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 cyborg-garden/hermes-agent-mt --skill drug-discoverygit clone --depth 1 https://github.com/cyborg-garden/hermes-agent-mtWrote 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/cyborg-garden/hermes-agent-mt/drug-discovery)<a href="https://agentmods.dev/skills/cyborg-garden/hermes-agent-mt/drug-discovery"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/drug-discovery/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/cyborg-garden/hermes-agent-mt/drug-discovery"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/drug-discovery.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 | $0.00084 | $0.02804 |
| Opus 5 | $0.00042 | $0.01402 |
| Sonnet 5 | $0.00017 | $0.00561 |
| Haiku 4.5 | $0.00008 | $0.00280 |
Grade A, and why
drug-discovery scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
commands: [curl, python3] This is a copy
84% identical to drug-discovery — 36 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Drug Discovery & Pharmaceutical Research
You are an expert pharmaceutical scientist and medicinal chemist with deep knowledge of drug discovery, cheminformatics, and clinical pharmacology. Use this skill for all pharma/chemistry research tasks.
Core Workflows
1 — Bioactive Compound Search (ChEMBL)
Search ChEMBL (the world's largest open bioactivity database) for compounds by target, activity, or molecule name. No API key required.
# Search compounds by target name (e.g. "EGFR", "COX-2", "ACE")
TARGET="$1"
ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$TARGET")
curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q=${ENCODED}&format=json" \
| python3 -c "
import json,sys
data=json.load(sys.stdin)
targets=data.get('targets',[])[:5]
for t in targets:
print(f\"ChEMBL ID : {t.get('target_chembl_id')}\")
print(f\"Name : {t.get('pref_name')}\")
print(f\"Type : {t.get('target_type')}\")
print()
"
# Get bioactivity data for a ChEMBL target ID
TARGET_ID="$1" # e.g. CHEMBL203
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" \
| python3 -c "
import json,sys
data=json.load(sys.stdin)
acts=data.get('activities',[])
print(f'Found {len(acts)} activities (pChEMBL >= 6):')
for a in acts:
print(f\" Molecule: {a.get('molecule_chembl_id')} | {a.get('standard_type')}: {a.get('standard_value')} {a.get('standard_units')} | pChEMBL: {a.get('pchembl_value')}\")
"
# Look up a specific molecule by ChEMBL ID
MOL_ID="$1" # e.g. CHEMBL25 (aspirin)
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/${MOL_ID}?format=json" \
| python3 -c "
import json,sys
m=json.load(sys.stdin)
props=m.get('molecule_properties',{}) or {}
print(f\"Name : {m.get('pref_name','N/A')}\")
print(f\"SMILES : {m.get('molecule_structures',{}).get('canonical_smiles','N/A') if m.get('molecule_structures') else 'N/A'}\")
print(f\"MW : {props.get('full_mwt','N/A')} Da\")
print(f\"LogP : {props.get('alogp','N/A')}\")
print(f\"HBD : {props.get('hbd','N/A')}\")
print(f\"HBA : {props.get('hba','N/A')}\")
print(f\"TPSA : {props.get('psa','N/A')} Ų\")
print(f\"Ro5 violations: {props.get('num_ro5_violations','N/A')}\")
print(f\"QED : {props.get('qed_weighted','N/A')}\")
"
What ships with it
3 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.
- 9d ago First seen · 228 lines · 84 tokens per session scan A b7eb32a28367
drug-discovery is a skill published in the GitHub repository cyborg-garden/hermes-agent-mt (13 stars, last pushed 4d ago), licensed MIT. It adds 84 tokens to every session and 2,804 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 84% identical to drug-discovery, differing in 36 lines, and is treated as a copy.
Other skills, from other repositories
hermes-memory-providers
Install and configure Mnemosyne as a Hermes Agent memory provider — local SQLite with vector search, episodic consolidation, and temporal knowledge graphs.
mnemosyne
Persistent cross-session memory via Mnemosyne — store, recall, and consolidate facts, preferences, and context.
mnemosyne-memory-override
Hard rule override that forces Mnemosyne for all durable memory storage. The legacy memory tool is DEPRECATED for user preferences, credentials, and project conventions. Use memory ONLY for ephemeral session state.
suap-iff-api
Authenticate to and consume the SUAP IFF (Instituto Federal Fluminense) API v2 from the CLI — obtain the JWT access/refresh token via matricula+senha, then fetch student data (periodos letivos, dados do aluno, boletim). Use when the user has a suap.iff.edu.br matricula+senha and wants to extract academic data…
lattes-xml-projetos
Use when gerar, limpar ou inserir itens de bolsa/projetos de pesquisa (PARTICIPACAO-EM-PROJETO / PROJETO-DE-PESQUISA) em XML de importação do Currículo Lattes. Cobre a estrutura de ATUACOES-PROFISSIONAIS, a limpeza de caracteres para ISO-8859-1 e a validação por well-formedness com xmllint (a DTD antiga de 2004 em…
jupyter-live-kernel
Iterative Python via live Jupyter kernel (hamelnb).