clinical-reports skill
Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.
Is the clinical-reports skill safe?
Clean: nothing in its files matched our rules. We read 36 files in the folder on 2026-09-28.
No findings.
Install the clinical-reports skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills mkdir -p ~/.claude/skills cp -r /tmp/scientific-agent-skills/skills/clinical-reports ~/.claude/skills/clinical-reports
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
Clinical Reports
Purpose
Prepare draft reporting structures, aggregate tables, and review manifests from verified authorized facts. Route each artifact to the correct reporting guidance, preserve provenance, and stop when source support or qualified review is missing.
This skill does not establish legal, regulatory, ethical, journal, accreditation, or institutional compliance. Its scripts check structure and internal consistency only.
Non-Negotiable Boundary
Never:
- diagnose, recommend treatment, choose or change dosing, triage, or provide return precautions;
- interpret images, specimens, raw laboratory results, symptoms, or other clinical observations;
- invent, infer, normalize, “complete,” or silently reconcile observations, results, dates, units, denominators, causality, expectedness, seriousness, outcomes, or conclusions;
- create an individual case safety report from patient-level narrative or decide reportability;
- sign, attest, approve, file, transmit, submit, amend a source record, or act as a licensed clinician, pathologist, radiologist, laboratorian, safety physician, statistician, privacy officer, attorney, or regulatory professional;
- use real PHI in examples, assets, tests, prompts, logs, or external services;
- call an external LLM, image service, API, or another skill.
All generated artifacts must remain visibly marked:
DRAFT — NOT FOR CLINICAL USE, SIGNATURE, FILING, OR SUBMISSION. Populate only from verified authorized source records. Qualified review and sign-off are required.
If the request crosses a boundary, stop the unsafe portion. Offer a blank structured template, a source-fact manifest, or a deterministic structural check. Direct clinical or regulatory decisions to the responsible qualified professional.
Input Gate
Proceed only when all conditions are true:
- Purpose is explicit: publication draft, diagnostic-report scaffold, trial-results manuscript, protocol reporting review, CSR draft, aggregate safety table, or aggregate research summary.
- Data class is allowed: synthetic, deidentified, or aggregate.
- Authority is documented: the requester is authorized to use the records for the stated purpose.
- Local-only handling is feasible: no upload, remote API, telemetry, or credential is needed.
- Minimum necessary is defined: exclude fields not needed for the artifact.
- Provenance exists: every populated field or claim maps to one or more verified source-fact IDs.
- Review owner is identified: qualified clinical, statistical, safety, privacy, legal, journal, and/or regulatory review as applicable.
Do not accept raw free-text patient records when a structured source-fact manifest can be supplied. Do not copy direct identifiers into this skill’s templates or scripts.
Route Before Drafting
Read references/reporttyperouting.md before choosing a route. Use the dated primary-source ledger in references/sources.md; check the live official source when requirements could have changed.
Safe Drafting Workflow
1. Create a source-fact manifest
Use assets/provenancemanifesttemplate.json. Record only local record locators, field paths, verification state, verifier role, verification date, and a SHA-256 value hash. Do not duplicate source content or direct identifiers.
Every draft claim or populated field must cite one or more fact IDs. Unsupported content remains null or missing; never replace it with plausible text.
2. Generate the correct template
PYTHONDONTWRITEBYTECODE=1 python3 scripts/generate_report_template.py --list
PYTHONDONTWRITEBYTECODE=1 python3 scripts/generate_report_template.py \
--type case-report \
--output ./case-report-draft.jsonThe generator copies a fail-closed JSON template. It does not populate clinical content, create directories, overwrite files by default, or certify readiness.
3. Populate verified fields only
- Keep draft_status unchanged.
- Replace null only when a verified fact ID supports the field.
- Preserve uncertainty and “not assessed” exactly as recorded.
- Do not translate a raw observation into a diagnosis, code, grade, stage, seriousness, causality, expectedness, or recommendation.
- Use notapplicablewith_rationale only when a qualified reviewer supplied the rationale.
- Keep source record and draft separate.
4. Run deterministic checks
CARE structure:
PYTHONDONTWRITEBYTECODE=1 python3 scripts/validate_case_report.py \
./case-report-draft.jsonICH E3, CONSORT 2025, or SPIRIT 2025 structure:
PYTHONDONTWRITEBYTECODE=1 python3 scripts/validate_trial_report.py \
./trial-report-manifest.jsonAggregate adverse-event table:
PYTHONDONTWRITEBYTECODE=1 python3 scripts/format_adverse_events.py \
./aggregate-ae.csv --metadata ./safety-aggregate.json \
--output ./aggregate-ae-table.mdTerminology schema:
PYTHONDONTWRITEBYTECODE=1 python3 scripts/terminology_validator.py \
./terminology-manifest.jsonDe-identification process documentation:
PYTHONDONTWRITEBYTECODE=1 python3 scripts/check_deidentification.py \
./deidentification-process.jsonTraceability and consistency:
PYTHONDONTWRITEBYTECODE=1 python3 scripts/provenance_validator.py ./provenance.json
PYTHONDONTWRITEBYTECODE=1 python3 scripts/consistency_checker.py ./consistency.jsonThese tools use the Python standard library, local bounded files, and no network, dynamic evaluation, serialization code execution, or patient-record extraction. A successful result still says review is required.
5. Apply the right review
At minimum:
- clinical facts and interpretations: qualified clinician for the specialty;
- statistical results, populations, estimands, denominators, and missingness: qualified statistician;
- safety coding, seriousness, causality, expectedness, and reportability: qualified safety professional;
- HIPAA, consent, authorization, and disclosure: privacy/legal/institutional review;
- CSR or regulatory safety output: sponsor regulatory and medical review;
- publication: all accountable authors and target-journal checks.
Never sign or submit on another person’s behalf.
Case Reports
Use assets/casereporttemplate.json and references/casereportguidelines.md.
- CARE’s current core checklist remains the 2013 checklist.
- Report only what the verified record supports.
- Do not turn a case into clinical advice or generalize causality from one case.
- Patient perspective and informed-consent status must be recorded accurately; do not draft a false consent statement.
- De-identification and consent are separate controls. Consent does not erase privacy risk.
Diagnostic Report Scaffolds
Use the radiology, pathology, or laboratory JSON asset and references/diagnosticreportsstandards.md.
- The assets are field maps, not diagnostic authoring systems.
- Never generate findings, impressions, diagnoses, grades, stages, reference intervals, critical thresholds, or follow-up recommendations.
- Preserve preliminary/final/corrected status and source-system version.
- Use current, exact CAP protocol and version for the specimen; do not maintain a generic cancer staging default.
- Communication and correction actions remain with the responsible clinical service.
The former SOAP, H&P, consultation, and discharge-summary interfaces were removed. Do not recreate patient-care notes, medication plans, triage instructions, billing support, or disposition advice.
Trial, CSR, and Safety Reporting
Read references/clinicaltrialreporting.md and references/safety_reporting.md.
- CONSORT 2025 has 30 minimum items for randomized-trial results; select relevant extensions from the current official catalogue.
- SPIRIT 2025 has 34 minimum items for randomized-trial protocols and supersedes SPIRIT 2013.
- ICH E3 remains the CSR basis; its 2012 Q&A explicitly permits justified adaptation.
- ICH E6(R3) consolidated Principles, Annex 1, and Annex 2 were adopted on 16 June 2026; regional implementation can differ.
- Distinguish seriousness from severity and an adverse event from a suspected adverse reaction.
- ICH E2B(R3) defines electronic ICSR data/message structure; it is not an aggregate-table format or a reportability decision rule.
- ICH E2D(R1), adopted 15 September 2025, addresses post-approval individual case safety reporting; aggregate periodic reporting is addressed separately.
- FDA requirements and electronic submission routes are role-, product-, study-, and date-specific. This skill never files or transmits.
Privacy
Read references/privacyanddeidentification.md.
- Handle only the minimum necessary data locally.
- HHS recognizes Safe Harbor and Expert Determination under 45 CFR 164.514(b).
- Safe Harbor also requires no actual knowledge that remaining information can identify an individual.
- Expert Determination must be performed and documented by an appropriately qualified expert.
- A checklist or pattern scan cannot establish de-identification or HIPAA compliance.
- Rare conditions, small cells, dates, free text, images, metadata, and combinations of quasi-identifiers can retain re-identification risk.
Assets
All assets contain synthetic schemas only and start blocked:
- assets/casereporttemplate.json
- assets/radiologyreporttemplate.json
- assets/pathologyreporttemplate.json
- assets/labreporttemplate.json
- assets/clinicaltrialcsr_template.json
- assets/clinicaltrialresults_template.json
- assets/trialprotocolreporting_checklist.json
- assets/clinicaltrialsafetyaggregatetemplate.json
- assets/adverseeventaggregateinputtemplate.csv
- assets/researchsummarytemplate.json
- assets/deidentificationprocesschecklist.json
- assets/qualityreviewchecklist.json
References
More skills from K-Dense-AI/scientific-agent-skills
- AadaptyvHow to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
- AaeonThis skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
- AalphagenomeLook up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.
- Aanalytical-method-validationPlan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
- AanndataData structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
- AarborAutonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
- AarboretoInfer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
- AastropyCore Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
- AautoskillObserve the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
- Abenchling-integrationBenchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
- Abgpt-paper-searchSearch scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
- AbidsUse this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.