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 agentmods add skills/docxology/template/connectorsnpx skills add docxology/template --skill connectorsgit clone --depth 1 https://github.com/docxology/templateWrote 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/docxology/template/connectors)<a href="https://agentmods.dev/skills/docxology/template/connectors"><img src="https://agentmods.dev/badge/skills/docxology/template/connectors.svg" alt="Measured on agentmods" 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 | $0.00087 | $0.01062 |
| Opus 5 | $0.00044 | $0.00531 |
| Sonnet 5 | $0.00017 | $0.00212 |
| Haiku 4.5 | $0.00009 | $0.00106 |
Grade A, and why
scientific-connectors 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 yesterday.
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.
`Connector` protocol. All connectors are stdlib-only (`urllib`), retry-safe, How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Connector Registry
Uniform discovery layer over eight science databases (OpenAlex, arXiv,
Semantic Scholar, CrossRef, Europe PMC, bioRxiv, UniProt, PDB) via the
Connector protocol. All connectors are stdlib-only (urllib), retry-safe,
and backed by an optional in-memory HTTP cache.
Quick Start
from infrastructure.search.connectors import (
ConnectorDomain,
get_registry,
list_connectors,
search_connector,
)
catalog = list_connectors()
biology_connectors = get_registry().by_domain(ConnectorDomain.biology)
hits = search_connector("openalex", "protein folding", max_results=10)
Fetch by ID
registry = get_registry()
protein = registry.get("uniprot").fetch("P12345")
structure = registry.get("pdb").fetch("4HHB")
Available Connectors
uv run python -m infrastructure.search.connectors list-dbs
| ID | Database | Domain |
|---|---|---|
openalex |
OpenAlex | literature |
arxiv |
arXiv | physics |
semantic_scholar |
Semantic Scholar | literature |
crossref |
CrossRef | literature |
europepmc |
Europe PMC | biology |
biorxiv |
bioRxiv | biology |
uniprot |
UniProt | proteomics |
pdb |
Protein Data Bank | structure |
CLI
# List all registered databases with their domains and descriptions
uv run python -m infrastructure.search.connectors list-dbs
# Search one connector
uv run python -m infrastructure.search.connectors search openalex "protein folding" --max-results 10
# Search all connectors (individual failures are reported as warnings)
uv run python -m infrastructure.search.connectors search --all "membrane" --max-results 5
Pipeline Orchestrator
# Run connector search for a named project
uv run python scripts/pipeline/stage_08_connector_search.py --project my_project
# One-off override that bypasses project connector_search configuration
uv run python scripts/pipeline/stage_08_connector_search.py \
--project my_project --connector arxiv --query "active inference" --max-results 5
What ships with it
22 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.
- __init__.py 4.0 KB runs code
- __main__.py 151 B runs code
- AGENTS.md 3.4 KB
- cli.py 3.3 KB runs code
- config.py 3.4 KB runs code
- http.py 7.1 KB runs code
- impl/__init__.py 1.2 KB runs code
- impl/AGENTS.md 2.0 KB
- impl/arxiv.py 4.6 KB runs code
- impl/base.py 1.3 KB runs code
- impl/biorxiv.py 4.0 KB runs code
- impl/crossref.py 4.2 KB runs code
- impl/europepmc.py 3.9 KB runs code
- impl/openalex.py 4.4 KB runs code
- impl/pdb.py 4.2 KB runs code
- impl/README.md 131 B
- impl/semantic_scholar.py 4.2 KB runs code
- impl/uniprot.py 3.8 KB runs code
- README.md 2.9 KB
- registry.py 4.1 KB runs code
- stage.py 12 KB runs code
- types.py 6.0 KB runs code
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.
- yesterday First seen · 142 lines · 87 tokens per session scan A 4711ae45c7b6
scientific-connectors is a skill published in the GitHub repository docxology/template (19 stars, last pushed today), licensed Apache-2.0. It adds 87 tokens to every session and 1,062 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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