「plan」の検索結果

AI agent guidance — agent architecture, tool design, planning loops, guardrails, evaluation, and production agents.

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。

Architect autonomous agent systems — perception/action loops, tool design, planning strategies, memory, and multi-agent orchestration patterns. Use when designing an agent from scratch or scaling a prototype to production.

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。

Execute a project from a folder of raw materials. Use when the user drops messages, screenshots, documents, drafts, and references into a project directory and wants Codex to identify the real goal, build a plan, do the work, drive the project to a submission-ready state, verify completion, and return a clear final summary of what was required, what was completed, and how it was done.

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。

Classify a proposed code change as Leaf Node or Core Code to decide how much AI usage and review rigor is appropriate. Use this skill whenever the user asks "is this leaf or core", "what kind of change is this", "should I vibe code this", "can AI write this", "how rigorous should the review be", or before starting any non-trivial change. Also useful as a sub-step inside other workflows (planning, PR prep, code review) when classification matters. Asks 6 diagnostic questions about propagation range, change frequency, tech-debt tolerance, examples, failure cost, and review intensity, then outputs a clear classification with the recommended approach. Trigger even when the user describes a change without explicitly asking for classification — the answer changes how they should proceed.

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。

Generate Product Requirements Documents (PRDs) in Markdown format. Use when users want to create a PRD, spec out a feature, document requirements, or plan new functionality. Triggers on requests like "create a PRD", "write requirements for", "spec out this feature", or "document this feature idea".

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。

Multi-agent management workflow — task delegation, progress monitoring, quality verification with regression testing, feedback delivery, and cross-review orchestration. Use this skill when coordinating multiple agents on a shared task, monitoring delegated work, ensuring quality across agent outputs, or implementing a multi-phase plan (3+ phases or 10+ file changes).

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。

Converts a successful threat hunt into a durable detection by extracting the discriminating logic, defining data sources and thresholds, and emitting a Sigma rule plus a test and tuning plan. Activates for requests to operationalize a hunt, turn a finding into a detection rule, or productionize hunt logic as a Sigma detection.

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。

Bypasses anti-VM and sandbox checks during analysis by locating the specific detection routines (artifact strings, timing, CPUID/hypervisor bit) and planning patches or environment hardening so the sample detonates. Activates for requests to bypass anti-VM checks, defeat sandbox detection, or make an evasive sample run for analysis.

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。

Deep code review - Elixir idioms, Phoenix patterns, architecture, safety. Launches 3 parallel reviewers + plan-reviewer gate. Use when: review, review this, code review, check this code, review my changes, is this good, what do you think, code smells.

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。

Implementer - fetches a GitHub issue, builds an implementation plan, then implements using TDD. Use when: implement, build this, code this, add this feature, TDD, test first, red green refactor, pick a task, next task, dev.

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。

Product Owner - scans codebase and creates or amends GitHub issues with PRD. Use when: PRD, feature, issue, create issue, amend issue, what should we build, plan feature, design feature, scope, requirements.

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。

Coordinate a cross-session or cross-machine handoff — snapshot the open work, write a status doc to the pulp-planning repo on main, and emit a ready-to-paste goal prompt that links it, so a fresh session (often on another machine) can pick up and finish. Use when the user says "coordinate a handoff", "hand this off", "move this work to a new machine", "we're retiring this session", or asks for a status doc plus a goal for someone else to continue.

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。

Search, inspect, plan, apply, remove, pack, and scaffold local Pulp package manifests. Use for Pulp-native source/UI/template kits and for untrusted or semi-trusted manifest-bearing artifacts that must be reviewed before project mutation.

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。

End-to-end SEO operations for B2B SaaS organic visibility. Use this skill when you need keyword research, technical SEO audits, content optimization, link building strategy, international expansion, AI/AEO optimization, schema markup, Core Web Vitals improvements, and organic traffic growth planning. Also triggers on: SEO, keyword research, can we rank for this keyword, which keywords are actually worth it, qualify these keywords, check the SERPs, keyword shortlist, too many keywords, technical SEO, link building, content optimization, international SEO, hreflang, x-default, multilingual site, translated pages not ranking, our German site gets no traffic, language switcher, auto-redirect by country, machine translation SEO, content parity, ccTLD vs subfolder, AI search, AEO, GEO, Core Web Vitals, schema markup, organic traffic, G2 listing, Capterra, TrustRadius, review-platform category page, best software roundup, listicle placement, directory submissions, do we need a Google Business Profile in [country], local SEO with no office, Google Business Profile eligibility, virtual office address SEO, coworking address business profile, business profile suspended, NAP consistency, local citations for SaaS, LocalBusiness schema, server log analysis, log file analysis, what is Googlebot crawling, is Googlebot hitting errors, verify Googlebot, fake Googlebot, crawl budget, do we have a crawl budget problem, Crawl Stats report, new pages not getting crawled.

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。

基于域健康报告和沙盘Gap记录，识别断裂模式，生成具体的补全行动方案：新Skill提案/现有Skill修改建议/缺失触发链路描述。与 skill-evolution-planner-meta 的区别：后者基于历史日志做宏观演进规划；本Skill基于具体Gap证据做定向修复。触发词：「优化[域名]域」「修复[域名]域Gap」「[域名]域自优化」「根据健康报告生成修复方案」。通常由 skill-closure-verifier-meta 调用，也可独立触发。

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。

整改计划制定 Skill。将差距报告、审核发现、或不兼容分析转化为有优先级、有验收标准的可执行整改计划。每个步骤配对验证方法，防止「计划没有验证」的情况。触发词：「基于这个分析制定整改计划」「怎么修复这些问题」「制定改进方案」「这些差距怎么补」「把发现转成行动计划」。

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。

主动式产品演进规划 Skill。基于「AI时代产品问题全景框架」等设计原则，主动分析当前产品现状，识别原则缺口，生成有理据的改进建议。与 role-产品经理 的区别：后者执行具体设计任务；本 Skill 是「对照原则做战略诊断，主动思考产品应该怎么演进」。触发词：「基于产品原则看看有什么可以改进的」「帮我看看产品还缺什么」「给我一些迭代建议」「从第一性原理看这个产品」「主动分析产品」「这个产品符合马斯洛新瓶颈吗」「产品演进建议」。

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。

Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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。

Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

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。

Risk analysis and approach selection before planning. Use when requirements are ambiguous, approaches vary, or work touches hub/bridge nodes. Skip for typo fixes.

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。

Proactive architecture review — explore codebase structure, generate a self-contained HTML report with Mermaid diagrams and candidate improvements, then grill the findings. Use when planning a large refactor, onboarding to an unfamiliar codebase, or before a major architectural change.

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 a formal state machine to issues — assign category (bug/enhancement/question/spike) and state (needs-triage → needs-info → ready-for-agent → ready-for-human → wontfix). Issues marked ready-for-agent become inputs to supergraph:plan. Use when processing a backlog, reviewing new issues, or preparing work for automation.

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。

Convert current conversation context into a structured Product Requirements Document (PRD) and optionally post it to GitHub Issues. Use before /supergraph:plan when requirements came from a conversation, user story, or feature discussion rather than a formal spec.

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。

One-shot module map — go up a layer of abstraction and get a domain-vocabulary module map of the codebase. Use when lost in unfamiliar code, after a long deep-dive session, or when you need to re-orient before planning.

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。