Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add beita6969/ScienceClaw --skill opentargets-databasegit clone --depth 1 https://github.com/beita6969/ScienceClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/beita6969/scienceclaw/opentargets-database)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/opentargets-database"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/opentargets-database/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/beita6969/scienceclaw/opentargets-database"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/opentargets-database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.03237 |
| Opus 5 | $0.00020 | $0.01618 |
| Sonnet 5 | $0.00008 | $0.00647 |
| Haiku 4.5 | $0.00004 | $0.00324 |
Grade A, and why
opentargets-database scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to opentargets-database — 50 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open Targets Database
Overview
The Open Targets Platform is a comprehensive resource for systematic identification and prioritization of potential therapeutic drug targets. It integrates publicly available datasets including human genetics, omics, literature, and chemical data to build and score target-disease associations.
Key capabilities:
- Query target (gene) annotations including tractability, safety, expression
- Search for disease-target associations with evidence scores
- Retrieve evidence from multiple data types (genetics, pathways, literature, etc.)
- Find known drugs for diseases and their mechanisms
- Access drug information including clinical trial phases and adverse events
- Evaluate target druggability and therapeutic potential
Data access: The platform provides a GraphQL API, web interface, data downloads, and Google BigQuery access. This skill focuses on the GraphQL API for programmatic access.
When to Use This Skill
This skill should be used when:
- Target discovery: Finding potential therapeutic targets for a disease
- Target assessment: Evaluating tractability, safety, and druggability of genes
- Evidence gathering: Retrieving supporting evidence for target-disease associations
- Drug repurposing: Identifying existing drugs that could be repurposed for new indications
- Competitive intelligence: Understanding clinical precedence and drug development landscape
- Target prioritization: Ranking targets based on genetic evidence and other data types
- Mechanism research: Investigating biological pathways and gene functions
- Biomarker discovery: Finding genes differentially expressed in disease
- Safety assessment: Identifying potential toxicity concerns for drug targets
Core Workflow
1. Search for Entities
Start by finding the identifiers for targets, diseases, or drugs of interest.
For targets (genes):
from scripts.query_opentargets import search_entities
# Search by gene symbol or name
results = search_entities("BRCA1", entity_types=["target"])
# Returns: [{"id": "ENSG00000012048", "name": "BRCA1", ...}]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 373 lines · 40 tokens per session scan A a307922ca59f
opentargets-database is a skill published in the GitHub repository beita6969/ScienceClaw (896 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 3,237 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to opentargets-database, differing in 50 lines, and is treated as a copy.
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