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/thesecondfox/skill/bindingdb-databasenpx skills add thesecondfox/skill --skill bindingdb-databasegit clone --depth 1 https://github.com/thesecondfox/skillWhat 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 | $0.00068 | $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 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.
response = requests.get(url, params=params, headers={"Accept": "application/json"}) 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.
- yesterday First seen · 333 lines · 68 tokens per session scan A af78f5c9ce34
bindingdb-database is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 4mo 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). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…