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Agent skill

Kegg Database

by aipoch in aipoch/medical-research-skills

Direct access to KEGG via the REST API for academic-only pathway/gene/compound/drug queries; use when you need precise HTTP-level control or targeted KEGG ID mapping.

MITAuto-check passedBackend & APIs

Install Kegg Database

skills CLI
$ npx skills add aipoch/medical-research-skills --skill kegg-database -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills kegg-database --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Evidence Insight/kegg-database' .claude/skills/kegg-database && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
kegg-database
GitHub stars
2k
Token cost
~1.3k tokens
SKILL.md length
416 words
Files
4 (incl. scripts, references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Direct access to KEGG via the REST API for academic-only pathway/gene/compound/drug queries; use when you need precise HTTP-level control or targeted KEGG ID mapping.

  • You need precise HTTP-level control
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Runs Python scripts from its folder
  • Targeted KEGG ID mapping

What it does

Kegg Database is an agent skill from aipoch/medical-research-skills. Direct access to KEGG via the REST API for academic-only pathway/gene/compound/drug queries; use when you need precise HTTP-level control or targeted KEGG ID mapping.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `kegg-database_audit_result_v1.json`, `references/kegg_reference.md` and `scripts/kegg_api.py`).

It sits in Backend & APIs, covering REST APIs. It works with Python. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need precise HTTP-level control
  • Targeted KEGG ID mapping

Example prompts

  • “/kegg-database”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

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

  • Network

    No URLs in SKILL.md.

    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.

Context cost

Kegg Database loads about 1.3k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 45 tokens; SKILL.md has 416 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~45
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.2k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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 aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 416 words, ~1,323 tokens.

Download SKILL.mdSave it as .claude/skills/kegg-database/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
kegg-database
description
Direct access to KEGG via the REST API for academic-only pathway/gene/compound/drug queries; use when you need precise HTTP-level control or targeted KEGG ID mapping.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You need to fetch KEGG pathway, gene, compound, enzyme, disease, or drug records directly from the KEGG REST API.
  • You want to perform gene ↔ pathway mapping (e.g., building inputs for pathway enrichment or reporting).
  • You need cross-references between KEGG databases (e.g., pathway → genes, gene → KO, pathway → compounds).
  • You must convert identifiers between KEGG and external databases (e.g., KEGG gene → NCBI Gene ID / UniProt; KEGG compound → PubChem).
  • You need drug–drug interaction (DDI) lookups for KEGG drug IDs.

Note: KEGG REST access is intended for academic use. Non-academic/commercial use may require a separate KEGG license.

Key Features

  • Full coverage of core KEGG REST operations via Python helpers:
    • kegg_info (database metadata)
    • kegg_list (catalog listing)
    • kegg_find (keyword/property search)
    • kegg_get (entry retrieval; sequences/structures/images)
    • kegg_conv (ID conversion)
    • kegg_link (cross-database linking)
    • kegg_ddi (drug–drug interactions)
  • Supports common KEGG identifiers and formats:
    • Pathways: map00010, hsa00010
    • Genes: hsa:10458
    • Compounds: cpd:C00002
    • Drugs: dr:D00001
    • Enzymes: ec:1.1.1.1
    • KO: ko:K00001
  • Output format options for kegg_get: aaseq, ntseq, mol, kcf, image, kgml, json (some formats are single-entry only).

Dependencies

  • Python >=3.9
  • requests >=2.31.0

Example Usage

python
"""
End-to-end example:
1) Find a human gene by keyword
2) Link the gene to pathways
3) Retrieve one pathway entry
4) Convert the gene ID to UniProt
"""

from scripts.kegg_api import kegg_find, kegg_link, kegg_get, kegg_conv

# 1) Search for a gene keyword in KEGG GENES
hits = kegg_find("genes", "p53")
print("FIND results (first lines):")
print("\n".join(hits.splitlines()[:5]), "\n")

# Choose a known KEGG gene ID for TP53 (human)
gene_id = "hsa:7157"

# 2) Link gene -> pathways
pathway_links = kegg_link("pathway", gene_id)
print("LINK gene -> pathways (first lines):")
print("\n".join(pathway_links.splitlines()[:5]), "\n")

# Parse the first pathway ID from the link output
# Typical line format: path:hsaXXXXX<TAB>hsa:7157
first_line = next((ln for ln in pathway_links.splitlines() if ln.strip()), None)
if not first_line:
    raise RuntimeError("No pathways returned for the gene ID.")

path_id = first_line.split("\t")[0].replace("path:", "")
print("Selected pathway:", path_id, "\n")

# 3) Retrieve the pathway entry (flat text)
pathway_entry = kegg_get(path_id)
print("GET pathway entry (first 30 lines):")
print("\n".join(pathway_entry.splitlines()[:30]), "\n")

# 4) Convert KEGG gene ID -> UniProt
uniprot_map = kegg_conv("uniprot", gene_id)
print("CONV KEGG -> UniProt:")
print(uniprot_map)

Implementation Details

API-to-function mapping

This skill wraps KEGG REST endpoints into Python functions (see scripts/kegg_api.py):

  • kegg_info(database_or_org)
    Retrieves database or organism metadata (release info, counts, etc.).

  • kegg_list(database, organism=None)
    Lists entries in a database; optionally scoped to an organism (e.g., ("pathway", "hsa")).
    Also supports listing explicit IDs (batch-style) when passed as a single string.

  • kegg_find(database, query, option=None)
    Searches by keyword or by chemical properties. Common option values:

    • formula (exact match)
    • exact_mass (range like 300-310)
    • mol_weight (range)

  • kegg_get(entry_ids, option=None)
    Retrieves full entries or specific formats:

    • Sequences: aaseq, ntseq
    • Structures: mol, kcf
    • Pathway assets: image (PNG), kgml (XML), json (Pathway JSON)

    Batching rules:

    • Most operations allow up to 10 entries per request.
    • image, kgml, and json typically allow only 1 entry per request.

  • kegg_conv(target_db, source)
    Converts IDs between KEGG and external databases (e.g., uniprot, ncbi-geneid, pubchem, chebi).
    Output is tab-delimited pairs: source_id<TAB>target_id.

  • kegg_link(target_db, source)
    Cross-references entries across KEGG databases (e.g., gene → pathway, pathway → compound, gene → KO).

  • kegg_ddi(drug_ids)
    Returns known drug–drug interactions for one or more KEGG drug IDs (up to typical batch limits).

Show full SKILL.md (72 more words)Show less
Practical constraints and error handling
  • Entry limits: Prefer chunking lists into batches of ≤10 IDs; enforce single-entry calls for image/kgml/json.
  • HTTP status codes: Treat non-200 responses as failures; common issues include:
    • 400 (bad request / malformed parameters)
    • 404 (unknown database or entry ID)
  • Rate behavior: KEGG does not publish strict rate limits; avoid high-frequency polling and add backoff/retry for robustness.
Reference documentation

For detailed endpoint syntax, database lists, and species codes, consult:

  • references/kegg_reference.md

© aipoch, 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 3 other files (scripts, references) in scientific-skills/Evidence Insight/kegg-database of aipoch/medical-research-skills.

  • SKILL.md
  • kegg-database_audit_result_v1.json
  • references/kegg_reference.md
  • scripts/kegg_api.py

Open the folder on GitHubat commit 686e09d

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Works with

Categories

Questions about Kegg Database

What does Kegg Database do?

Direct access to KEGG via the REST API for academic-only pathway/gene/compound/drug queries; use when you need precise HTTP-level control or targeted KEGG ID mapping. Kegg Database is an agent skill from aipoch/medical-research-skills. Direct access to KEGG via the REST API for academic-only pathway/gene/compound/drug queries; use when you need precise HTTP-level control or targeted KEGG ID mapping.

When should I use Kegg Database?

Kegg Database fits situations like: you need precise HTTP-level control; targeted KEGG ID mapping.

How do I install Kegg Database in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill kegg-database -a claude-code`. Or copy the skill folder (scientific-skills/Evidence Insight/kegg-database in aipoch/medical-research-skills) into .claude/skills/kegg-database in your project. Claude Code loads it when a task matches its description.

How do I install Kegg Database in Codex?

Run `npx skills add aipoch/medical-research-skills --skill kegg-database -a codex`. Or copy the skill folder (scientific-skills/Evidence Insight/kegg-database in aipoch/medical-research-skills) into .agents/skills/kegg-database in your project. Codex loads it when a task matches its description.

Can I use Kegg Database in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill kegg-database -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kegg-database, .gemini/skills/kegg-database, .github/skills/kegg-database and .opencode/skills/kegg-database in your project.

What does Kegg Database need to run?

Going by SKILL.md and its folder, Kegg Database needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Kegg Database access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Kegg Database safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Kegg Database use?

Kegg Database is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Kegg Database use?

About 1.3k tokens (SKILL.md is roughly 5.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.8k tokens, read only when the agent opens those files.

What are the alternatives to Kegg Database?

Skills that share tags, products or a category with Kegg Database: Zhihu Search (itwanger/toBeBetterJavaer, 18k stars), Fastcrud (benavlabs/fastcrud, 1.6k stars), Cloudflare Email Service (hodgef/apiker, 127 stars) and FastAPI Project Templates (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Kegg Database?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.