Extract a Markdown heading tree with line numbers before editing or integrating notes. Use when you need to inspect a long Markdown file quickly and choose the best insertion point instead of editing blind.

Organize menus and visit notes with grounded tasting analysis and comparisons to earlier experiences. Use when the user shares a restaurant menu, wants a tasting write-up, or asks to merge a visit into Notes/Gourmet.md without overwriting earlier visits.

Compare competing pull requests for a Pollinations community quest and select the smallest correct solution. Use when reviewing multiple implementations linked to a POLLEN-QUEST issue.

Read before designing a feature that writes to the Devices table, adding audit/history logging, or choosing between a SQLite trigger and a Python hook. Covers the full Devices write-path inventory, the FIELD_SOURCE_MAP *Source attribution system, and event-sourced vs snapshot audit logging tradeoffs.

Package a "claw"-type local-first AI agent (openclaw, nullclaw, zeroclaw, hermes-agent, picoclaw, odysseus, and future siblings) as a classic-confinement snap, consistently with the existing collection in ~/src/github/ai-labs/snaps. Use when creating a new claw snap, fixing build/release/CI workflows for one, or making the collection more consistent — covers snapcraft.yaml structure, version adoption per runtime, bundling Node/Chromium, self-contained Python venvs on core24, why not to patchelf interpreters under classic confinement, the systemd user-service pattern, and the PR/release/check-releases GitHub workflows.

当用户要求把采用 JSON 提交后轮询协议的视频或图片供应商接入 ArcReel，或要求编写、验证、测试、保存自定义调用端点定义时使用。

按发明人/工程师给的材料一趟写出交底书和申请文件；申请问题清单上的缺口最多改三轮。缺技术事实就问人，不编。触发：交底申请一起做、从零出交底和申请、一条龙、帮写交底再出申请、按清单改、会稿、案卷、/patent-docket。

Write PR titles and descriptions the way a staff engineer would. Use when drafting or editing a pull request title and body, before running `gh pr create`, or any time the user asks for a PR description, summary, or release-notes-style writeup of a change. Apply this skill from the start, not as a cleanup pass after a generic first draft.

Translate natural language to SQL, optimize query performance, and interpret EXPLAIN plans for SQLite and PostgreSQL. Triggered when users ask to convert questions into SQL, improve slow queries, tune indexes, analyze execution plans, or mention keywords like NL2SQL, query tuning, or full table scan.

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.

Use this skill when the task involves analyzing data to compute statistics, aggregations, or summaries, and presenting the results as a report. Suitable for tasks like "summarize this dataset", "calculate averages and totals", "generate a weekly sales report", or "show me trends in this data". Do NOT use for simple format conversion between file types.

Use this skill when the task involves analyzing data to compute statistics, aggregations, or summaries, and presenting the results as a report. Suitable for tasks like "summarize this dataset", "calculate averages and totals", "generate a weekly sales report", or "show me trends in this data". Do NOT use for simple format conversion between file types.

Produces structured, citation-anchored summaries of clinical notes — one-liner, hospital course, and problem-oriented views — where every claim cites a source span so nothing is hallucinated. Use after de-identifying notes when the user wants a discharge summary draft, handoff/SBAR, problem list, or chart-abstraction summary. De-identify FIRST with openmed.deidentify, then anchor summary claims to entity spans from openmed.analyze_text. Trigger keywords: summarize note, discharge summary, hospital course, problem-oriented, one-liner, SOAP, SBAR, handoff, chart abstraction.

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.

Scaffold a SMART-on-FHIR app (SMART App Launch v2 — EHR launch and standalone launch, OAuth2 PKCE, scopes, token handling, fhirContext) so an OpenMed-powered tool can run inside Epic or Cerner/Oracle Health. Covers the .well-known/smart-configuration discovery, authorize/token sequence, scopes like patient/DocumentReference.rs and launch/patient, and fetching clinical notes the app then de-identifies and runs NER on locally with OpenMed. Use when the user wants to embed OpenMed inside an EHR, mentions SMART on FHIR, OAuth2 launch, scopes, Epic/Cerner app, or clinician-facing FHIR app. Pairs adjacent.

Run expert-determination-style quasi-identifier risk scoring (k-anonymity, l-diversity) plus OpenMed's empirical re-identification attack on a de-identified dataset, then document residual risk in a defensible memo. Use when the user needs HIPAA Expert Determination (45 CFR 164.514(b)(1)) support, asks whether a dataset is safe to release, worries about singling-out via age/ZIP/dates, or wants a statistical "very small risk" determination. Covers identifying quasi-identifiers, computing k-anonymity / l-diversity, running openmed.eval.attacks.reid (run_reid_attack / run_reid_benchmark) as the adversarial attack, and writing the risk memo. Pairs after deidentifying-clinical-text and auditing-deid-leakage.

Deduplicate and reconcile OpenMed-extracted conditions into one clean active problem list with clinical status (active / resolved / historical). Use after NER and context resolution when the user wants a problem list, condition reconciliation, dedup of synonymous diagnosis mentions, or active-vs-resolved status from a note. Covers clustering synonymous mentions into one concept, excluding negated mentions, applying clinical context (historical / hypothetical / recent) to set status, and emitting a USCDI-Problem-shaped list. SNOMED CT concept grounding is user-supplied and out-of-process. Hand-off: consume openmed.analyze_text Disease entities plus resolving-clinical-context axes. Pairs after extracting-clinical-entities.

Parses free-text clinical-trial eligibility criteria into structured inclusion and exclusion logic, then matches them against patient facts that OpenMed extracted. Use when the user wants to turn a ClinicalTrials.gov eligibility block into machine-readable rules, screen a synthetic patient for trial fit, or explain why a patient does or does not meet criteria. Trigger keywords: eligibility criteria, inclusion, exclusion, trial matching, patient screening, criteria parsing, eligibilityModule, age/sex gates. Pairs after OpenMed and after searching-clinicaltrials: consume the eligibilityModule text from a study, structure it, and match against conditions, medications, labs, and demographics from openmed.analyze_text. Decision-support only — never autonomous enrollment.

Decodes pipe-delimited HL7 v2.x messages (ADT, ORU, MDM, ORM) into structured segments/fields/components and surfaces OBX-5 and NTE-3 free-text narrative for OpenMed. Use before OpenMed processing when ingesting HL7 v2 feeds from an interface engine, lab/results system, or ADT stream and you need the embedded clinical note text de-identified and analyzed. Flatten OBX/NTE text then call openmed.deidentify and openmed.analyze_text; segment-aware redaction is available via openmed.interop.hl7v2. Trigger keywords: HL7, HL7 v2, ADT, ORU, OBX, MSH, PID, pipe-delimited, interface engine, Mirth, lab results.

Searches and fetches PubMed and PMC via NCBI E-utilities (ESearch then EFetch/ESummary) to gather biomedical evidence and build text corpora. Use when the user wants citations for a condition or drug, abstracts to summarize, MeSH-based searches, or a corpus of literature to run NER over. Trigger keywords: PubMed, PMC, NCBI, E-utilities, ESearch, EFetch, ESummary, MeSH, PMID, literature search, abstracts, evidence. Pairs adjacent to OpenMed: fetched abstracts feed openmed.analyze_text for biomedical NER, and OpenMed-extracted diagnoses/drugs/genes become the search terms. E-utilities are public; an optional free API key raises rate limits from 3 to 10 requests/second.