ACMG Variant Classifier

Standard workflow for ACMG/AMP germline small-variant classification — collect evidence, assign criteria, detect conflicts, and produce a review-ready classi...

Security Scan

The skill's files, instructions, and tiny Python classifier are consistent with an ACMG/AMP decision‑support workflow and request no unrelated privileges or secrets.

Purpose & Capability

Name and description (ACMG variant classification) match the included materials: templates, SOP, test cases, and a small classifier script. Nothing requested (no env vars, no binaries) is out of scope for this purpose.

Instruction Scope

SKILL.md gives a narrow, guided interview workflow and describes exactly which fields to collect and how to apply combination logic. Instructions do not ask for unrelated files, system state, or external endpoints; they explicitly state this is decision support only.

Install Mechanism

No install spec — instruction-only with an included Python script. There are no downloads, external packages, or extract/install steps. The script is local and self-contained.

Credentials

The skill requires no environment variables, credentials, or config paths. The inputs it asks the user to provide (variant, phenotype, segregation, literature, etc.) are appropriate for ACMG classification.

Persistence & Privilege

Skill is not always-enabled and does not request persistent or elevated privileges. It does not modify other skills or system-wide settings.

Assessment

This appears to be a straightforward decision-support skill: the included Python classifier only applies combination rules to counts (no network calls or secret access). Before installing, confirm you are comfortable providing genetic/clinical data to the agent (these are sensitive), ensure local privacy/PHI policies are followed, and understand the skill is explicitly for provisional review only — final clinical classification requires expert manual review and possible ClinGen VCEP adjustments. If you will deploy this in a regulated environment, have a domain expert review the SOP and the classifier logic and confirm any gene-specific rule changes.

ACMG Variant Classification

Use this skill when a user wants a structured ACMG/AMP-style interpretation workflow for a germline SNV/indel.

Interaction mode

Default to a guided interview workflow.

When using this skill with a live user:

1. Ask for one block of information at a time
2. Wait for the user's answer before moving on
3. Do not request all evidence at once unless the user asks for a bulk template
4. Explicitly track what is known, unknown, and still needed
5. Treat phenotype, family history, segregation data, and parental genotypes as user-supplied inputs that may arrive incrementally

Recommended guided sequence:

1. Variant identity: gene, transcript, build, c.HGVS, p.HGVS, variant type
2. Clinical phenotype / suspected disease
3. Inheritance model and family structure
4. Parental genotype status and de novo / segregation details
5. Population / database / literature evidence
6. Functional and computational evidence
7. Criteria assignment and final review

At each step, summarize back in one compact block:

• confirmed facts
• missing facts
• provisional ACMG implications

Safety / scope

Always say clearly:

• This is decision support, not a final clinical diagnosis.
• Gene/disease-specific ClinGen guidance overrides generic ACMG rules where applicable.
• Final classification requires expert manual review.

Inputs you should collect

Use templates/intake.md and ask for or normalize these fields:

• Gene
• Transcript
• Genome build
• c.HGVS
• p.HGVS
• Variant type
• Zygosity
• Inheritance model
• Phenotype / disease context
• Population frequency evidence
• Functional evidence
• Segregation / de novo evidence
• Database assertions
• Literature evidence

If transcript, genome build, or HGVS is unclear, stop and ask for clarification before classification.

Standard workflow

Step 1: Confirm scope

Proceed only if all are true:

1. Variant is a germline small variant (SNV/indel)
2. Naming/build/transcript are defined
3. User understands output is review-only
4. Any gene-specific ACMG framework has been checked

Step 2: Normalize the record

Create a clean variant record using templates/intake.md.

Step 3: Gather evidence by ACMG bucket

Pathogenic side:

• PVS1
• PS1, PS2, PS3, PS4
• PM1, PM2, PM3, PM4, PM5, PM6
• PP1, PP2, PP3, PP4

Benign side:

• BA1
• BS1, BS2, BS3, BS4
• BP1, BP2, BP3, BP4, BP5, BP7

Step 4: Assign criteria carefully

Use templates/evidence-table.md. For each criterion, record:

• code
• strength
• triggered yes/no
• reason
• source
• caveat / limitation

Do not double count overlapping evidence.

Step 5: Evaluate conflicts

If both pathogenic and benign evidence exist:

1. Check whether evidence is truly independent
2. Downgrade/remove misapplied criteria if needed
3. If conflict remains unresolved, prefer VUS over forced certainty
4. State what additional data could resolve the conflict

Step 6: Apply combination logic

Use scripts/classifier.py or reproduce its logic manually.

Pathogenic if any:

• 1 Very Strong + >=1 Strong
• 1 Very Strong + >=2 Moderate
• 1 Very Strong + 1 Moderate + 1 Supporting
• 1 Very Strong + >=2 Supporting

• =2 Strong

• 1 Strong + >=3 Moderate
• 1 Strong + 2 Moderate + >=2 Supporting
• 1 Strong + 1 Moderate + >=4 Supporting

• =3 Moderate + >=3 Supporting

Likely Pathogenic if any:

• 1 Very Strong + 1 Moderate
• 1 Strong + 1 to 2 Moderate
• 1 Strong + >=2 Supporting

• =3 Moderate

• 2 Moderate + >=2 Supporting
• 1 Moderate + >=4 Supporting

Benign if any:

• BA1

• =2 Strong benign criteria

Likely Benign if any:

• 1 Strong benign + 1 Supporting benign

• =2 Supporting benign

Else: VUS

Guided questioning pattern

Use short, sequential prompts:

• Step A: ask only for variant identity fields
• Step B: ask only for phenotype and suspected diagnosis
• Step C: ask only for pedigree / family history / inheritance
• Step D: ask only for parental genotypes and segregation/de novo details
• Step E: ask only for outside evidence such as ClinVar, literature, frequency, and functional assays
• Step F: summarize triggered or candidate ACMG criteria before giving a provisional class

Included files

• templates/intake.md
• templates/evidence-table.md
• references/sop.md
• references/test_cases.json
• scripts/classifier.py