「iter」の検索結果

Inspect ADR health read-only; optionally select one ADR with --adr N.

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。

Restructure code without changing observable behavior: rename, extract, inline, move, deduplicate, or replace an internal implementation with an equivalent one. Prove parity through unchanged pre-existing tests. Not for new behavior, bug fixes, explanation-only questions, or committing finished work.

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 an opt-in deep codebase audit with persisted findings. Confirm cost before dispatch; filing and telemetry need separate approval.

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。

Audit stale provenance-tracked docs on request. Report first; re-scout or re-baseline only for selected docs.

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。

Build project context from an existing codebase through isolated scouts, resolve gaps, and preserve initialization gates.

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。

Sanctioned lane for non-behavioral work — docs-only edits, dependency bumps, reverts. Type-scaled gates; no TDD demanded of prose.

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。

Prepare a declared release target, or preview it with --dry-run. Derive its version and require authorization before publication.

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 a feature: brainstorm a spec, get it approved, then drive it test-first through the pipeline. The one entry to implementation.

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。

Create a verified local Git commit when committing changes is requested. Not for explaining commit history, drafting a message only, or postponing a commit. Applies every commit gate and never implies a push or PR.

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。

Agile product ownership for backlog management and sprint execution. Covers user story writing, acceptance criteria, sprint planning, and velocity tracking. Use when writing user stories, creating acceptance criteria, planning sprints, estimating story points, breaking down epics, or prioritizing the backlog.

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 wants to capture a writer's voice from a few sample articles and rewrite text to sound like that author — "蒸馏文风", "提炼XX的风格", "把这篇改成XX的味儿", "学某某笔法改写", "mimic this writer", "match this author's style", "style transfer". Works for any author, 王建硕 included.

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。

Find the papers that answer a research query in Firecrawl's research paper index — a corpus of paper abstracts whose largest share is biomedical and life-science literature (PubMed, bioRxiv, medRxiv), alongside arXiv preprints in CS, physics, and math — using semantic search, semantic and structural expansion, and in-body verification. Use this skill for literature-finding and paper-retrieval tasks of any kind, including clinical, biomedical, drug, gene, disease, and other life-science questions, whether the answer is a single paper or a full multi-paper set. The index is reached only through the `firecrawl_research_*` MCP tools or the `firecrawl research` CLI subcommands. Calling `firecrawl_search` with its `categories` option set to `["research"]` is a different feature — it filters ordinary web search to research-affiliated websites (the list includes PubMed, bioRxiv, medRxiv, arXiv, and publisher sites) and returns page results from them, without querying the paper records in this index.

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。

Produce an intensive, cited analytical report: executive summary, multi-angle findings, contrarian views, open questions, and full sources. Use only when the user needs rigorous synthesis of a complex topic (scientific, technical, policy, or market-analytical) that cannot be answered with a short search, and wants a formal written report, not a recommendation list. Do not use for product picks, top-N lists, quick lookups, or routine "find out about X" tasks. If the request does not clearly need this kind of report, do not use this skill. Do not use for a literature review over published papers. This skill collects evidence from the open web. A request for the literature on a biomedical, clinical, life-science, or other scientific topic — papers, studies, trials, preprints — belongs to firecrawl-research-papers, which queries Firecrawl's paper index (PubMed, bioRxiv, medRxiv, arXiv) instead of searching websites.

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。

Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification over Firecrawl's paper index — largely biomedical and life-science literature from PubMed, bioRxiv, and medRxiv, plus arXiv preprints in CS, physics, and math. Use when the user wants a literature review, systematic review, survey of studies, paper summary, research landscape, or sourced synthesis from scholarly and industry publications, including clinical, drug, gene, disease, epidemiology, and public-health topics. Prefer this over a general web-research workflow whenever the evidence base is published papers rather than web pages.

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。

Iterative review-fix loop for the current branch — reviews via daemon, fixes inline, re-reviews until passing or max iterations reached

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 a task is in pm_uat - verify every acceptance criterion and the human's comments yourself in the browser, on a device, or over a loopback HTTP request, record a verdict per criterion, then move to human_uat or need_revision

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 writing acceptance criteria for a task - express each as an observable Given/When/Then that QA can execute, including negative cases

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 any case fails or you reject a criterion - what goes in the review_criterion note, the failed case and the numbered need_revision comment, with 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。

Design well-structured agent loops with best-practice coaching and cross-model review gates before you run them. Use when the user wants to design, build, or set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge loop, multi-model council, reviewer/judge gate, or goal-driven looping process. Guides goal refinement, typed verification criteria, reviewer/judge selection, privacy boundaries, termination guards, and observability, then emits a RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json, LOOP.md, and run-loop.py.

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。

Unified skill for content strategy and marketing content strategy: content pillars, editorial calendars, keyword research by buyer stage, positioning, messaging hierarchy, trust-building, GEO/AI search optimization, and ROI measurement. Also handles content briefs, SEO briefs, content outlines for writers, on-page SEO optimization, meta descriptions, title tags, keyword density, content research, source discovery, expert sourcing, and information gathering. Use when planning content strategy, creating content briefs for writers, optimizing existing content for SEO, researching topics and sources, or managing editorial operations. Triggers: content strategy, marketing content strategy, content marketing strategy, content planning, editorial calendar, content pillars, messaging hierarchy, content brief, SEO brief, content outline, keyword research, buyer journey content, GEO optimization, AI search optimization, content ROI, content operations, content roadmap, brand messaging, positioning

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。

Find the papers that answer a research query in Firecrawl's research paper index — a corpus of paper abstracts whose largest share is biomedical and life-science literature (PubMed, bioRxiv, medRxiv), alongside arXiv preprints in CS, physics, and math — using semantic search, semantic and structural expansion, and in-body verification. Use this skill for literature-finding and paper-retrieval tasks of any kind, including clinical, biomedical, drug, gene, disease, and other life-science questions, whether the answer is a single paper or a full multi-paper set. The index is reached only through the `firecrawl_research_*` MCP tools or the `firecrawl research` CLI subcommands. Calling `firecrawl_search` with its `categories` option set to `["research"]` is a different feature — it filters ordinary web search to research-affiliated websites (the list includes PubMed, bioRxiv, medRxiv, arXiv, and publisher sites) and returns page results from them, without querying the paper records in this index.

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。

This skill should be used when the user asks to "write a hook for my post", "give me opening lines", "help me start this LinkedIn post", "I need a better opener", or when the linkedin-post-writer skill needs strong opening options. Also triggered by "my posts aren't getting clicks" or "how do I get people to read my posts".

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。

Advanced Amazon product search and filtering powered by Nexscope data, supporting multi-dimensional criteria including category, price, monthly sales, keywords, BSR rank, review count, rating, package dimensions, weight, fulfillment type, and more. Trigger when users mention Nexscope product selection, Amazon product search, advanced product selection, BSR filtering, sales rank filtering, keyword-based product discovery, category search, competitor screening, niche product discovery, historical rank filtering, or similar terms. Even if the user does not explicitly mention "Nexscope," trigger this skill whenever the request involves multi-criteria Amazon product search, sales-metric-based filtering, or advanced product selection beyond simple keyword search.

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。

教師なし分析と EDA 前処理（欠測 / 欠損 / NaN / 補完 / imputation / 外れ値処理、クラスタリング / セグメンテーション / k-means / 階層クラスタリング / GMM / DBSCAN、次元削減 / PCA / 主成分分析 / 因子分析 / UMAP / t-SNE、異常検知 / anomaly detection / 不正検知）を実行したら必ずセットで出す図と値のルーター。fillna, dropna, fill_null, SimpleImputer, IterativeImputer, missingno, KMeans, AgglomerativeClustering, DBSCAN, GaussianMixture, silhouette_score, dendrogram, PCA, FactorAnalysis, calculate_kmo, TSNE, umap.UMAP, IsolationForest, LocalOutlierFactor, pyod がコードに現れたとき、またはユーザーが「欠損を埋めて」「クラスタリングして」「主成分で 要約して」「異常を検知して」「データをきれいにして」と言ったときに使う。安定性・k の根拠・平行分析・ベースラインに 言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、対応する references/<手法>.md を読んでから実行する。 教師あり予測は predictive-modeling-diagnostics、回帰・検定は statistical-inference-diagnostics を使う。