scientific-skills/Others/schedule-management/SKILL.md
Local schedule management for adding events, tracking deadlines, generating reminders, and detecting time conflicts when users need offline scheduling with optional popup notifications.
npx skillsauth add aipoch/medical-research-skills schedule-managementInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
type=deadline) and list them by time range.events.jsonl.reminders.csv.notified.log.>= 3.9>= 5.1 to run scripts/notify.ps1Time format must be
YYYY-MM-DD HH:MM(24-hour).
python scripts/schedule_tool.py \
--operation add \
--data-dir "./data" \
--title "Project Sync" \
--start "2026-02-10 09:00" \
--end "2026-02-10 10:00" \
--type "meeting" \
--location "Room 3A" \
--notes "Bring status updates" \
--tags "work,weekly" \
--remind 30
python scripts/schedule_tool.py \
--operation list \
--data-dir "./data"
python scripts/schedule_tool.py \
--operation conflicts \
--data-dir "./data"
python scripts/schedule_tool.py \
--operation reminders \
--data-dir "./data"
This generates:
./data/events.jsonl./data/reminders.csvpython scripts/schedule_tool.py --operation reminders --data-dir "./data"
powershell -ExecutionPolicy Bypass -File scripts/notify.ps1 -DataDir "./data"
Notifications are deduplicated using ./data/notified.log so each reminder time is shown only once.
Additional examples may be available in
references/examples.md.
Storage
events.jsonl in the specified --data-dir.reminders.csv in the same directory.notified.log in the data directory.Operations
add: validates required fields and writes a new event record.import: imports event records (format depends on the script’s supported import mode).list: prints a summary of stored events (optionally filtered by time range).conflicts: checks for overlapping events and reports conflicts.reminders: computes upcoming reminder times and exports them.Time Parsing Rules
YYYY-MM-DD HH:MM (24-hour).Conflict Detection
startA < endB and startB < endADeadlines
type=deadline.Failure Handling
Security & Compliance
--data-dir.tools
Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers five study patterns (signature-first prognostic workflow, hub-gene-first biomarker workflow, hybrid signature-to-hub workflow, immune-context biomarker workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
development
Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG discovery, enrichment/GSEA interpretation, hub-gene prioritization, regulatory-network and immune interpretation, multi-layer public validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
tools
Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal structure awareness, variable-role classification, and critical design review rather than generic statistical advice.
testing
Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.