Results for “problem-sortierung”

Organizes files into folders by type, size, or date, with preview, undo, and custom rule support.

Ranks any list of impediments and their countermeasures using a value-stream scoring model (ROI, Cost to Implement, Ease of Deployment, Risk Factor) and a fixed prioritization formula.

Turns a pile of bugs, issues, and error reports into a ranked, actionable plan with reproduction evidence. Ingests GitHub issues via gh, TODO/FIXME scans, error logs, or a pasted list; attempts to reproduce or classify every item; scores by impact, frequency, fix cost, and regression risk; clusters duplicates; and.

Clusters a GitHub issue backlog by root cause into plan-master issues, redirects children with a standardized comment, and bundles architectural-fix PRs that close clusters atomically.

Provides a structured methodology for solving nontrivial, ambiguous problems by framing outcomes, selecting evidence, routing specialists, choosing options, and verifying results.

Generate a scaffolded sequence of practice problems with graduated difficulty and strategic variability. Use when creating worksheets, homework sets, or independent practice materials.

Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

Urgent issue classification, root cause analysis, and fast-path routing for production hotfixes

A stakeholder asks to make search faster.

Rank a backlog of feature ideas by impact, effort, risk, and strategic alignment to identify the top 5 to pursue.

Transforms fuzzy problems into structured briefs that AI agents can reason about, critique, and act upon, while evaluating how much of the problem can be automated.

選択肢(A/B/C)の深掘り比較→淘汰→推奨で判断負担を下げ判断の質を上げるスキル。5段階（L1固定3点/L1.5案拡張Diverge・自動/L2評価軸マトリクス/L3複数LLM弁証論/L4過去判断照合）。 「比較して」「深掘りして」「メリデメ教えて」「お勧めは？」「徹底的に」「過去の判断と照合」「前にどう決めたっけ」「/sentaku」等で発火。teian(浅)の深掘り要求を受け取り、brainstorming(深:設計全体)と棲み分け。

동시에 굴리던 여러 워크트리를 한 번에 통합합니다. 각 워크트리와 연결된 이슈를 확인하고 증거로 실제 해결 여부를 판정한 뒤, merge 를 시도하기 전에 충돌을 확정하고 계획한 순서대로 누적 검증해 순서 때문에 깨지는 경우까지 잡아냅니다. 충돌은 작업 브랜치 쪽에서 양쪽 의도를 보존하는 방향으로 해소해 승인받고, 비판 서브에이전트로 모호성을 걸러낸 다음 PR 을 merge 하고 통합 테스트로 재검증하고 이슈를 닫습니다. `$issue-merge`, "워크트리 전부 merge", "이슈들 통합", "PR 충돌 해결하고 합쳐줘" 요청과 issue-end 에서 merge 를 선택했을 때 사용합니다.

Analyzes algorithm time and space complexity, classifies problems by complexity classes, proves NP-completeness, and designs approximation algorithms.

Builds an evidence-backed ECC install plan for a repository by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes.

Diagnoses and resolves slow Qdrant search performance issues including high latency, low throughput, and slow filtered searches.

Archive non-actionable noise from the Sentry javascript project issue queue, using untilEscalating mode with documented categories.

Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.

Diagnoses Qdrant production issues using metrics and observability tools, covering optimizer problems, memory spikes, and slow queries.

研究优先于编码的工作流程。在编写自定义代码之前，搜索现有的工具、库和模式。调用研究员代理。

4-phase systematic debugging methodology with root cause analysis and evidence-based verification. Use when debugging complex issues.

Reference guide to 9 prioritization frameworks with formulas, when-to-use guidance, and templates — RICE, ICE, Kano, MoSCoW, Opportunity Score, and more.

Investigates bugs, regressions, and crashes by dispatching four parallel read-only sub-agents, then ranks hypotheses and recommends the fastest proof or fix path.

Create, search, triage, and label GitHub issues using the gh CLI or REST API, with guidance on avoiding duplicates and managing the backlog.

Curriculum Learning

Prioritize product work with explicit criteria, scoring, tradeoffs, and decision rationale.

Rank a topic's user-flow branches by proof priority (value × risk × frequency) right after user-flow-map, ordering the branches, gating variation breadth, and promoting or pruning flows so state-model and ux-variations grow the tree in proof order — writes only existing flow-tree ordering fields, no schema change.

Use when content or notes need organizing into a structured, hierarchical outline

Searches Polish marketplaces (Allegro, Ceneo, Google Shopping) to compare product prices and delivery options, presents a comparison table, and assists with checkout while leaving final payment to the user.

Analyze card sorting results to inform information architecture and navigation structure. Use after conducting open or closed card sort studies.

Triage a bug or issue by exploring the codebase to find root cause, then create a GitHub issue with a TDD-based fix plan. Use when user reports a bug, wants to file an issue, mentions "triage", or wants to investigate and plan a fix for a problem.

Relational modelling lets you defer query design: normalize the entities, and

Resolves GitHub issues using isolated worktrees and test-driven development, then delegates PR creation to /github:create-pr so the quality gate and the /github:review-pr loop always run. This skill should be used when the user asks to "resolve an issue", "fix issue

Sorts an email inbox into reply-now, reply-later, and ignore buckets, and drafts replies for urgent messages using an existing Gmail connector.