'loop' 검색 결과

Detects N+1 query patterns (relation access inside loops without eager loading), full-table get()/all() loads, and unsafe chunk()/cursor() usage in Laravel Eloquent code.

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指摘対応の反復（レビューコメント→修正→再レビュー）で、個別には正しい修正の蓄積が当初スコープを逸脱した（scope creep）、または成立済みの前提（"動いていた"状態）を破壊した（premise break）連鎖を diff-time で検出する。技術的正しさとスコープ整合性を別軸で評価し、report-only で finding/question のみ出力する。1コメント単位のトリアージ・構造変更後の caller 残骸・plan 前提解消は隣接 skill へ委譲する

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Self-verification loop. After completing a task, re-read the original request, list each requirement, verify whether it was met, and fix gaps before stopping.

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Autonomously improve an MCP skill via the skillit audit→draft→review loop (build or runtime mode) Also: skillit, refine, mcp, skill-generation, cli, audit, overlay.

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Bootstrap an AI-agent skill from a TypeScript codebase by running the deterministic skillit generate/audit loop and enriching repo source (JSDoc, README, config-type properties, MCP tool annotations, examples, package.json) until the skill reaches its grade target. Use for cli, typedoc, config, or mcp (build-mode) projects; never edit SKILL.md/references directly.

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スライド作成の“プレゼント”一式。このリポジトリ（リンク/フォルダ）を渡されたら内部を読み、相手の状況に合わせて「壁打ち」または「フォルダテンプレート作成」または「スライド作成」を行う。構成→1スライドずつ生成→批評→改善→自動検証のループで、はみ出しゼロ・1スライド1メッセージのスライド（単一HTML）を作る。「スライド作って」「資料にして」「プレゼン作って」「この台本をスライドに」「このリポジトリでスライド作りたい」で使う。Codex 推し（AGENTS.md / .codex/agents）。Claude Code でも動く。

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Anime.js v4 core animations: production-level target selection, property keyframes, duration, ease, loop, play/pause. Use when implementing, reviewing or testing anime.js v4 core animations for React, Vite, Next.js or Perfect_AI projects.

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Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

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Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool ...

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Shape FriendlyBet product decisions, MVP scope, user stories, tournament flows, multi-sport expansion, pool mechanics, social competition loops, and feature prioritization. Use when planning what FriendlyBet should build, cut, sequence, or validate, especially for new sports, prediction formats, pool flows, leaderboards, sharing, or product strategy.

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Shape FriendlyBet Pundit content, bilingual product copy, community voice, share-card copy, social moments, video briefs, Hebrew/English wording, and group-chat friendly communication. Use when a task touches copy, Pundit tone, social videos, share text, community loops, or public-facing FriendlyBet voice.

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Govern FriendlyBet agent values, professional standards, anti-hallucination behavior, truthfulness, risk reporting, AI resource discipline, feedback loops, skill improvement, performance reviews, decision-rights hygiene, and team quality. Use when creating, reviewing, promoting, correcting, or retiring agents; reducing hallucinations or mistakes; reviewing AI/tool usage; handling agent incidents; or aligning work with Eyal's philanthropic user-first approach.

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Execute per-layer adversarial audit/remediation loop, persist fixes metadata, and manage fresh-run advancement

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Layer-by-layer remediation loop using adversarial audits, targeted fixes, and fresh-run verification enforcement

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Set up and optimize repositories for AI coding agents. Creates minimal AGENTS.md, CLAUDE.md symlink, docs/REQUIREMENTS.md, docs/BUSINESS-RULES.md, feedback loops, and deterministic enforcement (Claude Code hooks, OpenCode plugins). Use when user wants to make a repo AI-friendly, set up AGENTS.md/CLAUDE.md, document requirements/business rules for AI, add pre-commit hooks for AI workflows, or optimize codebase structure for coding agents.

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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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RED-GREEN-REFACTOR for process documentation - baseline without skill, write addressing failures, iterate closing loopholes

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Scan a codebase for deepening opportunities, present them as a visual HTML report, then guide a user-directed decision loop for the selected candidate.

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two-stage review (spec compliance first, then code quality), refactoring, and quality improvement. Use when reviewing code, eliminating code smells, reducing technical debt, refactoring methods, running self-critique loops, or improving maintainability and readability.

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Evaluate and improve AI-generated output with explicit rubrics, reflection loops, and stop conditions. Use when building self-critique workflows, evaluator-optimizer pipelines, or acceptance gates for code, docs, analysis, or plans.

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Expert en developer advocacy (communauté, contenu, docs, demos, feedback loop, métriques)

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Run the startcycle-graph pipeline: durable state, repair loop, quality gate, escalation. Generated from plugin-commands.json; invoke as $bdb-aos:graph.

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Use before starting any significant build or before shipping: problem-framing pre-flight, one-way/two-way door classification, ADR-lite decision logging, and post-ship outcome loop. Complements godmode-shipping (technical gate) and bdbresilience (error recovery).

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Linear multi-agent build pipeline with file hand-offs in production_artifacts/, run after /bdbrainstorm or /grill-me. This is the linear variant — no state.json, no repair loop, no dispatcher graph — distinct from startcycle-graph, which adds durable state and automated escalation. Use when an approved plan should be built, when the user types /startcycle or "run a startcycle", or when you pick a startcycle variant from the situation yourself (no user prompt needed).

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