omics-target-evidence-mapper
Aggregate public target-level evidence across omics and translational sources for research triage.
Best use case
omics-target-evidence-mapper is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Aggregate public target-level evidence across omics and translational sources for research triage.
Teams using omics-target-evidence-mapper should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/omics-target-evidence-mapper/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How omics-target-evidence-mapper Compares
| Feature / Agent | omics-target-evidence-mapper | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Aggregate public target-level evidence across omics and translational sources for research triage.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Why This Exists Researchers often need a quick first-pass view of whether a gene or protein target has evidence across multiple public sources. In practice, this usually means checking several websites manually and informally combining results. This skill makes that process reproducible by retrieving and organising public evidence into one structured output. This skill is for research triage only. It does not infer causality, rank therapeutic value, or make clinical recommendations. # Core Capabilities 1. Accept a gene or protein target and an optional disease term. 2. Retrieve canonical target information from UniProt. 3. Retrieve disease-target association evidence from Open Targets. 4. Retrieve recent literature hits from PubMed. 5. Optionally retrieve trial records relevant to the target and disease. 6. Produce a machine-readable JSON file and a human-readable Markdown report. # Input Formats | Argument | Required | Example | Notes | |---|---|---|---| | `--gene` | Yes, unless `--demo` is used | `IL6R` | Gene or target symbol | | `--disease` | No | `coronary artery disease` | Optional disease context | | `--output` | Yes | `demo_out` | Output directory | | `--max-papers` | No | `5` | Number of PubMed hits to include | | `--max-trials` | No | `5` | Number of trial records to include | | `--demo` | No | `--demo` | Runs the built-in demo query | # Workflow 1. Validate CLI inputs. 2. Resolve the query from either user input or demo mode. 3. Query public data sources. 4. Normalise results into a structured evidence object. 5. Write JSON and Markdown outputs. 6. Write file checksums for reproducibility. # CLI Reference ## Demo mode ```bash python skills/omics-target-evidence-mapper/omics_target_evidence_mapper.py --demo --output demo_out
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