1,957 件 ・ 関連度順
概要と使いどころ
명령형 for/while 루프를 Stream API/Collection Pipeline으로 변환하여 데이터 흐름 의도 명확화. "스트림으로 바꿔", "루프를 파이프라인으로", "for문 stream", "/replace-loop-with-pipeline" 요청 시 사용. 단, 루프 안 로직이 컬렉션 클래스로 모여야 하면 /first-class-collection이 적합. /replace-loop-with-pipeline [commit-ref]로 호출.
日本語の概要は準備中です。原文の説明を表示しています。
Detects deeply nested loops with duplicated inline logic and recommends extracting into small, named functions. Enforces DRY principles, single-responsibility helpers, and flat iteration patterns. Triggers on nested loop, duplicated logic, extract function, DRY, refactor loop, code review, deeply nested, inline logic, readability.
日本語の概要は準備中です。原文の説明を表示しています。
Interaktives Reißverschluss-Audit-Verfahren für eine Serie von Apps/Prüfobjekten: der User testet live und gibt kurzes Feedback im Chat, der Agent startet dabei bereits das nächste Objekt UND wertet parallel das Feedback des vorigen aus (strukturiert erfassen, Reparaturaufträge sofort an Worker delegieren) — statt sequenziell zu warten. Nutze diesen Skill bei "human-loop-audit", "lass uns die Apps im Reißverschluss durchtesten", "ich teste, du wertest aus und reparierst", oder wenn eine Reihe von GUIs/Produkten gemeinsam mit dem Nutzer durchgetestet werden soll und Befunde sofort in Reparaturen münden sollen. Abschluss ist eine strukturierte Befundliste je Objekt (Funktioniert/Defekt/Wunsch) plus die Liste der bereits gestarteten Reparaturen.
日本語の概要は準備中です。原文の説明を表示しています。
Start, inspect, or continue a persistent SIPS self-improvement loop. Use when the user asks for /selfloop, wants the agent to iteratively improve itself, or wants a goal dedicated only to SIPS and agent capability.
日本語の概要は準備中です。原文の説明を表示しています。
River Review のメインエントリポイント。 レビュー依頼の intent classification → 専門 skill 選択 → 実行 → finding verification → feedback classification → fixture / reference / suppression への還元までを束ねる improvement-loop orchestrator。
Expert en agent loops (ARIS, Ralph, recherche autonome, build-score-budget, guardrails)
日本語の概要は準備中です。原文の説明を表示しています。
Run a prompt repeatedly on an interval, using the harness built-in plus loop templates. Generated from plugin-commands.json; invoke as $bdb-aos:loop.
日本語の概要は準備中です。原文の説明を表示しています。
Use when turning something that happened into something that makes the next run better - "write a retro", "record this paper cut", "this bug escaped verification", "make this an eval", "why do we keep hitting this", "did that change actually help", "scorecard", "what did we learn from that run", "lessons keep getting re-learned". Covers the six-step loop (capture, classify, convert, change, measure, retro), the repo artifact each step produces, and how escaped bugs become eval cases. Do NOT use to design or judge a single workflow experiment (see `workflow-experimentation`, which this loop feeds), to decide where to run work (see `dogfooding`), or to debug one failed execution in the moment.
日本語の概要は準備中です。原文の説明を表示しています。
Master orchestrator for the 5-step productivity loop. Runs audit, architect, analyst, refinery, and compounder in sequence or individually. Use when you want to run the full loop, start the system, check system status, resume from where you left off, or run a specific step. Manages state, sequencing, and skill chaining. Supports full loop, single step, resume, and status-only modes.
日本語の概要は準備中です。原文の説明を表示しています。
Master orchestrator for the 5-step productivity system. Runs the full audit-architect-analyst-refinery-compounder loop or individual steps. Use when: (1) "run the full loop", "start the system", "architect system", (2) "run step N", "run audit", "run architect", (3) "what step am I on?", "system status", "where am I in the loop?", (4) "continue", "resume", "next step". Manages state, sequencing, and skill chaining across all 5 steps.
日本語の概要は準備中です。原文の説明を表示しています。
Run the full implement-review-fix loop using implementer and reviewer personas. Supports effort-based multi-reviewer scaling (1-5 reviewers) with automatic specialization selection. Includes memory-based feedback loop that learns from past review patterns. Loops until all reviewers find 0 issues of any severity.
日本語の概要は準備中です。原文の説明を表示しています。
Runs Charted Coding iterations in a loop — route via charted-continue, batch red or green work for an entire test file, commit, then repeat until the PR is complete. Infers design doc and PR number when omitted. Use when the user invokes charted loop, wants batch TDD for a PR test file, or asks to continue charted work with commits.
日本語の概要は準備中です。原文の説明を表示しています。
Design AI feedback loops for continuous improvement. TRIGGERS - Use when user needs help with ai-feedback-loop related tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Interact with UI Toolkit (UXML/USS) runtime UI in PlayMode. Use INSTEAD OF uloop-simulate-mouse-ui / uloop-simulate-mouse-input whenever the target UI is UI Toolkit — those tools only reach uGUI and report 'no UI element hit' or silently do nothing on UI Toolkit panels.
日本語の概要は準備中です。原文の説明を表示しています。
/review-fix をループ実行し、CONVERGED になるまで自動修正を繰り返す。スコープ引数は /review-all と同じ（wt / PR #N / pr:N / vs:<branch> / git range / ファイル列挙、デフォルトは比較ブランチとの差分）。最大イテレーション数を数値で指定可能。Use when user says 'レビュー収束まで繰り返して', 'review-fix-loop', '自動修正ループ', or '収束するまでレビュー'.
Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user hands over a GitHub issue to be delivered end to end — 'resolver a issue #42', 'loop na issue 42', 'deliver issue https://github.com/o/r/issues/42' — or when an Orca worker preamble dispatches `feat-loop #N`. Turns the issue into a plan, executes it, reviews the result and runs at most 2 fix cycles until APPROVED, then opens a pull request that closes the issue. Never merges. Do NOT use without an issue (feat-feature / feat-quick), to only plan (feat-feature), to only review (feat-review) or to execute an existing plan (feat-exec).
日本語の概要は準備中です。原文の説明を表示しています。
Sanguo event review loop with LangGraph Studio visualization. Use for: 三國事件審核、generic event candidates、battle candidates、event review MCQ、accepted/rejected events、promote events、LangGraph Studio、human-in-the-loop、事件候選可視化、chunk -> extract -> validate -> review -> publish。
日本語の概要は準備中です。原文の説明を表示しています。
Sanguo RAG unresolved mention resolution loop. Use for: 三國 RAG, unresolved mentions, alias review, observed mentions, MCQ triage, manual roster seeds, false-positive exclusion, running run_resolution_loop.py, applying unresolved-triage answers, delegating 三國名詞查證 to a web-capable agent, or Pilot ETL chunk experiments with LangChain text splitters / LangGraph / LangFlow around 張飛長坂橋等小樣本流程。
日本語の概要は準備中です。原文の説明を表示しています。
Design a self-improvement loop for a shipped product's AI agents — production traces feed evals, evals feed improvement proposals (prompts, playbooks, retrieval configs), and a human approval gate promotes changes with rollback. Technique-agnostic: chooses per project between ACE-style evolving playbooks, GEPA/MIPROv2 offline optimization, or simple eval-driven iteration via references/techniques.md. Load when the user asks to make my product's agents self-improving, learn from production traces, add a learning loop, evolve prompts or playbooks safely, promote agent improvements, or GEPA-style optimization. NOT harness-evolution (that improves the coding agent), NOT experimentation (product A/B tests), NOT agent-run-retro (dev-phase manual retros — this skill is the production-scale continuation).
日本語の概要は準備中です。原文の説明を表示しています。
Apply Boyd's OODA loop to navigate fast-moving, uncertain, or competitive situations — Observe what is actually happening, Orient through mental models and context, Decide on the clearest path, Act quickly, then loop again. Load when the situation is changing faster than the current plan, when a competitive response is needed, when the user is stuck in analysis paralysis in a dynamic environment, when shipping under uncertainty, or when deep-thinking diagnoses a fast-moving / competitive frame. Triggers on "what should we do right now", "the situation is changing", "how do we respond to this", "competitive response", "we need to move fast", or "we're stuck deciding". Based on John Boyd's OODA loop — Observe, Orient, Decide, Act — adapted for product and business contexts (OODA Canvas 2026).
日本語の概要は準備中です。原文の説明を表示しています。
Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.
日本語の概要は準備中です。原文の説明を表示しています。
front-end-design-patterns を使い、指定されたフロントエンド範囲に対して サブエージェント調査（必要時は複数スコープに分割した独立 fresh audit） → メインセッション修正 → 必要時のみ性能計測 → simple-add → 再調査を NO_FINDINGS まで繰り返す、機能変更なしの内部リファクタリングループ。 Trigger: front-end-refactor-loop, front-end-design-patterns リファクタリングループ, フロントエンドリファクタリングループ, Reactリファクタリングループ, サブエージェントでフロントエンド調査, NO_FINDINGSまでリファクタ, 性能計測つきフロントエンドリファクタ
This skill should be used when the user wants to "test a claim against data", "validate an empirical hypothesis", "refine a model/theory with evidence", "characterize a pattern and verify it", "演繹と帰納で検証", "仮説をデータで検証して更新", "実例と反例で確かめる", "弁証法ループ", "主張を現物で裏取り", "傾向分析を検証して精緻化", or mentions iteratively updating a hypothesis by deriving predictions and testing them against a real corpus. NOTE: Use this for VALIDATING/REFINING an empirical claim or model against real data through a derive→test→update loop. Use strong-inference for debugging an UNKNOWN cause, and devils-advocate for adversarially stress-testing a DESIGN proposal.