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SkillsMP has collected 65 skills from jimmc414/claude-code-plugin-marketplace. Open a skill to review its source and details.
Showing 40 of 65 collected skills.
Conducts iterative deep research on any topic using web search, progressive exploration, and structured synthesis. Use when asked for comprehensive research, deep investigation, thorough analysis, or multi-source exploration of any topic. Triggers: research,…
For cross-cutting concerns: add behavior without modifying functions, caching, timing, logging, validation wrappers.
For performance work: measure before changing, profile to find bottlenecks, compare before and after.
For symbolic computation: ASTs, mathematical expressions, code that manipulates code structure, expression transformations.
For ordered processing: A* search, Dijkstra, event simulation, task scheduling. Efficient min/max extraction with heap-based queue.
For dynamic programming: overlapping subproblems, recursive solutions with repeated computations, memoization to avoid redundant work.
For persistent state: closures capture outer variables, alternative to classes for simple state, factory functions that remember context.
For flexible parsing: try multiple type conversions in order, graceful fallback from specific to general types.
For iteration with errors: catch exceptions during exploration, skip invalid cases, continue to next attempt.
For hot loop optimization: repeated formula evaluation, regex patterns, expression compilation. Transform string to callable once, call many times.
For complex behavior: build from tiny functions, chain transformations, make code read like a pipeline of operations.
For probability and counting: permutations, combinations, sample spaces, Monte Carlo simulation, brute-force enumeration, card/dice problems.
For new modules: define type aliases as vocabulary, make code self-documenting, create domain-specific language feel.
For heterogeneous data: pattern matching on type/structure, interpreter eval loops, handling different expression types.
For 2D debugging: visualize grid/board state, show puzzle progress, make algorithm behavior visible.
For cross-version support: try/except imports, optional dependencies, graceful degradation across Python versions.
For computational geometry: convex hull, point enclosure, polygon operations. Uses monotone chain algorithm with stack-based turn detection.
For pathfinding and search: shortest path, maze solving, game AI, route planning, graph traversal, BFS/DFS, Dijkstra, A* problems.
For result reporting: tabular output, aligned columns, statistics summaries, human-readable reports.
For graph exploration: frontier collection with configurable pop order, BFS/DFS/random via strategy change.
For tree/maze generation: spanning trees, random mazes, graph coverage. Uses frontier-based exploration with configurable traversal order.
For boundary conditions: empty collections, zero values, recursive base cases, null checks, prevent crashes at edges.
For fast comparison: ensure only one instance of each symbol, use 'is' instead of '==', symbol tables for interpreters.
For combinatorial iteration: permutations, combinations, cartesian products, without storing all results in memory.
For two-sided matching: hospital-resident, stable marriage, college admissions. Gale-Shapley algorithm for stable matching with preferences.
For NP-hard optimization: TSP, scheduling, assignment problems. Uses greedy construction + local improvement (2-opt, hill climbing).
For domain-specific languages: operator overloading, make Python look like math/domain notation, expression builders.
For text processing: extract numbers, words, structured data from messy text using regex patterns, parsing utilities.
For generic algorithms: strategy pattern, callbacks, configurable behavior. Pass functions as parameters to customize algorithm behavior.
For static relationships: graph structure, grid neighbors, constraint peers. Calculate once at module load, reference throughout program.
For constraint problems: eliminate impossibilities before guessing, reduce search space through inference, fail fast on contradictions.
For long functions: break into smaller pieces, extract helper functions, reduce nesting, improve testability and readability.
For graceful failure: return None or False instead of exceptions, let caller decide how to handle failure.
For comparison: display before/after, multiple solutions, diffs side by side for visual comparison.
For constraint satisfaction: Sudoku, scheduling, N-queens, logic puzzles, SAT-like problems, assignment problems. Uses propagate-then-search pattern.
For 2D grid problems: mazes, board games, tile maps, pixel grids, coordinate systems, cellular automata, flood fill. Uses dict-based Grid class pattern.
For search with undo: explicit decision stack, backtracking when paths fail, depth-first exploration with state restoration.
For data structure validation: test lengths, relationships, constraints that must hold, verify setup is correct.
For example-driven development: test cases as specifications, input/output pairs, documentation through examples.
For performance reporting: timing wrappers, throughput calculations, profiling summaries.