Agent skill · Databases

turingdb-graph

Build, query, and analyse biomedical knowledge graphs in TuringDB, a columnar graph database with git-like versioning.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill turingdb-graph --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 14
SKILL.md size: 17 KB
Bundled scripts: yes
Version: 0.1.0
Declared author: TuringDB <team@turingdb.ai>
Path: skills/clawbio/turingdb-graph/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 🕸 TuringDB Graph You are **TuringDB Graph**, a specialised ClawBio agent for building, querying, and analysing biomedical knowledge graphs in TuringDB — a columnar graph database with git-like versioning. ## Trigger **Fire this skill when the user says any of:** - "build a knowledge graph from this CSV" - "load this GML into a graph database" - "run a Cypher query against my graph" - "analyse my patient cohort graph" - "show me the top conditions and medications" - "comorbidity analysis" / "comedication analysis" - "graph database for biomedical data" - "run the TuringDB demo" - "pathway graph" / "antibody graph" **Do NOT fire when:** - The user wants a Neo4j or Neptune query — this skill targets TuringDB only - The user wants statistical inference (p-values, odds ratios, survival curves) — this skill does descriptive counts only - The user wants vocabulary normalisation (ATC, ICD, SNOMED, HGNC) — out of scope ## Why This Exists - **Without it**: building a biomedical graph from flat files requires hand-writing Cypher, managing the TuringDB daemon lifecycle, and assembling cohort analytics from scratch. - **With it**: a single CLI call ingests a CSV/GML/JSONL into a versioned gr

What's inside
Steps it walks through
  1. Trigger
  2. Why This Exists
  3. Core Capabilities
  4. Scope
  5. Input Formats
  6. Workflow
  7. CLI Reference
  8. Global flags
  9. Demo
  10. Example Queries
  11. Example Output
  12. Output Structure
  13. Algorithm / Methodology
  14. Cohort analyses
Ships with 13 files
  • demo/antibody.csv
  • demo/cohort.csv
  • demo/pathway.gml
  • http_server.py
  • reference/algorithms.md
  • reference/biomedical.md
  • reference/introspection.md
  • reference/querying.md
  • reference/startup.md
  • reference/writing.md
  • requirements.txt
  • tests/test_turingdb_graph.py
  • turingdb_graph.py
Commands it runs
Build a graph from CSV
python skills/turingdb-graph/turingdb_graph.py \
Build from GML
Run a Cypher query
Analyse a patient cohort
Run a demo (auto-starts TuringDB if needed)
python skills/turingdb-graph/turingdb_graph.py --demo cohort --out /tmp/demo
python skills/turingdb-graph/turingdb_graph.py --demo pathway --out /tmp/demo
python skills/turingdb-graph/turingdb_graph.py --demo antibody --out /tmp/demo
Stop the TuringDB daemon
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About this skill
What does the turingdb-graph skill do?

Build, query, and analyse biomedical knowledge graphs in TuringDB, a columnar graph database with git-like versioning.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill turingdb-graph --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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