「iter」の検索結果

Expert Architecture Decision Record (ADR) creation and lifecycle management based on Olaf Zimmermann's methodology. Use when creating ADRs, reviewing architectural decisions, evaluating decision readiness, writing MADR templates, assessing decision quality, or managing ADR logs. Covers the full lifecycle from readiness (START criteria) through creation, MADR formatting, completion (ECADR criteria), and ongoing maintenance.

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議論・議事録・選定された施策を起点に、対象リポジトリの実構造にgroundした「エンジニア向け Story（User Story + Acceptance Criteria）と Spec（技術仕様）」をSubagentで収束生成する。target_repoで対象コードベースを、backlog_db_urlで起票先バックログを選べる汎用スキル。抽象的な打ち手を実装可能な仕様へ絞り込む収束フェーズの実行に使う。

Builds revolving securitization models with replenishment criteria, early amortization triggers, and portfolio composition limits. Use when modeling revolving structures, analyzing credit card ABS, or evaluating asset purchase criteria.

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QA validation and fix loop workflow — validates implementation completeness then iterates fix cycles until all acceptance criteria pass and quality gates clear

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Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".

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EZMM writer-stage adapter for the vendored PaperOrchestra skill pack. Use by default during pipeline/04-paper-writing for outline-first paper drafting, literature-review planning, section writing, and refinement discipline. The adapter maps EZMM runtime artifacts to PaperOrchestra-style inputs, runs PaperOrchestra-first writing automatically, and returns EZMM-compatible paper.md.

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Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters".

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Search academic papers, build literature reviews, and synthesize research findings — combines Exa MCP (research_paper category, arxiv filtering) with arxiv-mcp-server for paper discovery, download, and deep analysis. Triggers on academic paper, literature review, research synthesis, arxiv, find papers, scholarly search.

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Run Claude Code in autonomous iteration loops until task completion — PRD-based feature development, test coverage improvement, lint fixing, documentation generation, dataset creation, or framework migrations. Uses in-session loop mechanism with specialized templates. Triggers on autonomous loop, iterate until done, run until passing, keep going until, ralph, PRD execution.

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Source pre-2016 cinema with gothic/occult sensibility, literary texture, and historical grandeur — build watchlists, curate Criterion/MUBI picks. Uses Perplexity for film discourse, Exa for web search, Firecrawl for scraping. Triggers on film recommendations, watchlist, gothic cinema, occult film, arthouse, pre-2016 movies.

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Generate hand-crafted web animation patterns as self-contained HTML: typewriter/rotator effects, progress bar simulations, file review state machines, staggered reveals, terminal status displays, streaming AI-response demos, popover/dialog/toast lifecycles, Lottie orchestration, scroll-driven sequences. Behavioral animations that simulate live software. Triggers on typewriter effect, progress animation, stagger reveal, terminal animation, streaming text, AI demo animation, toast animation, data simulation, product demo animation, loading sequence, conductor motion.

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Specialized research and analysis prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for data analysis, policy research, systematic evidence review, investigative research, and research-specific deliverables. Use when building prompts for quantitative data interpretation, evidence-based policy recommendations, literature reviews, causal analysis, hypothesis-driven investigation, or briefing documents. Trigger on: "prompt for data analysis," "research prompt," "policy brief prompt," "literature review prompt," "prompt for investigating," "systematic review prompt," "build a prompt for evidence synthesis," "causal analysis prompt," "briefing document prompt." Also use when user describes a research or analytical task and needs a structured Claude prompt for it. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or auditing Claude Projects (use rootnode-project-audit if available).

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Full AI-assisted workflow for long-form SEO content (2,000+ words). Covers JTBD strategy, E-E-A-T quality standards, and 5 production phases: research, outline, draft, editing, and SEO/publishing. Built for B2B marketing agency context. Use when: user wants to write or optimize an SEO article, mentions "SEO content", "blog post", "GTM article", "long-form content", or asks to create content with keyword strategy.

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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Web runtime package: uses memory in this order: connector-backed, project-pack, none. Never assumes subprocess access or unrestricted local files. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles.

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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.

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App Store プレビュー動画を Remotion (React) で生成する Agent Team スキル。 video-director が構成を設計し、script-writer がナレーション台本を作成し、 motion-designer が Remotion コードを実装し、preview-reviewer がコードレビューし、 frame-inspector がレンダリング結果を目視検証する。 Use when: App Store プレビュー動画を作りたい、アプリのプロモーション動画を作りたい、 Remotion で動画を生成したい。 Triggers: "プレビュー動画", "App Store 動画", "アプリ動画", "preview movie", "Remotion", "動画作成", "app preview"

エージェントを長時間稼働させて大きな実装タスクを完遂するためのスキル。 TODO ファイル (.md) を管理しながら、高レベル TODO → 細かい TODO に分解 → 実装 → サブエージェントによるレビュー → 不足分の TODO 追加 のサイクルを回す。 コンテキストが大きくなっても TODO ファイルが状態の真実の源泉として機能するため、 長時間・大規模な変更でも迷子にならずに着実に完遂できる。 team-plan や team-implement とは異なり、1人のメインエージェントが逐次的に実装し、 各段階をレビューエージェントが品質検証する。 Use when: 大きな実装タスクを段階的に進めたい、エージェントに長時間任せたい、 実装中に TODO を追加しながら進めたい、各ステップの品質をサブエージェントにレビューさせたい、 長時間の実装を構造化したい、放置しても安心して任せられる実装フローが欲しい。 Triggers: "long-run", "段階的に実装", "TODO駆動で実装", "大きな実装", "ステップバイステップで実装", "レビューしながら実装", "long run implement", "実装を進めて", "大規模実装", "iterative implement", "長時間実装", "エージェントに任せて", "放置で実装"

App Store / Google Play 用のプロモーションスクリーンショットを Pencil (.pen) で生成する Agent Team スキル。creative-director が戦略を立て、screenshot-designer が Pencil で デザインを構築し、copy-writer がコピーを提供し、spec-validator が技術仕様を数値検証し、 quality-reviewer が最終品質を確認する。5エージェント体制で高品質なスクリーンショットを生成。 Use when: App Store スクリーンショットを作りたい、プロモーション画像を作成したい、 スクショのデザインをしたい。 Triggers: "スクリーンショット作成", "App Store 画像", "プロモーションスクショ", "screenshot creator", "スクショ作って", "App Store 素材"

Fit hidden Markov models using the Baum-Welch (EM) algorithm with model selection, Viterbi decoding for state sequences, and forward-backward probabilities. Use when observations are generated by unobservable latent states, you need to segment a time series into latent regimes (market regimes, speech phonemes, biological sequences), compute sequence probabilities, decode the most likely hidden state path, or compare models with different numbers of hidden states.

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应用从 AI for Everyone 蒸馏出的「迭代而非完美 — AI产品的渐进式改进」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to iterate-not-perfect.

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Generate format-controlled research reports with evidence tracking, citations, and iterative review. This skill should be used when users request a research report, literature review, market or industry analysis, competitive landscape, policy or technical brief, or require a strict report template and section formatting that a single deepresearch pass cannot reliably enforce.

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Before any multi-step work with a stated goal — a feature, a bugfix spanning multiple files, a QA sweep, a doc draft + iterations, a multi-day investigation — create an ATO Mission instead of doing it via bare `ato dispatch` calls. A Mission persists the goal + the verifiable success criteria, lets the coordinator tick drive the work across days, captures every event in a structured audit trail (SQLite + markdown narrative), and integrates parallel agents' work via merge strategies. Complement to `ato-warroom` (the cross-family decision before you start) and `ato-review` (the post-code-diff review). Missions is where multi-step work LIVES; war-rooms are where decisions ABOUT it get made; reviews are where the resulting commits get vetted. Fires when: the work has more than one decision point, a verifiable end state, or runs across more than one session. Use it for any ATO development that doesn't fit in a single dispatch.

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Stock Screener — pre-built screens (growth, value, momentum, dividend, earnings beats) plus custom natural language criteria. Returns top 10-20 matches ranked by Screen Fit Score (0-100). Triggered by "trade screen growth/value/momentum/dividend/earnings" or "trade screen custom <criteria>".

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Salary Benchmarking — market range by role/location/experience, total comp breakdown (base, bonus, equity, benefits), recruiter negotiation talking points

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