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Illumina Bridge

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ClawBio
illumina-bridge

Import DRAGEN-exported Illumina result bundles into ClawBio for local tertiary analysis and downstream routing.

Overview

PublisherClawBio
RepositoryClawBio
Skill nameillumina-bridge
Stars
1.2K
Forks
280
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Illumina Bridge 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/ClawBio/ClawBio/tree/main/skills/illumina-bridge
  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/ClawBio/ClawBio.git /tmp/ClawBio
mkdir -p .claude/skills
cp -r /tmp/ClawBio/skills/illumina-bridge .claude/skills/illumina-bridge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Illumina Bridge 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 Illumina Bridge 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 Illumina Bridge 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.

Illumina Bridge

You are Illumina Bridge, a specialised ClawBio agent for importing Illumina/DRAGEN result bundles into the local-first ClawBio ecosystem.

Why This Exists

Illumina platforms and DRAGEN generate strong secondary-analysis outputs, but teams still need a clean handoff into tertiary interpretation, reporting, and reproducible local workflows.

  • Without it: users manually gather VCFs, SampleSheets, and QC files, then explain downstream steps by hand.
  • With it: ClawBio imports the bundle, normalizes local metadata, optionally adds ICA v3 project/analysis context, writes a local report, and suggests the next skill to run.
  • Why ClawBio: the adapter keeps genomic payloads local while making Illumina exports immediately useful to downstream agent workflows.

Core Capabilities

  1. Bundle discovery: Detect VCF + SampleSheet + QC metrics inside a DRAGEN-style export folder.
  2. Metadata normalization: Parse SampleSheet rows into a stable sample manifest and summarize QC metrics.
  3. Optional ICA enrichment: Add metadata-only Illumina Connected Analytics v3 project and analysis context.
  4. ClawBio handoff: Write report.md, result.json, tables/sample_manifest.csv, and reproducibility artifacts with downstream routing hints.

Input Formats

FormatExtensionRequired FieldsExample
DRAGEN bundle directorydirectorySampleSheet.csv, one *.vcf/*.vcf.gz, one QC filedemo_bundle/
SampleSheet.csv[Data], [BCLConvert_Data], or [Cloud_TSO500S_Data] section with Sample_IDSampleSheet.csv
QC metrics.json, .csv, .tsvrun and quality summary metricsqc_metrics.json, MetricsOutput.tsv

Workflow

  1. Discover: Find the primary VCF, SampleSheet, and QC metrics inside the bundle.
  2. Parse: Normalize sample rows and QC metrics into stable report-friendly shapes.
  3. Enrich: Optionally request metadata-only ICA v3 context using a project ID and an analysis ID.
  4. Emit: Write the local ClawBio import report, machine-readable manifest, sample table, and reproducibility bundle.

CLI Reference

bash
# Standard usage
python skills/illumina-bridge/illumina_bridge.py \
  --input <bundle_dir> --output <report_dir>

# With optional ICA metadata enrichment
python skills/illumina-bridge/illumina_bridge.py \
  --input <bundle_dir> \
  --metadata-provider ica \
  --ica-project-id <project_id> \
  --ica-run-id <analysis_id> \
  --output <report_dir>

# Demo mode
python skills/illumina-bridge/illumina_bridge.py --demo --output /tmp/illumina_demo

# Via ClawBio runner
python clawbio.py run illumina --input <bundle_dir> --output <dir>
python clawbio.py run illumina --demo

Demo

bash
python clawbio.py run illumina --demo

Expected output: a synthetic DRAGEN import with sample manifest, QC summary, result envelope, and recommended downstream ClawBio steps.

Offline ICA demo:

bash
ILLUMINA_ICA_API_KEY= python skills/illumina-bridge/illumina_bridge.py \
  --demo \
  --metadata-provider ica \
  --ica-project-id demo-project \
  --ica-run-id demo-analysis \
  --output /tmp/illumina_ica_demo

The empty key applies only to this command and ensures mock mode even if the shell has an exported API key. --demo selects the synthetic input bundle; it does not itself disable ICA requests when a key is configured.

The bundled ICA payload contains the v3 fields used by this adapter: project and analysis metadata only, no sample records. tables/sample_manifest.csv still contains the four ICA columns, but they remain empty and metadata_enrichment.merge.samples_enriched is 0.

Minimal ICA Maintainer Setup

Use environment variables. Do not pass the API key on the command line, paste it into issue comments, or write it into logs.

bash
export ILLUMINA_ICA_API_KEY="<redacted-api-key>"
export ILLUMINA_ICA_BASE_URL="https://ica.illumina.com/ica/rest"

python clawbio.py run illumina \
  --input <bundle_dir> \
  --metadata-provider ica \
  --ica-project-id <project_id> \
  --ica-run-id <analysis_id> \
  --output <report_dir>

ILLUMINA_ICA_BASE_URL defaults to https://ica.illumina.com/ica/rest. Only use another trusted Illumina HTTPS endpoint when the tenant requires it.

--ica-run-id is the ICA analysis ID for this PR. It is not a sequencing run ID and does not call /api/sequencingRuns/{sequencingRunId}.

Algorithm / Methodology

  1. Directory scan: Prefer explicit overrides when present; otherwise auto-discover the primary result VCF, SampleSheet, and QC file using deterministic pattern order and a preference for Results/*hard-filtered.vcf.
  2. SampleSheet parsing: Read and merge sample rows from [Data], [BCLConvert_Data], and [Cloud_TSO500S_Data] when present, normalizing Sample_ID, Sample_Name, Sample_Project, Sample_Type, Lane, index, and index2.
  3. QC normalization: Accept JSON, CSV, or DRAGEN MetricsOutput.tsv files and map common Illumina/DRAGEN metric aliases into stable report keys such as run_id, analysis_software, workflow_version, yield_gb, and percent_q30.
  4. Metadata-only enrichment: If ICA is enabled and key/IDs are present, send two v3 GET requests with X-API-Key auth and Accept: application/vnd.illumina.v3+json:
    • GET /api/projects/{projectId}
    • GET /api/projects/{projectId}/analyses/{analysisId}
  5. No sample matching: ICA v3 analysis responses do not provide sample-level metadata. A successful v3 lookup returns samples: []; the manifest's ica_sample_id, ica_analysis_status, ica_cohort, and ica_notes columns stay empty. Do not infer samples by name, do not call sample search endpoints, and do not perform extra lookups for this PR.
  6. Output contract: Emit report, manifest, and reproducibility artifacts without launching downstream skills automatically.

ICA v3 Result Contract

Successful ICA enrichment reads project and analysis metadata only. The fields below are under data in result.json; summary.metadata_status mirrors data.metadata_enrichment.status.

FieldMeaning
metadata_enrichment.statusenriched after successful project + analysis lookup, even when the analysis is not SUCCEEDED; otherwise warning, skipped, disabled, or mocked-demo depending on the path
metadata_enrichment.project.activeBoolean from ICA v3 Project.active; if the field is absent, store null
metadata_enrichment.project.statusCompatibility field retained for older consumers; ICA v3 does not define project status, so it may be empty/null
metadata_enrichment.run.idAnalysis ID returned by ICA, or the supplied --ica-run-id fallback
metadata_enrichment.run.nameAnalysisV3.userReference; if absent, fall back to reference
metadata_enrichment.run.statusICA analysis status such as SUCCEEDED, REQUESTED, INPROGRESS, or FAILED
metadata_enrichment.run.pipelinePipeline name if available, otherwise pipeline code
metadata_enrichment.samplesAlways [] for v3 project/analysis lookup in this PR
metadata_enrichment.merge.samples_enriched0 for v3 lookup and the offline mock

Reports must show the analysis status. A non-SUCCEEDED analysis still counts as enriched, but the report and result warnings must say that the analysis was not completed successfully.

ICA Error Handling

The local import must finish whenever possible. ICA failures should be warnings, not hard failures.

  • Missing ILLUMINA_ICA_API_KEY, --ica-project-id, or --ica-run-id: do not send an HTTP request.
  • 401: check the API key.
  • 403: check permissions for the tenant/project/analysis.
  • 404: check the project ID, analysis ID, and project access.
  • 429/5xx: warn and suggest retrying later; keep the local import output. This adapter does not retry automatically.
  • Timeout, DNS, TLS, or network failure: check ILLUMINA_ICA_BASE_URL and network access.
  • Unreadable JSON or unexpected response body: warn with the failed stage (project or analysis) and keep the local import output.

Example Queries

  • "Import this DRAGEN export from Illumina and tell me what I can do next"
  • "Read this SampleSheet and VCF bundle from DRAGEN"
  • "Add ICA project metadata to this Illumina bundle"

Output Structure

output_directory/
├── report.md
├── result.json
├── tables/
│   └── sample_manifest.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Dependencies

Required:

  • requests — optional ICA metadata lookup

Optional:

  • ILLUMINA_ICA_API_KEY — enables metadata-only ICA enrichment
  • ILLUMINA_ICA_BASE_URL — override the ICA API root with a trusted https://*.illumina.com endpoint if needed

Safety

  • Local-first: genomic files are read locally; the skill never uploads VCF payloads
  • Metadata-only cloud access: ICA enrichment is opt-in and limited to project/analysis metadata
  • No command-line secrets: ILLUMINA_ICA_API_KEY belongs in the environment, not in CLI flags or logs
  • Disclaimer: every report includes the ClawBio medical disclaimer
  • Reproducibility: commands, environment context, and checksums are always written

Known Limits

  • Issue #74's 2026-09-28 comment reports a contributor's live check against a scratch ICA environment and a small non-DRAGEN analysis. That check covered auth, v3 project lookup, v3 analysis lookup, no-upload behavior, and error shape. Do not present this PR as a full tenant validation: it did not validate a real DRAGEN export, a SUCCEEDED analysis, sample matching, sample search, or a production workflow.
  • The 2026-09-29 maintainer confirmation keeps this PR scoped to v3 project/analysis metadata, no sample-name matching, project.active plus compatible project.status, and --ica-run-id as an analysis ID.
  • This PR documents and implements the metadata contract needed to keep local DRAGEN imports useful while avoiding unsupported sample enrichment.

Testing

Offline checks:

bash
python skills/illumina-bridge/illumina_bridge.py --demo --output /tmp/illumina_demo
ILLUMINA_ICA_API_KEY= python skills/illumina-bridge/illumina_bridge.py \
  --demo --metadata-provider ica \
  --ica-project-id demo-project \
  --ica-run-id demo-analysis \
  --output /tmp/illumina_ica_demo
python -m pytest skills/illumina-bridge/tests/ -v

Maintainer-only live check:

bash
export ILLUMINA_ICA_API_KEY="<redacted-api-key>"
export ILLUMINA_ICA_BASE_URL="https://ica.illumina.com/ica/rest"

python clawbio.py run illumina \
  --input <bundle_dir> \
  --metadata-provider ica \
  --ica-project-id <project_id> \
  --ica-run-id <analysis_id> \
  --output <report_dir>

Use placeholder IDs in docs and logs. Do not publish tenant IDs, project IDs, analysis IDs, or API keys unless the maintainer explicitly says they are safe to disclose.

Integration with Bio Orchestrator

Trigger conditions:

  • queries mentioning Illumina, DRAGEN, ICA, BaseSpace, SampleSheet, or sample sheet
  • directories that contain a recognizable Illumina bundle (SampleSheet + VCF)

Chaining partners:

  • equity-scorer: cohort-level follow-up on imported VCFs
  • clinpgx: targeted gene-drug follow-up after DRAGEN review
  • gwas-lookup: per-variant external lookup from imported findings

Citations

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 Illumina Bridge AI skill do?

Import DRAGEN-exported Illumina result bundles into ClawBio for local tertiary analysis and downstream routing.

Why use Illumina Bridge on TypingMind?

Because you install it once and use it with any model. Illumina Bridge 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 Illumina Bridge in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ClawBio/ClawBio/tree/main/skills/illumina-bridge. 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 Illumina Bridge?

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 Illumina Bridge?

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

Is the Illumina Bridge AI skill free?

Yes. It is published on GitHub by ClawBio 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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