1,459 件 ・ 関連度順
概要と使いどころ
Prepare to work on a GitHub issue or RFC. Use when the user says /start-work, asks to start an issue, begin an RFC implementation, or pick up a task. Creates the branch, gathers context, and checks learnings.
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 unclaimed open source issues that match the user's skills and experience level. Searches for issues created by maintainers/org admins, checks contribution eligibility, and ranks by learning value. Use when looking for an issue to contribute to, starting OSS contributions, or finding GSoC-friendly issues. Not for finding problems that nobody has filed an issue for yet. Use oss-find-real-issues for that.
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。
Guided codebase exploration for understanding a repo's architecture, patterns, and domain language before contributing. Not issue-specific. builds general understanding. Use when exploring a new repo, wanting to understand how a project works, or preparing to become a regular contributor. Not for learning a framework or language you have never used. Use oss-learn-stack for that.
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 learning retrospective on the Kai harness. Mines gate-failure logs and 30-day performance results into lessons, triages candidate lessons (promote/keep/retire), and graduates repeated lessons into enforced gate checks with golden corpus cases. Use when "retro", "what have we learned", "triage lessons", "promote lessons", "why does this keep failing", "harness retrospective", or monthly / after any heavy content sprint.
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。
Monthly marketing audit and executive review. Pulls the last 30 days of source-backed marketing, analytics, SEO, CRO, content, paid media, lifecycle, reputation, and pipeline data; compares it to the previous period; summarizes strategic learning; and produces an executive report plus next-month plan. Use when "monthly audit", "monthly marketing review", "monthly report", "executive marketing review", "board-ready marketing report", "month-end audit", or any request for a 30-day marketing audit.
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 at end of sprint to append operational learnings as structured JSONL to the project retro log.
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。
Working memory protocol — maintains context across sessions via CONTINUITY.md. Inspired by Loki Mode. Read at turn start, update at turn end. Captures mistakes and learnings to prevent repeating errors.
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。
Self-learning SEO content pipeline: dashboard, multi-agent queue, token budgets, research → write → audit → publish. StoryBrand/Cialdini/JTBD-style frameworks; config-driven. Use for content factory, batch articles, or scaled publishing.
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 a book or long document into a reusable AI mentor with source-grounded lessons, guided practice, citations, and persistent learning records.
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。
5-phase repeatable structure for autonomous agent sessions: context-load, tiered work-selection, coordination claim, execute, and persist-learning. Prevents duplicate work across concurrent sessions and ensures every run produces durable artifacts. Runtime-agnostic — Claude Code, gptme, Codex, or any persistent-workspace agent.
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。
audioFlux is a deep learning tool library for audio and music analysis and feature extraction, supporting dozens of time-frequency transforms and hundreds of feature combinations for classification, separation, MIR, and ASR 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。
Expert sales enablement strategist for building high-performing sales teams. Use when designing sales training programs, onboarding and ramp plans, sales playbooks, coaching frameworks, certification programs, or competitive intelligence distribution. Covers content strategy, tool adoption, performance measurement, and continuous learning systems. Use for building sales academies, creating enablement content, and optimizing sales productivity.
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。
Nature-style remote-sensing manuscript reviewer skill for evaluating Earth-observation products, retrievals, machine-learning maps, validation design, uncertainty propagation, spatial transferability and claim-scope calibration.
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。
Session lifecycle management. Parent skill for session-related skills: learning (pattern extraction) and compact (context compression).
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。
Extracts reusable patterns from sessions. Use at session end to capture debugging insights and project-specific knowledge.
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。
Structured retrospective after completing a delivery increment or diamond. Captures learning for continuous improvement.
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。
Captures project-specific patterns and anti-patterns into the project documentation. Loaded by other skills when they discover project-specific knowledge worth encoding for future sessions.
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。
Captures project-specific patterns and anti-patterns into the project's configuration file. Loaded by other skills (bug-killer, feature-dev, etc.) when they discover project-specific knowledge worth encoding for future sessions.
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。
Captures project-specific patterns and anti-patterns into the project's CLAUDE.md. Loaded by other skills (bug-killer, feature-dev, etc.) when they discover project-specific knowledge worth encoding for future sessions.
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。
Evaluates ML applications in investment with feature engineering, model selection, and implementation considerations for alpha generation. Use when evaluating ML for investing, designing ML pipelines, or assessing ML strategy feasibility.
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。
Extract actionable insights from completed coding sessions. Use when a session completes to capture learnings for future sessions.
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。
CORE learning workflow — use this whenever the user has a source note open (paper, post, book, lecture, course, clipping) and wants to extract knowledge, understand what they just read, or build it into their vault. Triggers on "process this note", "process this paper", "extract concepts", "what can I learn from this", "atomize this", "add this to my vault", "what's important here", or when the user shares a note and asks for help understanding it. Do NOT wait for an explicit /process command — if the user is engaging with a source note and wants to go deeper, this skill applies.
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 repeated wins or failures should become durable memory, wiki, or skill updates.
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。
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.
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。