Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.

Run stepped HTTP load tests with ab/wrk, ramping concurrency levels to collect p50/p90/p99 latency, detect performance inflection points, and recommend optimal concurrency. Triggered by requests like 'load test this URL', 'benchmark my API', 'find the max concurrency', or mentions of p99 latency, throughput saturation, or capacity planning.

Authentication patterns: session vs JWT vs OAuth comparison, provider selection (NextAuth, Clerk, Supabase Auth), security checklist, and common mistakes. Use when implementing auth, reviewing auth flows, or choosing auth providers.

Visualise the result of an analysis as a chart (line, bar, area, scatter, etc.). Use when the user asks to "plot...", "chart...", "show me the trend of...", "visualise...", or when a numerical result has more than ~10 rows and would be easier to read as a picture. Produces an image file plus the script that generated it.

Split a clinical note into canonical sections (Chief Complaint, HPI, PMH, Medications, Allergies, Assessment & Plan, etc.) before running OpenMed NER or de-identification, so section context sharpens downstream precision. Use when the user has a free-text note or discharge summary and wants section-aware processing, header detection, mapping headers to LOINC document-section codes, or per-section NER/de-id. Covers heuristic header detection, normalization to canonical section labels, LOINC/SecTag framing, and why a finding in PMH is historical while the same finding in A&P is active. Hand-off: feed each sectioned chunk into openmed.analyze_text / openmed.deidentify. Pairs before extracting-clinical-entities.

Extract arbitrary, custom entity types from clinical or biomedical text with no fine-tuning using OpenMed's GLiNER / GLiNER2 zero-shot support. Use when the user wants to define their own labels on the fly (e.g. Drug, Symptom, Device, Procedure), has no labelled data or a label set not covered by a fine-tuned model, or asks about openmed zero deps/index/infer, the gliner extra, or GLiNER. Pairs adjacent to extracting-clinical-entities (use that for high-accuracy fixed-schema NER) and loading-openmed-models.

Replace detected PHI with realistic, type-matched fake values in OpenMed so clinical notes stay readable and parseable instead of full of [REDACTED] markers. Use when the user wants surrogate names, MRNs, addresses, or dates rather than opaque masks, needs consistent fake identities across a document, must keep notes natural for downstream NLP, or wants to register a custom surrogate generator or provider. Covers deidentify(method="replace", consistent=True, seed=..., locale=...), register_label_generator, register_clinical_provider, and Anonymizer/AnonymizerConfig. Pairs with OpenMed deidentifying-clinical-text and configuring-privacy-policies.

Map OpenMed-extracted, terminology-coded conditions, drugs, and measurements into OMOP CDM v5.4 clinical tables (condition_occurrence, drug_exposure, measurement) for OHDSI/ATLAS analytics. Use when the user wants to load NLP-derived facts into an OMOP database, build an OHDSI ETL from clinical notes, populate condition_occurrence or drug_exposure from text, or standardize note-derived findings to OMOP standard concepts. Covers the source-to-standard concept mapping pattern, required vs optional CDM fields, type concepts for NLP-derived rows, and the user-supplied OHDSI vocabulary (CONCEPT/CONCEPT_RELATIONSHIP). Consumes coded OpenMed analyze_text output (after SNOMED/RxNorm/LOINC linking) and produces OMOP-conformant rows.

Assemble a chronological patient timeline from OpenMed-extracted clinical events, normalizing dates and resolving relative time expressions on-device. Use when the user wants to build a patient timeline, order events from clinical notes, reconstruct a longitudinal history, plot a course of illness, or turn analyze_text/deidentify output into a sorted sequence of dated encounters, diagnoses, medications, and procedures. Covers temporal normalization (absolute and relative), event modeling toward FHIR Encounter/Condition.onsetDateTime, anchoring to a document/admission date, and handling undated or ambiguous events. Consumes OpenMed analyze_text entities plus clinical temporality (resolving-clinical-context); produces a sorted event list ready for charting or FHIR export.

Combine OpenMed clinical NLP with Microsoft Presidio, spaCy, or LangChain through OpenMed's built-in interop adapter registry (openmed.interop). Covers the lazy adapter registry (available_adapters, get_adapter, adapter_spec), the presidio/spacy/langchain pip extras, and the verified callables — Presidio to_canonical/from_canonical/merge_with_openmed, the spaCy openmed_deid pipeline factory, and the LangChain create_redaction_runnable. Use when the user wants to add Presidio recognizers, embed OpenMed PII detection in a spaCy pipeline, or use OpenMed de-identification as a LangChain runnable. Pairs adjacent to the OpenMed PII skills.

Vercel deployment and CI/CD expert guidance. Use when deploying, promoting, rolling back, inspecting deployments, building with --prebuilt, or configuring CI workflow files for Vercel.

Send and verify one-time passcodes (OTPs) via Twilio Verify over SMS, RCS, voice, email, or WhatsApp. Covers creating a Verify Service, sending tokens, checking submitted codes, automatic WhatsApp-to-SMS fallback, and service configuration. TOTP is supported via the Factors API (a separate family from channel-based OTP). Use this skill to add phone or email verification or two-factor authentication to any application.

SMS and MMS deep-dive reference. Covers SMS-specific error codes, message filtering troubleshooting ("Messages Being Filtered or Blocked?" diagnostic checklist), MMS media support (US/CA/AU only), and SMS pumping indicators. For sending SMS, use twilio-send-message instead. Use this skill only when debugging SMS delivery issues or needing SMS-specific details not in the consolidated send skill.

Receive and respond to inbound messages and track outbound delivery status via Twilio webhooks — across SMS, MMS, WhatsApp, and RCS. Covers webhook request parameters, replying with TwiML, validating webhook signatures for security, and handling status callbacks. Use this skill whenever an agent needs to handle incoming messages on any channel or track outbound message delivery in real time.

Validate that a Dynamo deployment's NIXL/UCX/NCCL interconnect is ready for disaggregated serving over RDMA/NVLink. Use after recipe-runner brings a deployment up (especially disagg/multi-node) to confirm the KV transport is correct; use troubleshoot for diagnosing already-failed pods.

Translates Figma motion and animations into production-ready application code. Use when implementing animation/motion from a Figma design — user mentions "implement this motion", "add animation from Figma", "animate this component", provides a Figma URL whose node is animated, or when `get_design_context` returns motion data or instructs you to call `get_motion_context`.

Record Twilio voice calls correctly. Covers the critical distinction between Record verb (voicemail) and Dial record (call recording), dual-channel for QA, mid-call pause for PCI, Conference recording, and the ConversationRelay workaround. Use this skill whenever you need to capture call audio for compliance, QA, or analytics.

Use when Codex is asked to build, run, test, debug, or configure an OpenAI-backed or provider-unspecified AI app, UI, script, CLI, generator, or tool, especially requests phrased only as "using AI" or generators driven by forms/user input; also use for OPENAI_API_KEY or sk-proj setup. Treat this as the credential gate: inspect safely, ask reuse-vs-new before API work, and never expose plaintext.

Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.

Submit compact RNAcentral API requests for RNA entry browsing, single-entry lookup, and cross-reference retrieval. Use when a user wants concise RNAcentral summaries