Agent skill

Functional Genomics

by exon-research in exon-research/genomi

Candidate gene evidence from perturbation, dependency, resistance, sensitivity, viability, or assay-context records.

Apache-2.0Auto-check passedResearch & Science

Install Functional Genomics

skills CLI
$ npx skills add exon-research/genomi --skill functional-genomics -a claude-code

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

GitHub CLI
$ gh skill install exon-research/genomi functional-genomics --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/exon-research/genomi.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/functional-genomics .claude/skills/functional-genomics && 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
functional-genomics
GitHub stars
484
Token cost
~2k tokens
SKILL.md length
929 words
Files
1
Skills in repo
20
Repo updated
First seen
Licence
Apache-2.0

At a glance

Candidate gene evidence from perturbation, dependency, resistance, sensitivity, viability, or assay-context records.

  • Works in 7 steps: Extract candidate gene symbols and the… → Call… → Call… → …
  • Tasks that involve Bioinformatics
  • SKILL.md covers Contract, Tool Flow, Source Record Shape and Cross-Capability Synthesis, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Functional Genomics is an agent skill from exon-research/genomi. Candidate gene evidence from perturbation, dependency, resistance, sensitivity, viability, or assay-context records.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: Local-first, open-source Claude Science alternative, before Claude Science is a thing. Turn your AI agent into personal DNA expert. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Bioinformatics

Example prompts

  • “/functional-genomics”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Extract candidate gene symbols and the requested context: organism, cell
  2. Call functional_genomics.compare_gene_perturbation for the normal flow. It
  3. Call functional_genomics.retrieve_perturbation_records only for explicit
  4. Call functional_genomics.query_geo when the advantage is public source
  5. If the source is a local CSV/TSV result table, call
  6. Pass supplied, imported, or retrieved source records to
  7. Use verified perturbation-source evidence when the user asks for only the gene

What it can do on your machine

Read from SKILL.md and the folder at commit 1df4f5b. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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

Functional Genomics loads about 2k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 929 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~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); files beside SKILL.md are not scanned.

SKILL.md

The full file from exon-research/genomi at commit 1df4f5b, republished under its Apache-2.0 licence (© exon-research). 929 words, ~2,010 tokens.

Download SKILL.mdSave it as .claude/skills/functional-genomics/SKILL.md (or your agent's skills folder).
name
functional-genomics
description
Candidate gene evidence from perturbation, dependency, resistance, sensitivity, viability, or assay-context records.
tools
functional_genomics.retrieve_perturbation_records, functional_genomics.query_geo, functional_genomics.import_perturbation_table…
mutating
true

Functional Genomics Perturbation Evidence

Retrieve functional-genomics perturbation evidence for a declared experimental context plus candidate genes. Screens are one supported perturbation experiment subtype, not the capability name.

Contract

Perturbation evidence comes from native public retrieval, user-provided local tables, reviewed stored research, or explicitly supplied source records. Generic gene biology can explain a result, but it should not outrank direct perturbation-source evidence.

Direct support is source-verified perturbation evidence. Source records carry verified fields or support spans for the requested cell line, perturbation, assay/readout, and candidate gene relationship; broader biology remains adjacent or plausibility-only evidence.

Native coverage currently includes BioGRID ORCS when a BioGRID ORCS access key is available, DepMap CRISPR gene-effect release tables when a CSV URL or path is configured, and bounded NCBI GEO metadata/table discovery. GEO's advantage is source discovery for public or published perturbation datasets: SeriesMatrix files, supplementary tables, and accession-indexed study records that curated screen APIs may not expose for the requested cell line, perturbation, assay, or readout. If native sources cannot be queried, the response makes that coverage state visible rather than weak ranking evidence.

Tool Flow

  1. Extract candidate gene symbols and the requested context: organism, cell line, perturbation, assay, phenotype, resistance, sensitivity, viability, or readout.
  2. Call functional_genomics.compare_gene_perturbation for the normal flow. It retrieves native public perturbation records when configured, verifies source records, and returns candidate evidence rows.
  3. Call functional_genomics.retrieve_perturbation_records only for explicit native-source inspection, coverage debugging, or source availability review.
  4. Call functional_genomics.query_geo when the advantage is public source discovery: the question mentions a published/public screen dataset, study accession, supplementary table, SeriesMatrix-style file, or compare has insufficient BioGRID/DepMap/stored evidence for a requested perturbation context that likely came from a public study. The user does not need to name GEO. GEO metadata alone is not direct evidence; direct support still requires table-derived, source-verified candidate gene and perturbation-context fields.
  5. If the source is a local CSV/TSV result table, call functional_genomics.import_perturbation_table first.
  6. Pass supplied, imported, or retrieved source records to functional_genomics.compare_gene_perturbation; it verifies source records before comparing candidate genes.
  7. Use verified perturbation-source evidence when the user asks for only the gene symbol. Audit decision_evidence before explaining the result.

functional_genomics.compare_gene_perturbation returns evidence rather than a universal answer. If source records do not support an identifier-only answer, do not invent a gene; state the source gap or gather better source records.

Source Record Shape

functional_genomics.compare_gene_perturbation accepts reviewed source records. Prefer records that include the source title or URL, named genes, the source-backed finding, source type, and any verified perturbation context such as cell line, perturbation, assay, phenotype, readout, PMID, or DOI.

When a paper or dataset directly supports the requested perturbation context, include the specific source-backed spans that verify the cell line, perturbation, assay/readout, and gene relationship. Generic pathway or co-mention literature should remain adjacent evidence.

Direct perturbation-source context outranks generic literature or pathway plausibility only when source-backed fields verify the context. If no source records are supplied, or if records are generic literature without context verification, the tool cannot fairly make a high-support ranking.

Cross-Capability Synthesis

A scope-limited result from this capability is not a final user-facing answer when other Genomi capabilities can contribute orthogonal evidence to the same question. Returning "cannot answer" while applicable capabilities remain unexamined is a host-agent failure mode.

Tools

Show full SKILL.md (388 more words)Show less
functional_genomics.compare_gene_perturbation

Compare candidate genes by verified functional-genomics perturbation experiment evidence.

Use when: Retrieves native public perturbation experiment records when configured, verifies source records, and returns candidate-gene evidence rows for the declared perturbation context.

Why necessary: Screen and dependency questions need verified perturbation evidence, not inherited-variant or disease association evidence.

Example prompts: Which candidate gene is best supported by this CRISPR resistance screen?

Result semantics: Runs source-record verification before candidate comparison; generic literature stays separate from direct perturbation experiment evidence.

functional_genomics.import_perturbation_table

Extract verified perturbation experiment source records from a local CSV or TSV result table.

Use when: The agent has a local CSV/TSV perturbation, dependency, viability, resistance, or supplementary result table and needs source records before candidate comparison.

Why necessary: User-supplied screen tables need structured extraction before they can support gene comparisons.

Result semantics: Extracts table rows into source records and verifies row-level genes plus perturbation context; it does not select the answer gene.

functional_genomics.query_geo

Query NCBI GEO metadata and bounded public study tables for functional-genomics perturbation source records.

Use when: The advantage is public dataset discovery: a published/public screen, study accession, supplementary table, SeriesMatrix-style file, or an under-covered perturbation context where curated sources did not provide direct source records.

Why necessary: GEO can find source-backed tables for study-specific cell lines, perturbations, assays, and readouts that BioGRID ORCS, DepMap, or stored reviewed records may not cover; it keeps metadata-only hits separate from direct perturbation evidence.

Result semantics: Returns GEO metadata hits, download candidates with skip reasons, and verified source records when candidate genes are supplied. Metadata-only matches never count as direct evidence; direct support requires source-verified gene plus requested perturbation context fields.

functional_genomics.retrieve_perturbation_records

Retrieve native public functional-genomics perturbation records from BioGRID ORCS and DepMap for candidate genes and declared experimental context.

Use when: Explicit native-source inspection, source availability review, or coverage debugging for BioGRID ORCS and configured DepMap perturbation records.

Why necessary: Raw native-source retrieval lets agents inspect what BioGRID ORCS or DepMap returned, or why a native source was unavailable, without running candidate comparison.

Result semantics: Returns native functional-genomics source records from BioGRID ORCS and configured DepMap release tables; it does not select the final gene. For normal candidate-gene comparison, use functional_genomics.compare_gene_perturbation directly because it can retrieve native records when configured. BioGRID ORCS requires an access key; DepMap requires a configured public CRISPR gene-effect CSV URL or path.

© exon-research, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/functional-genomics of exon-research/genomi.

Open the folder on GitHubat commit 1df4f5b

Compare with similar skills

Functional Genomics next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Functional Genomics compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Functional Genomics this skillexon-research/genomi484—~2kAutomated safety check: PassApache-2.0
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

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Questions about Functional Genomics

What does Functional Genomics do?

Candidate gene evidence from perturbation, dependency, resistance, sensitivity, viability, or assay-context records. Functional Genomics is an agent skill from exon-research/genomi. Candidate gene evidence from perturbation, dependency, resistance, sensitivity, viability, or assay-context records.

When should I use Functional Genomics?

Functional Genomics fits situations like: tasks that involve Bioinformatics.

How do I install Functional Genomics in Claude Code?

Run `npx skills add exon-research/genomi --skill functional-genomics -a claude-code`. Or copy the skill folder (skills/functional-genomics in exon-research/genomi) into .claude/skills/functional-genomics in your project. Claude Code loads it when a task matches its description.

How do I install Functional Genomics in Codex?

Run `npx skills add exon-research/genomi --skill functional-genomics -a codex`. Or copy the skill folder (skills/functional-genomics in exon-research/genomi) into .agents/skills/functional-genomics in your project. Codex loads it when a task matches its description.

Can I use Functional Genomics 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 exon-research/genomi --skill functional-genomics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/functional-genomics, .gemini/skills/functional-genomics, .github/skills/functional-genomics and .opencode/skills/functional-genomics in your project.

What does Functional Genomics need to run?

SKILL.md names no scripts, command-line tools or credentials: Functional Genomics is instructions for the agent only.

Does Functional Genomics 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 Functional Genomics 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. Review the folder before installing.

What licence does Functional Genomics use?

Functional Genomics is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Functional Genomics use?

About 2k tokens (SKILL.md is roughly 8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Functional Genomics?

Skills that share tags, products or a category with Functional Genomics: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Functional Genomics?

exon-research (a GitHub organization) maintains it in exon-research/genomi, which has 484 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on August 31, 2026.

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