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A Python DSL for composing, querying and analyzing calendar intervals: find free time, detect conflicts, filter events, and compute timeline metrics.
Calgebra provides a set-algebra DSL for calendar timelines and intervals. It lets agents compose lazy timelines, slice them with timezone-aware bounds, filter and transform events, compute metrics (duration, coverage, counts), and convert results to dataframes or iCalendar files. Typical capabilities include finding free slots, detecting conflicts, merging overlapping events, and producing cyclic histograms (by hour, day, month).
Use this skill when you need to reason about time ranges or calendars: calculating busy/free windows, filtering events by duration or tags, aggregating meeting time, comparing schedules across timezones, or exporting filtered events to .ics or dataframes for display. Ideal for user requests like “find my next free 1-hour slot” or “show weekly meeting coverage by day.”
at_tz()), composing timelines with operators (|, &, -, ~), converting to DataFrame (to_dataframe), common patterns for recurring rules and transformations, and metric functions (total_duration, coverage_ratio).Likely to be used by Python-capable agents or agents that can run Python snippets (Copilot/Code-style agents).
Calgebra is a Python DSL for calendar set algebra — compose timelines with union/intersection/difference operators, filter by duration or custom fields, compute metrics, and handle recurring patterns. The SKILL.md is well-structured with clear quick-start, core concepts, operators, and common patterns sections. No bundled scripts to test; the skill references an external Python package (calgebra) that must be installed separately. No security concerns — purely a data-querying/analysis skill with no network calls, shell execution, or credential handling in the skill body itself.
calgebraClean, well-documented skill with no security issues. The SKILL.md is comprehensive with good progressive disclosure — quick start → core concepts → operators → filtering → advanced patterns. Architecture follows skill spec well with proper frontmatter, clear sections, and a logical flow. Deductions: no scripts bundled (so can't verify behavior), depends on external package, and niche audience (scheduling-focused developers). Minor code quality deduction for one vague reference ('access_token' undefined in examples).
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