How to mint a citation. Every fact FKs to a citations row; citations verify against documents.content (never disk) at insert time and are immutable after.

Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.

Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.

Answer a question across a corpus of contract documents with verified citations. Use when the user asks what a contract says, which contracts have a clause, what changed between amendments, or any question that needs reading and citing across a set of contract files. The corpus must be on the local filesystem (see README).

Extract plain text from a document file - PDF, DOCX, XLSX, PPTX, RTF, or plain text/markdown/HTML. Use when a binary document needs to be turned into text, for example a contract PDF or an EHR DocumentReference attachment. Other skills (contracts, fhir) invoke scripts/extract.ts directly.

Connect to a hospital's FHIR R4 server (Epic, Oracle Health/Cerner, MEDITECH, athenahealth, or any SMART-on-FHIR endpoint), pull a patient's clinical data and notes, and extract structured findings. Use when users say "connect to the EHR", "connect to Epic/Cerner", "pull notes for patient X", "what do the last 6 months of notes say about Y", or any task that starts from a live EHR rather than pasted text.

FHIR API development guide for building healthcare endpoints. Use when: (1) Creating FHIR REST endpoints (Patient, Observation, Encounter, Condition, MedicationRequest), (2) Validating FHIR resources and returning proper HTTP status codes and error responses, (3) Implementing SMART on FHIR authorization and OAuth scopes, (4) Working with Bundles, transactions, batch operations, or search pagination. Covers FHIR R4 resource structures, required fields, value sets (status codes, gender, intent), c

Screen a Medicare/Medicaid claims corpus for fraud, waste, and abuse and produce ranked, fully-cited investigation referrals for an SIU / program-integrity team. Use when asked to run a fraud sweep, screen claims for FWA, find billing anomalies, or generate investigation referrals over a claims dataset.

Extract billable ICD-10-CM diagnosis codes from a clinical note the way a professional coder builds the claim. Use when users say "code this encounter", "assign ICD-10 codes", "what diagnosis codes apply", "code this chart", or when turning clinical documentation into claim-ready diagnosis codes.

After a run completes, propose durable facts learned during the run for the knowledge index. A human ratifies — always. Proposals ride the queue.

Automate payer review of prior authorization (PA) requests. This skill should be used when users say "Review this PA request", "Process prior authorization for [procedure]", "Assess medical necessity", "Generate PA decision", or when processing clinical documentation for coverage policy validation and authorization decisions.

Assign CPT and HCPCS Level II procedure codes from clinical documentation the way a professional coder builds the claim. Use when users say "code this encounter for procedures", "what CPT codes apply", "assign HCPCS codes", "code this op note", or when turning visit notes or operative reports into claim-ready procedure codes.

After a sweep round, dedupe and triage worker unknowns into the human queue. Self-resolve the obvious (visibly); blocking items end the round.

Turn the user's question into a versioned BRIEF (rubric, stated assumptions, done-criteria, scope intent). Use before any sweep. Consults the knowledge index and may pre-scan a few documents to sharpen terms.

Fan out over the scoped document set via the saved sweep workflow. Workers full-read their shard and write findings + citations directly via cli.ts. Recall over precision — never skip a scoped doc.

After sweep rounds settle, turn findings into a report with cited claims, run a sampled semantic audit, and surface knowledge proposals. Analysis happens via scripts, never mental math.