Ontology Term Resolution

What it does

When your agent uses it

• An ontology identifier must be produced
• Checked - annotating tissue
• Developmental stage fields
• Preparing metadata for GEO

Example prompts

• “ontology term”
• “ontology ID”
• “controlled vocabulary”
• “/ontology-term-resolution”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires Python 3.11+. Scripts use only the standard library - no third-party packages. Needs network access to https://www.ebi.ac.uk/ols4, https://bioregistry.io, https://resolver.api.identifiers.org, and https://www.ebi.ac.uk/spot/zooma (all public, no API key).
• Pre-approved tools (allowed-tools): Read, Write, Edit, Bash

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write
  • Edit
  • Bash

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 7 files in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python3

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • ebi.ac.uk
  • purl.obolibrary.org

  Also links to:

  • arxiv.org
  • doi.org
  • export.arxiv.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires Python 3.11+. Scripts use only the standard library - no third-party packages. Needs network access to https://www.ebi.ac.uk/ols4, https://bioregistry.io, https://resolver.api.identifiers.org, and https://www.ebi.ac.uk/spot/zooma (all public, no API key).

  From compatibility in the SKILL.md frontmatter.

Context cost

Ontology Term Resolution loads about 3.6k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 258 tokens; SKILL.md has 1,438 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~258

When it runs · the whole SKILL.md, loaded when a task matches

~3.6k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Read, Write, Edit, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 1,438 words, ~3,642 tokens.

name

ontology-term-resolution

description

Resolves free-text scientific labels to ontology term IDs and validates existing CURIEs against the EBI Ontology Lookup Service (OLS4). Also looks up prefixes in Bioregistry, resolves compact identifiers via Identifiers.org, maps lab shorthand with ZOOMA, and builds Ontobee term pages. Use whenever an ontology identifier must be produced or checked - annotating tissue, cell type, disease, phenotype, assay, chemical, organism, sex, or developmental stage fields; preparing metadata for GEO, ENA, BioSamples, CELLxGENE, HCA, or ISA-Tab submission; auditing a metadata table of term IDs; checking whether a term is obsolete and what replaced it; or deciding HPO vs HP. Triggers include "ontology term", "ontology ID", "CURIE", "controlled vocabulary", "UBERON", "CL:", "MONDO", "HPO", "EFO", "ChEBI", "NCBITaxon", "GO term", "PATO", "Zooma", "Bioregistry", "Identifiers.org", "Ontobee", "annotate this tissue/cell type/disease", and any request to emit or verify an identifier shaped like PREFIX:0001234.

allowed-tools

Read, Write, Edit, Bash

compatibility

Requires Python 3.11+. Scripts use only the standard library - no third-party packages. Needs network access to https://www.ebi.ac.uk/ols4, https://bioregistry.io, https://resolver.api.identifiers.org, and https://www.ebi.ac.uk/spot/zooma (all public, no API key).

license

MIT

metadata.version

1.4

metadata.last-reviewed

2026-10-01

metadata.skill-author

K-Dense Inc.

cd skills/ontology-term-resolution/scripts

# one string, constrained to the ontology that should define it
python3 resolve_terms.py "liver" --ontology uberon

query   rank  curie           iri                                               label  ontology  match_type   strategy  defining_ontology
liver   1     UBERON:0002107  http://purl.obolibrary.org/obo/UBERON_0002107       liver  uberon    exact_label  exact     true

# a column of tissue names; anything not an exact hit is reported, not guessed
python3 resolve_terms.py --input tissues.txt --ontology uberon \
    --exact-only --format tsv -o resolved.tsv

# accept fuzzy fallbacks, then review the partial hits by hand
python3 resolve_terms.py "left ventrical of heart" --ontology uberon --top 3

python3 validate_terms.py UBERON:0002107 EFO:0001067 UBERON:9999999

id              status     actual_label                  ontology  replacement     detail
UBERON:0002107  ok         liver                         uberon
EFO:0001067     obsolete   obsolete_parasitic infection  efo       MONDO:0005135   obsolete; replaced by MONDO:0005135
UBERON:9999999  not_found                                                          no such term in the ontology this prefix names

# id + label columns; catches IDs that exist but are labelled as something else
python3 validate_terms.py --input metadata.tsv --strict

# a tissue column must hold UBERON anatomical entities and nothing else
python3 validate_terms.py --input tissue_ids.tsv \
    --branch UBERON:0000465 --expect-ontology uberon

python3 lookup_prefix.py HP HPO HP:0001250 HPO:0001250

query        status          preferred_prefix  canonical_curie  pattern    detail
HP           ok              HP                                 ^\d{7}$
HPO          synonym_prefix  HP                                 ^\d{7}$    'HPO' is a synonym of preferred prefix HP
HP:0001250   ok              HP                HP:0001250       ^\d{7}$
HPO:0001250  synonym_prefix  HP                HP:0001250       ^\d{7}$    'HPO' is a synonym of preferred prefix HP

# after resolve_terms.py returned unresolved / partial
python3 map_terms.py PBMC --ontology cl --high-confidence-only

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts, references) in skills/ontology-term-resolution of K-Dense-AI/scientific-agent-skills.

• SKILL.md
• references/companion-apis.md
• references/curation-rules.md
• references/ols4-api.md
• references/ontology-registry.md
• scripts/id_client.py
• scripts/lookup_prefix.py
• scripts/map_terms.py
• scripts/ols_client.py
• scripts/resolve_terms.py
• scripts/validate_terms.py
• scripts/zooma_client.py

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

• lingxling/awesome-skills-cnclaude-scientific-skills/skills/ontology-term-resolution