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 K-Dense-AI/drug-discovery-agent-skills --skill open-targetsgit clone --depth 1 https://github.com/K-Dense-AI/drug-discovery-agent-skillsWrote 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/k-dense-ai/drug-discovery-agent-skills/open-targets)<a href="https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/open-targets"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/open-targets/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/k-dense-ai/drug-discovery-agent-skills/open-targets"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/open-targets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 24 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 26 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00124 | $0.02466 |
| Opus 5 | $0.00062 | $0.01233 |
| Sonnet 5 | $0.00025 | $0.00493 |
| Haiku 4.5 | $0.00012 | $0.00247 |
Grade A, and why
open-targets 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open Targets Platform
Open Targets aggregates genetic, somatic, clinical, pathway, expression, animal-model, and literature evidence into scored target–disease associations, and attaches druggability and safety annotation to every target. It answers the question that comes before any modelling work: is this target worth working on for this disease, and what is already known about it?
Endpoint: https://api.platform.opentargets.org/api/v4/graphql — POST, JSON, no key.
Docs: platform-docs.opentargets.org ·
playground
Checked against: the live API, August 2026 — meta reports API 26.6.3, data release 26.06.
Read references/graphql-schema.md before writing a query by hand, references/datasources.md before interpreting or filtering a score, and references/query-cookbook.md for tested documents to adapt.
Start here: three identifier rules
Everything else fails downstream of getting these wrong.
- Targets are Ensembl gene ids (
ENSG00000146648) — never symbols, UniProt accessions, or transcript ids. - Diseases are MONDO ids (
MONDO_0005233) in almost all cases, even though the argument is still namedefoId. MostEFO_*ids from older tutorials now returnnullsilently. A few nodes legitimately keepEFO_,HP_, orOTAR_ids, so you cannot rewrite the prefix — resolve the name and use what comes back. - Drugs are ChEMBL molecule ids (
CHEMBL939).
Always resolve first:
python skills/open-targets/scripts/ot_query.py resolve EGFR "non-small cell lung carcinoma" gefitinib
term id name entity score
EGFR ENSG00000146648 EGFR target 1
non-small cell lung carcinoma MONDO_0005233 non-small cell lung carcinoma disease 1
gefitinib CHEMBL2087361 ICOTINIB drug 1
gefitinib CHEMBL553 ERLOTINIB drug 1
gefitinib CHEMBL939 GEFITINIB drug 1
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 196 lines · 124 tokens per session scan A 12d0da2b3bc6
open-targets is a skill published in the GitHub repository K-Dense-AI/drug-discovery-agent-skills (28 stars, last pushed 5d ago), licensed MIT. It adds 124 tokens to every session and 2,466 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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