Agent skill

Genomi

by exon-research in exon-research/genomi

A skill your agent uses for genetics, genome source, variant, gene, phenotype, disease, screen, pharmacogenomics, and Genomi install/setup maintenance questions.

Apache-2.0Auto-check passedResearch & Science

Install Genomi

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

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

GitHub CLI
$ gh skill install exon-research/genomi genomi --agent claude-code

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

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

Facts

Skill name
genomi
GitHub stars
484
Token cost
~4k tokens
SKILL.md length
1,965 words
Files
685 (incl. scripts, assets)
Skills in repo
20
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses for genetics, genome source, variant, gene, phenotype, disease, screen, pharmacogenomics, and Genomi install/setup maintenance questions.

  • Pharmacogenomics
  • SKILL.md covers How To Call Genomi, Core Rules, Routing and Setup, plus 6 more sections
  • Calls git
  • Genomi install/setup maintenance questions

What it does

Genomi is an agent skill from exon-research/genomi. Use this skill for genetics, genome source, variant, gene, phenotype, disease, screen, pharmacogenomics, and Genomi install/setup maintenance questions.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 687 other files, including scripts and assets (for example `.github/workflows/ci.yml`, `AGENTS.md` and `CLAUDE.md`).

It sits in Research & Science, covering Bioinformatics. It works with Model Context Protocol. 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

  • Pharmacogenomics
  • Genomi install/setup maintenance questions

Example prompts

  • “/genomi”

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

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

    Shell commands in SKILL.md call:

    • git

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

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Genomi loads about 4k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 1,965 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~4k

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 exon-research/genomi at commit 1df4f5b, republished under its Apache-2.0 licence (© exon-research). 1,965 words, ~4,010 tokens.

Download SKILL.mdSave it as .claude/skills/genomi/SKILL.md (or your agent's skills folder). This skill also uses 684 other files; get the full folder from GitHub.
name
genomi
description
Use this skill for genetics, genome source, variant, gene, phenotype, disease, screen, pharmacogenomics, and Genomi install/setup maintenance questions.

Genomi

Genomi gives agents local tools for genetics and DNA-aware evidence work. Use it when the user's request is about variants, genes, phenotypes, disease genes, biological screens, medication response, ancestry reference-panel context, polygenic-score context, or a genome source.

How To Call Genomi

Call Genomi through the MCP server: mcp__genomi__<operation> (or your host's equivalent namespace).

If those tools are missing from the current session's tool list but the host's MCP list shows Genomi connected, the session pre-dates the server's registration. Ask the user to start a new session.

Core Rules

  • Public by default: if the chat has not mentioned a genome source, answer from public/tool sources only.
  • Active Genome Index context is chat-scoped: use it only when this conversation provides a genome source, names a previous run, asks about the selected Active Genome Index context, or says something like "my Active Genome Index" or "my genome".
  • If the user provides a genome source path in this chat, that is approval to read that source for this session.
  • If the user says "my Active Genome Index" or "my genome" without a path, it is acceptable to check for an already imported Active Genome Index context.
  • Reading imported/parsed Active Genome Index artifacts, resuming a previous run for evidence, or searching for an existing "my Active Genome Index"/"my genome" context requires explicit user approval for this session. Record approval with active_genome_index.approve_access before calling those tools.
  • A configured default user may select an Active Genome Index as metadata, but default selection never replaces current-session read approval.
  • Do not use unrelated genome sources from other chats, workspaces, or external evaluation tasks.
  • Call narrow tools first and inspect evidence before making a claim.
  • Treat this root skill as static startup guidance. Do not infer live session state from it; use genomi.describe_context, genomi.check_libraries, and tool result envelopes for changing context.
  • If an MCP tool returns status="in_progress", call genomi.check_background_job with the returned job_id. Do not retry the same work with a capped parse or raw text scan unless the user asks for that fallback.
  • Only send parameters supplied by the current user request, current Genomi context, a previous Genomi result, explicit user approval, or an explicit override. Omit unknown optional parameters.
  • Defaults are part of the reasoning chain. Tool definitions expose parameterDefaults; returned results include defaults_applied for omitted defaults so the host agent can inspect and override them in a follow-up call when the user intent requires it.
  • Tool definitions expose dependencyContract when a tool needs local installed libraries or external network/API sources. Missing local libraries return requires_library_install; unavailable external sources return source_unavailable; local source-file requirements appear as localResources.
  • In the final answer, mention Active Genome Index use only when it materially affects the result: for example, it supports or refutes a user-specific claim, changes a limitation, blocks an operation until approval, or explains a required next action. Do not add a routine source-status line.
  • Derive confidence dynamically for each Genomi-guided answer from tool evidence, source trust, coverage, conflicts, and missing evidence. Do not use a static default confidence or a user-selected confidence profile. Genomi result fields describe evidence support, coverage, overlap, and source state; they are not final answer-confidence labels.
  • Use genomi.describe_context when the user asks about personal context, their own genome/context, a genome source, a previous run, a selected user, or before making sample-specific claims. When you call it, inspect active_response_profile.guidance; the active profile id is persisted in the Genomi registry (set via genomi.set_response_profile) and falls back to the catalog default when none is set. Do not call it only to bootstrap a public-only question.
  • Handle Active Genome Index lifecycle states yourself. When a read op's envelope or genomi.describe_context returns active_genome_index_readiness.status == "needs_reparse", look up the recorded source path under active_genome_index.agi_intake_source_path and call genomi.parse_source with it — routine maintenance, no user prompt needed. Only ask the user when availability.agi_intake_source_path is false (path moved or deleted) or the status is schema_too_new (Genomi runtime out of date). Never proceed with a stale Active Genome Index while silently substituting placeholder data; use the Active Genome Index skill for the full procedure.
  • For search-like operations, pass host-inferred alternate wording in semantic_context when the current chat reasonably supports alternate biomedical wording. Send the user's original wording as raw_query, add host_expansions only as retrieval terms, and add host_entities for helpful proposed spans such as drug, gene, phenotype, trait_or_condition, variant, or rsid. These terms are retrieval inputs, not evidence; Genomi reports source/retrieval hits in term_matches and no-hit terms in term_misses.

Routing

MCP tools/list returns only the default set:

  • Core genomi.*, research.*, and journal.* operations.
  • The complete direct genomilab.* host-application boundary.
  • genomi.invoke — the dispatcher for every other capability tool.

Before using a genomilab.* operation, load skills/genomilab/SKILL.md. Those tools keep the current Claude/Codex task in control; they require a query-ready selected Active Genome Index, bind patient authorization to the current MCP session, and use the portal only for onboarding, exact approvals, provider setup, and committed-event monitoring.

To use a non-base capability tool, load the matching focused capability skill, then call:

genomi.invoke({"tool": "<operation_name>", "params": {...}})

Example:

genomi.invoke({"tool": "variant.resolve", "params": {"rsid": "rs429358"}})

The dispatcher validates the registered operation name, runs the underlying tool's input-schema validation, and returns the underlying tool's response with an added dispatched_tool field.

Operation namespaces are tool-name prefixes, not disclosure branches. Use namespace filters only for debugging or audits.

Resolve context, select the intent capability, read its skill markdown, call the smallest useful operation through genomi.invoke (or direct call for base tools), inspect evidence, journal material findings, and continue until the answer is supported. Use genomi.describe_context only when this chat asks about personal context, the user's own genome/context, Active Genome Index context, a selected user, a genome source, or a previous run.

Setup

genomi.install installs or updates Genomi, and genomi install / genomi update are the same operation. It always updates everything that can be updated: the runtime code (git pull --ff-only on a git checkout, unless GENOMI_SKIP_RUNTIME_GIT_PULL is set for a non-git distribution), all public reference libraries into GENOMI_HOME (idempotent — each installed library is checked against its source and re-downloaded only if it changed upstream, so re-running transfers nothing when nothing changed; pass force to re-download regardless), host-agent skill symlinks in detected host skill directories (including stale/dangling repair and obsolete Genomi capability-link removal), the public retrieval indexes, and a background reparse of any genome whose index schema is older than the updated runtime's. There are no per-step skip flags — genomi update does the full update by default. The libraries parameter only narrows which reference libraries to materialize (default everything): pass a specific library (or comma-separated set) to install just those — both for an install-time subset the user chose and to add a single new library on demand at runtime (the "install this missing library" path). To see which libraries are already installed vs missing before deciding, call genomi.check_libraries (its summary reports installed_count / missing_count). This applies once Genomi is installed; first-time setup on a machine without the genomi runtime follows the source bootstrap in INSTALL_FOR_AGENTS.md.

Show full SKILL.md (827 more words)Show less

Parsing A Genome Source

genomi.parse_source is a core genomi.* tool: it detects, parses, and digitizes a genome source (VCF/gVCF, BAM, paired-end FASTQ, or a consumer-array raw genotype export from 23andMe, AncestryDNA, MyHeritage, FamilyTreeDNA, or Living DNA; bare text/CSV, gzip/bzip2/xz-compressed, or inside a zip/tar archive; or a .genome/1.0 bundle such as sample.genome.tar.gz) into a queryable Active Genome Index.

  • Use when: the user supplied a genome source in this chat and downstream questions need a queryable Active Genome Index this session.
  • Why: raw VCF/BAM/genotype files are too large and irregular for reliable direct reasoning; parsing builds the scoped index later tools query.
  • Not for: public-only genetics questions, selecting an already-parsed index, or capped sample scans that should not replace a complete index.
  • Result: digitizes local intake into an Active Genome Index; Genomi auto-detects the source type. Supplying user_nickname links the parsed artifact to a user profile. It does not run whole-callset annotation — focused tools materialize public libraries lazily when their evidence is needed.
  • If it returns status="in_progress" with a job_id, poll genomi.check_background_job; don't substitute a capped parse or raw scan unless the user explicitly asks for a fallback.
  • gVCFs parse in two phases. A gVCF is ~96% reference blocks, so the parse returns as soon as every variant is stored and indexed — the result reports variants_ready (not yet completed) and the whole interpretation surface (rsID, gene, region, exact-allele lookup, ClinVar, PRS, …) is already correct. The reference-block tail is appended by a detached background job (active_genome_index.build_reference_pass) whose job_id is surfaced in the result's next_actions. Until that job reports completed, only "is this locus confirmed reference vs not-callable" coverage answers are provisional — every readiness/coverage result carries reference_pending to say so. Plain VCFs, small files, and capped (max_records) parses stay single-phase. Other sources (consumer arrays, BAM/FASTQ) have no reference tail to defer.
  • After a parse, offer to name the profile. When the user did not pass user_nickname, the result includes an ask_user next action: ask them for a profile nickname and whether to set it as the machine default, then record it by re-running with user_nickname (+ set_default_user=true) or via the invoke-only active_genome_index.assign_user_genome / set_default_user tools — exactly the offer INSTALL_FOR_AGENTS.md Step 8 makes.

The parse/digitize/user-management workflow (selecting users, approving access, assigning a genome to a profile, lifecycle reparse) lives in the Active Genome Index skill, which also owns the active_genome_index.* interpretation tools.

Journal

Use journal when an investigation spans multiple Genomi tools and the host agent needs to record reasoning over evidence. Journal entries are agent notes with traceability links; they are not source evidence and should not be used as candidate-ranking source_records.

Default Tools

These tools appear in the default tool list. Their full metadata is available without expansion.

Genomi context and users:

  • genomi.check_background_job
  • genomi.check_libraries
  • genomi.describe_context
  • genomi.install
  • genomi.invoke
  • genomi.list_resources
  • genomi.search_indexes
  • genomi.set_response_profile

Active Genome Index:

  • genomi.parse_source

All other active_genome_index.* and focused genetics tools are invoke-only: reach them via genomi.invoke after loading the matching capability skill.

GenomiLab Research Desk (default-complete):

  • genomilab.open_workspace
  • genomilab.create_investigation
  • genomilab.form_specialist_board
  • genomilab.report_specialist_progress
  • genomilab.record_specialist_report
  • genomilab.inspect_investigation
  • genomilab.prepare_authorization
  • genomilab.record_patient_observations
  • genomilab.submit_plan
  • genomilab.execute_request
  • genomilab.check_request
  • genomilab.submit_brief
  • genomilab.submit_research_artifact
  • genomilab.verify_sequence_substitution
  • genomilab.run_esm_substitution_analysis
  • genomilab.run_proto_blinded_experiment_design
  • genomilab.list_research_artifacts
  • genomilab.list_research_tools
  • genomilab.revoke_context

The agent creates investigations and owns all task lifecycle. The portal must not create, message, or cancel an agent task. A repeated local stdio MCP initialize is a new agent session and requires renewed patient authorization before private investigation state becomes visible. HTTP MCP initialization is public-tools-only and cannot create or replace the private GenomiLab runtime.

Public research:

  • research.build_target_packet
  • research.list_sources
  • research.query
  • research.record
  • research.search

Journal:

  • journal.append_entry
  • journal.export_memory
  • journal.search_entries
  • journal.summarize

Candidate Evidence

Candidate and ranking operations return evidence views, decision_evidence, warnings, and coverage. Use source-specific candidate-gene tools instead of a universal comparator: phenotype.compare_gene_hpo_evidence for HPO/single-subject phenotype matching, gwas.compare_gene_associations for GWAS Catalog gene-field evidence, phenotype.compare_drug_target_evidence for drug-target evidence, and functional_genomics.compare_gene_perturbation for perturbation evidence. phenotype.retrieve_trait_gene_records retrieves trait-to-gene records from integrated sources. Records labelled association_only_not_causal are visible evidence, not an answer. Any operation that exposes an answer-shaped candidate result must expose the evidence behind that result. Use that evidence for the host-agent decision.

Multi-Stream Synthesis

When multiple Genomi capabilities can contribute orthogonal evidence to the same question, combine them — both in the initial plan and in follow-ups.

A scope-limited single-capability result (missing calibration, no record at locus, association-only, library-not-installed, low overlap, source unavailable, etc.) is not a final user-facing answer when other Genomi capabilities can contribute orthogonal evidence to the same question. Returning "I cannot answer" while applicable capabilities remain unexamined is a host-agent failure mode, not a Genomi limitation.

When multiple plausible plans differ materially in cost, surface the choice once with the tradeoff and commit to the user's pick. Over-checkpointing is itself a failure mode.

Answering

Lead with the answer. When a finding is grounded in a public dataset (ClinVar, GWAS Catalog, PGS Catalog, 1000 Genomes panel, etc.) and that source materially shapes the result, name the dataset inline in the prose. Keep clinical language informational and recommend clinical confirmation for medical decisions. Confidence is an answer-time synthesis judgment, not static metadata. Adapt explanation depth to the selected response profile without weakening evidence limits, privacy boundaries, or clinical-confirmation language.

© 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

SKILL.md and 684 other files (scripts, assets) in the repository root of exon-research/genomi.

  • SKILL.md
  • .github/workflows/ci.yml
  • .gitignore
  • AGENTS.md
  • CITATION.cff
  • CLAUDE.md
  • GENOMILAB_PRODUCT_DEFINITION.md
  • INSTALL_FOR_AGENTS.md
  • LICENSE
  • MANIFEST.in
  • README.md
  • README.zh-CN.md
  • RELEASE_NOTES.md
  • assets/genomi-logo.png
  • llms-full.txt
  • llms.txt
  • pyproject.toml
  • scripts
  • … and 667 more

Open the folder on GitHubat commit 1df4f5b

Compare with similar skills

Genomi 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.

Genomi compared with similar skills
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Genomi this skillexon-research/genomi484—~4kAutomated safety check: PassApache-2.0
Hcls Build Agentaws-samples/amazon-bedrock-agents-healthcare-lifesciences274—~885Automated safety check: PassMIT-0
Tooluniverseynulihao/AgentSkillOS6182 repos~2.5kAutomated safety check: PassNone
Remote Compute Sshaipoch/open-science5.5k—~5.7kAutomated safety check: PassApache-2.0
Latchbio IntegrationK-Dense-AI/scientific-agent-skills48k1 repos~2.5kAutomated safety check: NotesMIT
Hcls Get Startedaws-samples/amazon-bedrock-agents-healthcare-lifesciences274—~607Automated safety check: PassMIT-0

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Questions about Genomi

What does Genomi do?

A skill your agent uses for genetics, genome source, variant, gene, phenotype, disease, screen, pharmacogenomics, and Genomi install/setup maintenance questions. Genomi is an agent skill from exon-research/genomi. Use this skill for genetics, genome source, variant, gene, phenotype, disease, screen, pharmacogenomics, and Genomi install/setup maintenance questions.

When should I use Genomi?

Genomi fits situations like: pharmacogenomics; genomi install/setup maintenance questions.

How do I install Genomi in Claude Code?

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

How do I install Genomi in Codex?

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

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

What does Genomi need to run?

Going by SKILL.md and its folder, Genomi needs the command-line tools its instructions call (git).

Does Genomi access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Genomi 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 Genomi use?

Genomi is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Genomi use?

About 4k tokens (SKILL.md is roughly 16k 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 Genomi?

Skills that share tags, products or a category with Genomi: Hcls Build Agent (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Tooluniverse (ynulihao/AgentSkillOS, 618 stars), Remote Compute Ssh (aipoch/open-science, 5.5k stars) and Latchbio Integration (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Genomi?

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.