC or IF)
--output
, 
-o
stringstdoutNoOutput file path
--format
stringtextNoOutput format (text, json, markdown)

Returns

  • Recommended retrieval method
  • Antibody dilutions
  • Blocking conditions
  • Counterstain suggestions

Example

Brain tissue + Phospho-protein → Citrate retrieval, 1:200 antibody

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