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 nevergoodstudy-hub/wechat-article-summarizer --skill bindingdb-databasegit clone --depth 1 https://github.com/nevergoodstudy-hub/wechat-article-summarizerWrote 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/nevergoodstudy-hub/wechat-article-summarizer/bindingdb-database)<a href="https://agentmods.dev/skills/nevergoodstudy-hub/wechat-article-summarizer/bindingdb-database"><img src="https://agentmods.dev/badge/skills/nevergoodstudy-hub/wechat-article-summarizer/bindingdb-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/nevergoodstudy-hub/wechat-article-summarizer/bindingdb-database"><img src="https://agentmods.dev/badge/skills/nevergoodstudy-hub/wechat-article-summarizer/bindingdb-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 | $0.00068 | $0.03135 |
| Opus 5 | $0.00034 | $0.01568 |
| Sonnet 5 | $0.00014 | $0.00627 |
| Haiku 4.5 | $0.00007 | $0.00314 |
Grade A, and why
bindingdb-database 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 6d 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.
response = requests.get(url, params=params, headers={"Accept": "application/json"}) This is a copy
100% identical to bindingdb-database — 0 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 — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BindingDB Database
Overview
BindingDB (https://www.bindingdb.org/) is the primary public database of measured drug-protein binding affinities. It contains over 3 million binding data records for ~1.4 million compounds tested against ~9,200 protein targets, curated from scientific literature and patent literature. BindingDB stores quantitative binding measurements (Ki, Kd, IC50, EC50) essential for drug discovery, pharmacology, and computational chemistry research.
Key resources:
- BindingDB website: https://www.bindingdb.org/
- REST API: https://www.bindingdb.org/axis2/services/BDBService
- Downloads: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp
- GitHub: https://github.com/drugilsberg/bindingdb
When to Use This Skill
Use BindingDB when:
- Target-based drug discovery: What known compounds bind to a target protein? What are their affinities?
- SAR analysis: How do structural modifications affect binding affinity for a series of analogs?
- Lead compound profiling: What targets does a compound bind (selectivity/polypharmacology)?
- Benchmark datasets: Obtain curated protein-ligand affinity data for ML model training
- Repurposing analysis: Does an approved drug bind to an unintended target?
- Competitive analysis: What is the best reported affinity for a target class?
- Fragment screening: Find validated binding data for fragments against a target
Core Capabilities
1. BindingDB REST API
Base URL: https://www.bindingdb.org/axis2/services/BDBService
import requests
BASE_URL = "https://www.bindingdb.org/axis2/services/BDBService"
def bindingdb_query(method, params):
"""Query the BindingDB REST API."""
url = f"{BASE_URL}/{method}"
response = requests.get(url, params=params, headers={"Accept": "application/json"})
response.raise_for_status()
return response.json()
2. Query by Target (UniProt ID)
def get_ligands_for_target(uniprot_id, affinity_type="Ki", cutoff=10000, unit="nM"):
"""
Get all ligands with measured affinity for a UniProt target.
Args:
uniprot_id: UniProt accession (e.g., "P00519" for ABL1)
affinity_type: "Ki", "Kd", "IC50", "EC50"
cutoff: Maximum affinity value to return (in nM)
unit: "nM" or "uM"
"""
params = {
"uniprot_id": uniprot_id,
"affinity_type": affinity_type,
"affinity_cutoff": cutoff,
"response": "json"
}
return bindingdb_query("getLigandsByUniprotID", params)
# Example: Get all compounds binding ABL1 (imatinib target)
ligands = get_ligands_for_target("P00519", affinity_type="Ki", cutoff=100)
What ships with it
1 file 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.
- 6d ago First seen · 333 lines · 68 tokens per session scan A af78f5c9ce34
bindingdb-database is a skill published in the GitHub repository nevergoodstudy-hub/wechat-article-summarizer (5 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 3,135 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to bindingdb-database, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
results-report
A workflow for turning completed experiment analyses into a structured research report with findings, limitations, failures, and next steps.
knowledge-base-management
A lifecycle system for managing an Obsidian knowledge base, which is a folder of linked notes. It organizes raw material, AI-maintained wiki pages, and generated views into separate layers.
research-swarm
Turn any hypothesis, however fringe, into an argument map rather than a verdict: five independent lenses (consensus, skeptic, frontier, historian, experimental design), then a synthesis of for and against, evidence quality, a confidence tier and the cheapest decisive experiment. Triggers: "/research-swarm ", "map the…
healthmd-cli
Install and operate the standalone Health.md CLI and portable healthmd-mcp server on macOS, Linux, or Windows. Use when a user wants to pair an iPhone, configure Codex/Claude MCP, check direct readiness, query or chart typed health data, export Apple Health data, extract canonical JSON, manage durable jobs, automate…
healthmd-cli
Install and operate the standalone Health.md CLI and portable healthmd-mcp server on macOS, Linux, or Windows. Use when a user wants to pair an iPhone, configure Codex/Claude MCP, check direct readiness, query or chart typed health data, export Apple Health data, extract canonical JSON, manage durable jobs, automate…
healthmd-cli
Safely install and use the Health.md CLI and MCP server to query user-authorized health data, chart typed metrics, inspect sleep and workouts, export scoped Apple Health or Health Connect data, and recover durable jobs on macOS, Linux, or Windows. Use for consumer workflows, not Health.md development.