nano-banana-pro-prompts-recommend-skill
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Submit research questions for AI-powered analysis via Clarity Protocol. Use when the user asks to analyze a protein variant, ask a research question, get AI analysis of a mutation, or query Clarity's aggregated data sources. Requires CLARITY_WRITE_API_KEY. Capabilities: submit analysis questions, get AI answers grounded in 7 data sources.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install clarityprotocol/clarity-analyzeInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Submit research questions to Clarity Protocol's AI analysis engine. Questions are answered using data from 7 aggregated sources: fold data, ClinVar, gnomAD, PubMed literature, Open Targets, agent findings, and agent annotations.
Ask a research question:
python scripts/ask_question.py --question "What is the clinical significance of SOD1 A4V?"
Ask about a specific variant:
python scripts/ask_question.py \
--question "How does this mutation affect protein stability?" \
--variant-id 1 \
--focus clinical literature
Get plain text answer (no JSON wrapper):
python scripts/ask_question.py \
--question "What is the clinical significance of SOD1 A4V?" \
--format text
The analysis engine draws from:
Prioritize specific data sources in the analysis:
clinical — ClinVar, gnomAD dataliterature — PubMed papersstructural — AlphaFold predictionsfunctional — Open Targets, agent findingsexport CLARITY_WRITE_API_KEY=your_write_key_here
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# Clarity Analyze Skill Submit research questions to Clarity Protocol's AI analysis engine. Questions are answered using data from 7 aggregated sources: fold data, ClinVar, gnomAD, PubMed literature, Open Targets, agent findings, and agent annotations. ## Quick Start Ask a research question: ```bash python scripts/ask_question.py --question "What is the clinical significance of SOD1 A4V?" ``` Ask about a specific variant: ```bash python scripts/ask_question.py \ --question "How does this mutation affect protein stability?" \ --variant-id 1 \ --focus clinical literature ``` Get plain text answer (no JSON wrapper): ```bash python scripts/ask_question.py \ --question "What is the clinical significance of SOD1 A4V?" \ --format text ``` ## Data Sources The analysis engine draws from: 1. **Fold data** — AlphaFold structure predictions, confidence scores 2. **Clinical data** — ClinVar pathogenicity, gnomAD allele frequency 3. **Literature** — PubMed papers and citations 4. **Structural analysis** — AlphaFold structural predictions 5. **Open Targets** — Disease-gene associations 6. **Agent findings** — Research agent discoveries 7. **Agent annotations** — Community observations ## Focus Options Prioritize specific data sources in the analysis: - `clinical` — ClinVar, gnomAD data - `literature` — PubMed papers - `structural` — AlphaFold predictions - `functional` — Open Targets, agent findings ## Authentication ```bash export CLARITY_WRITE_API_KEY=your_write_key_here ``` ## Rate Limits - **Analysis requests**: 10 per day (per API key) - Identical questions return cached responses (7-day TTL) ## Error Handling - **403 Forbidden**: Invalid or missing write API key - **404 Not Found**: Specified variant does not exist - **422 Validation Error**: Question is off-topic (must be about protein research)
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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