Clinical Decision
Protocol Skills
A standardised, version-controlled format for packaging specialty clinical knowledge, decision protocols, and institutional guidelines into portable AI skills — deployable across specialties, hospitals, and clinicians in DoctorAssist.AI.
What are Clinical Agent Skills?
Clinical Agent Skills are structured, portable packages of medical expertise. Each skill encodes a specialty's decision logic — guidelines, risk scores, dosing rules, alert thresholds, and institutional protocols — into a folder that DoctorAssist.AI agents load on demand at the point of care.
Modelled on the open Agent Skills standard, clinical skills carry additional medical metadata: evidence grades, guideline sources (AHA, ESC, WHO, NCCN), specialty context, and patient safety constraints built into the architecture rather than applied as an afterthought.
Agents load only a skill's name and description at startup. The full clinical protocol loads into context only when a matching patient scenario is detected — keeping the AI's reasoning efficient and precise without overwhelming the context window.
The SKILL.md Format
Every clinical skill is a folder with a SKILL.md file as its entrypoint, using frontmatter for metadata and Markdown for clinical instructions. Additional directories bundle calculators, drug protocols, and reference guidelines.
How Skills Work in Practice
DoctorAssist.AI agents use a three-stage progressive disclosure model to activate clinical skills at the right moment — without loading unnecessary context or adding friction to the clinical workflow.
At session start, the agent loads only each skill's name and description fields. This gives it a complete map of available clinical expertise without loading any protocol detail into active memory.
As the agent reads incoming data — labs, vitals, notes, orders — it matches clinical signals to skill descriptions. A rising troponin activates the Cardiology ACS skill. A new lung mass activates the Oncology skill. Activation is automatic, no prompting required.
The full SKILL.md loads into the agent's reasoning context. It follows the decision workflow, cross-references bundled guidelines, runs calculators, and surfaces evidence-linked recommendations before you ask.
Available Specialty Skills
DoctorAssist.AI ships with a core library of specialty skills, each maintained against the latest international guidelines. Institutions can extend, fork, or layer these with local protocols.
ACS protocols, HEART score, heart failure staging, arrhythmia management, and antiplatelet therapy — aligned to AHA/ACC 2024 and ESC 2023.
Staging, molecular profiling, regimen selection, toxicity grading, MDT summaries, and palliative transitions across solid tumours and haematological malignancies.
Acute stroke pathways (NIHSS, tPA eligibility, thrombectomy window), epilepsy management, Parkinson's titration, and neuro ICU monitoring protocols.
Sepsis bundles (Surviving Sepsis 2024), ventilator management, vasopressor titration, ARDS protocols, and haemodynamic monitoring decision trees.
Diabetes management (ADA 2025), insulin titration, DKA protocol, thyroid workup, adrenal crisis management, and HbA1c stratification by comorbidity.
AKI staging (KDIGO), CKD dose adjustment, contrast nephropathy prevention, renal replacement therapy thresholds, and electrolyte correction protocols.
Adding a Specialty Skill
Any licensed clinician or hospital administrator with author access can add a specialty skill to their DoctorAssist.AI instance — sourced from the Skill Registry, forked from another institution, or authored from scratch.
Step-by-step: Deploy Oncology to your instance
In your DoctorAssist.AI dashboard, go to Settings → Clinical Skills → Browse Registry. Search by specialty or filter by guideline source, evidence grade, or institution adoption count.
Preview the full SKILL.md, evidence grades, guideline versions, and last-updated date. Each skill shows how many institutions have validated it in production use.
Optionally add an institution.json overlay for local formulary restrictions, department-specific thresholds, or preferred drug brands — without modifying the base skill or breaking future updates.
Deploy hospital-wide, to a single department, or to individual physician accounts. Skills are immediately active for new patient encounters on assigned accounts with no restart required.
Once deployed, the skill begins pattern-matching on all incoming patient data for assigned clinicians — surfacing staging alerts, regimen suggestions, and MDT prompts automatically before the next encounter opens.
Copying Skills: Hospital to Hospital
Skills are portable, version-controlled folders. A validated protocol from one institution can be adopted — with or without customisation — by any other hospital on DoctorAssist.AI in minutes, not months of committee review.
Validated · 847 patients
institution.json applied
| Method | Use Case | Customisation | Time to Deploy |
|---|---|---|---|
| Registry Install | Adopt a published skill as-is | Institution overlay only | < 5 minutes |
| Fork & Customise | Adapt a peer institution's protocol | Full edit access | 1–2 hours |
| Export / Import | Air-gapped or on-prem transfer | Full edit access | < 30 minutes |
| API Sync | Automated multi-site updates | Version-locked or floating | Automated |
Copying Skills: Doctor to Doctor
A senior consultant's curated skill set — refined over years of clinical practice — can be shared directly with a registrar, fellow, or colleague joining the team. This transfers institutional knowledge that no textbook contains.
Skill sharing between clinicians requires administrator approval and full audit logging. All transferred skills inherit the receiving clinician's institution's safety constraints and approval workflows. No action can be taken without the receiving clinician's explicit consent.
From their profile, a senior clinician selects one or more skills and shares via a secure link or direct push to a colleague's account. The package includes the SKILL.md, calculators, and any personalised decision thresholds.
The receiving clinician previews the full protocol before accepting. They can merge it with their existing skills or apply it selectively — for example, only for paediatric oncology patients within their panel.
Every transfer is logged with timestamp, sender, recipient, version, and any post-transfer modifications. This creates a traceable lineage of protocol adoption for compliance, accreditation, and quality governance.
Advantages in Clinical Practice
Standardising clinical decision logic through portable skills has measurable benefits across patient safety, operational efficiency, and institutional knowledge — grounded in how medicine is actually practised.
Patient Safety
When a night registrar takes over, the same evidence-based protocol governs every decision — not memory or habit. A STEMI patient at 3am receives the same cath lab activation logic as at 3pm with the attending consultant present.
Drug protocols embedded in skills automatically adjust recommendations based on the patient's current eGFR, liver enzymes, and weight — catching prescribing errors before orders are placed, not after.
Institutional Knowledge Transfer
A retiring oncologist's curated treatment approach — built from decades of MDT discussions — persists in a skill file rather than departing with them. The institution retains that clinical intelligence in a queryable, auditable form.
Registrars and fellows operate with a consultant-level clinical framework from day one. Skills scaffold decision-making with evidence links and confidence scores, reducing the learning curve without removing clinical autonomy.
Operational Efficiency
For ACS, stroke, and sepsis — where minutes alter outcomes — automated skill activation means the right protocol is in front of the clinician before they finish reviewing the presenting complaint. No search required.
When NCCN releases new oncology guidelines, updating the base skill in the registry propagates the change to every hospital and physician using it — with full changelog visibility and rollback support.
Research and Quality Improvement
When multiple hospitals run the same skill version against similar patient cohorts, DoctorAssist.AI can generate de-identified comparative outcome data — turning everyday clinical practice into real-world evidence.
Because every recommendation links to a specific skill version and guideline clause, adherence can be tracked per-physician, per-department, and per-institution — making audit a continuous feedback loop rather than a periodic event.
The Clinical Skill Registry
DoctorAssist.AI maintains a curated registry of peer-reviewed, guideline-anchored clinical skills. Each entry is versioned, attributed, and carries a transparent evidence grade.
| Skill | Version | Guidelines | Institutions | Updated |
|---|---|---|---|---|
| cardiology-acs-protocol | 3.1.0 | AHA/ACC 2024 · ESC | 42 | Jan 2025 |
| oncology-clinical-decision | 2.4.1 | NCCN v2.2025 · ESMO | 31 | Feb 2025 |
| sepsis-surviving-sepsis | 4.0.0 | Surviving Sepsis 2024 | 58 | Dec 2024 |
| stroke-acute-pathway | 2.2.0 | AHA Stroke 2024 · EAN | 27 | Nov 2024 |
| diabetes-ada-management | 5.0.0 | ADA Standards 2025 | 64 | Mar 2025 |
| aki-kdigo-protocol | 1.8.3 | KDIGO 2024 · ERA-EDTA | 19 | Oct 2024 |
Deploy your first skill
Start with a core specialty from the registry, or bring your own institutional protocols into DoctorAssist.AI in under an hour.