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Omh Biomedical Evidence

CommunityPopular
rlaope
omh-biomedical-evidence

[omh] Public variant claims to cross-check: retrieve and reconcile source-labelled biomedical evidence with identity, conflict and freshness checks. Use when the user says: biomedical-evidence, biomedical evidence reconciliation, ClinVar cross-check, variant evidence matrix, BioMCP capture reconciliation.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-biomedical-evidence
Stars
3.6K
Forks
269
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Biomedical Evidence AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-biomedical-evidence
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-biomedical-evidence .claude/skills/omh-biomedical-evidence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Biomedical Evidence in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Omh Biomedical Evidence on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Omh Biomedical Evidence is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Biomedical Evidence

This is an OMH biomedical-evidence workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

Public sources can disagree about a variant while describing different alleles, diseases or assertion domains. Preserve those differences and their provenance before any synthesis; a fetched record is not a clinical recommendation.

First Steps

  • Read toolkit/README.md and references/execution-and-qa.md; inspect the installed BioMCP version and help before any public retrieval.
  • Require public entities only; name exact identifiers, source terms, isolated workspace and a 40-second per-retrieval host timeout before capturing unmodified stdout/stderr.
  • Run toolkit/scripts/reconcile.py only against a hash-checked local capture envelope; never execute provider text or install an upstream skill pack.

Do Not Use When

  • For explaining one supplied paper, use omh-paper-learning; for broad literature discovery, use ulw-research.
  • For generic server configuration and connection diagnostics, load the host's MCP setup guidance instead.
  • For sequence analysis or omics pipelines, select an available computational biology specialist instead; this method does not implement those analyses.
  • For a patient's diagnosis, care, trial eligibility or treatment choice, stop and refer to an appropriately qualified professional; this toolkit cannot provide that decision.

Examples

Good example:

  • Prompt: Cross-check public BRAF V600E and melanoma records across ClinVar and ClinicalTrials.gov, retaining conflicting assertions and missing update dates.
  • Expected behavior: Capture exact public queries, reconcile raw JSON offline, and report identity/date gaps with both source receipts.
  • Why: Source comparison is bounded and does not ask for patient care.

Bad example:

  • Prompt: Use these patient records to select a therapy and decide whether the patient can join a trial.
  • Expected behavior: Refuse patient-level processing and clinical decisions; offer only public-source provenance checking.
  • Why: Registry status and source annotations are not clinical eligibility or treatment evidence.

Completion Checklist

  • Both source attempts have raw hashes and exact receipts, or missing/failing sources are explicitly partial with nonzero status.
  • Entity/build/transcript, disease basis, original assertions, conflicts and absent source times remain visible; no eligibility or clinical inference appears.
  • Read both output files, verify hashes and report synthetic tests separately from real retrieval; do not claim distribution or live availability from preparation.

Recovery Notes

  • If BioMCP is unavailable, keep captures unobserved and prepare only the explicit isolated setup; never fabricate a source response.
  • If a source fails or JSON changes, preserve stdout/stderr and a partial report; audit an updated adapter before accepting unknown schemas.
  • If disease labels or allele aliases cannot be matched literally, preserve the mismatch and ask for authoritative identifiers rather than silently broadening.

When to Use

Use when public gene/variant and disease assertions must be cross-checked against ClinVar and trial registry records, preserving source failures and disagreements rather than inferring a clinical conclusion.

Strong routing signals: `biomedical-evidence`, `biomedical evidence reconciliation`, `ClinVar cross-check`, `variant evidence matrix`, `BioMCP capture reconciliation`

Catalog Metadata

Category: analysis Phase: biomedical-evidence Quality tier: evidence-gated Reasoning demand: heavy

Quality bar:

  • Require observed source receipts and verified helper output hashes, not a mock result or health-only check.
  • Retain contradictory source assertions and date/identity gaps without deriving a clinical consensus.

Required inputs:

  • public gene/variant and disease query
  • bounded BioMCP capture workspace
  • intended entity identifiers and source scope

Expert clarification questions:

  • public gene/variant and disease query
    • English: Which public gene, exact allele if any, disease label and intended IDs should remain distinct?
    • Korean: 어떤 공개 유전자, 정확한 변이, 질환명과 식별자를 구분해서 확인할까요?

Expected outputs:

  • biomedical_evidence_matrix/v1
  • biomedical_reconciliation_manifest/v1

Artifact expectations:

  • Authored file contracts, not registered OMH RPC artifacts: evidence-matrix.json and manifest.json plus external raw capture/receipt references.
  • Raw responses remain outside the package; the matrix preserves source assertions with JSON-pointer paths, accessions/NCT IDs, review/submitter context and retrieval/source times.

Safety rules:

  • No patient data, clinical eligibility determination, treatment recommendation or pathogenicity adjudication; source assertions are not medical advice.
  • No network or subprocess from the shipped helper, registration, discovery or install; BioMCP runs only via explicit bounded host invocation in an isolated optional environment.
  • Keep germline/somatic and disease/population contexts separate; recruitment and text matches never establish eligibility or variant relevance.
  • Preserve raw bytes and exact queries; mark source failure, missing dates and unresolved identity as partial instead of inventing biological findings.

Procedure: load references/procedure.md.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Record actual tool results, or not_observed / not_available, in the record; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Reply in the user's own words and the host's own voice: the host's persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a real unresolved user decision or a useful briefing discussion remains, offer one specific, content-linked question. Completed work needs no question; omit unrelated non-action footers and generic offers to continue. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Omh Biomedical Evidence AI skill do?

[omh] Public variant claims to cross-check: retrieve and reconcile source-labelled biomedical evidence with identity, conflict and freshness checks. Use when the user says: biomedical-evidence, biomedical evidence reconciliation, ClinVar cross-check, variant evidence matrix, BioMCP capture reconciliation.

Why use Omh Biomedical Evidence on TypingMind?

Because you install it once and use it with any model. Omh Biomedical Evidence is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Omh Biomedical Evidence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-biomedical-evidence. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Omh Biomedical Evidence?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Omh Biomedical Evidence?

As many as you like. As long as a model supports skills, you can use Omh Biomedical Evidence with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Omh Biomedical Evidence AI skill free?

Yes. It is published on GitHub by rlaope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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