161 件 ・ 関連度順
概要と使いどころ
Reddit 公开社区数据查询与讨论分析 skill，通过 MaxHub API 查询 subreddit、帖子详情、评论树、用户资料、搜索、热门/趋势内容等。适合社区舆情、话题研究、竞品反馈收集、内容洞察和用户讨论分析。默认 read-only；agent 应优先使用 recipes 串联 subreddit/post/comment/user 相关端点，并显式处理评论/UGC 中的个人信息和敏感内容。所有请求发送到 https://www.aconfig.cn。
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。
快手（Kuaishou）公开数据查询与短视频分析 skill，通过 MaxHub API 查询作品详情、作者主页、评论、搜索、热榜、直播和相关内容。适合快手内容选题、达人分析、作品互动追踪、热门趋势监控和运营复盘。默认 read-only；agent 应按 recipes 选择单次或链式调用，使用字段流字典传递 photo_id/user_id 等参数，禁止自行拼接路径或猜测参数。所有请求发送到 https://www.aconfig.cn。
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。
知乎（Zhihu）公开内容与用户数据分析 skill，通过 MaxHub API 查询问题、回答、文章、专栏、评论、热榜、搜索和用户画像。适合问答内容研究、KOL 分析、舆情监控、选题挖掘和营销洞察。默认 read-only；agent 应按 recipes 选择问题/回答/文章/用户/搜索链路，正确传递 question_id/answer_id/article_id/url_token 等参数，避免混用 ID。所有请求发送到 https://www.aconfig.cn。
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。
Lemon8 内容与用户公开数据分析 skill，通过 MaxHub API 查询笔记/帖子详情、评论、用户资料、搜索与推荐内容。适合生活方式内容研究、种草笔记分析、创作者画像、话题趋势和竞品监控。默认 read-only；agent 应从 recipes/_index.md 匹配用户意图，再按 recipe 详情传递 post_id/user_id/keyword 等参数。所有请求发送到 https://www.aconfig.cn。
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。
Threads 公开社交内容查询与用户分析 skill，通过 MaxHub API 查询帖子、用户资料、评论、搜索、时间线和相关互动数据。适合海外社媒内容研究、账号画像、话题追踪和互动分析。默认 read-only；agent 应从 recipes 匹配用户意图，按 post_id/user_id/keyword 等字段流链式调用，并避免过度采集个人社交数据。所有请求发送到 https://www.aconfig.cn。
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。
微信公众号（WeChat Official Accounts）公开内容查询与文章分析 skill，通过 MaxHub API 查询公众号文章、账号信息、评论、阅读互动、视频/媒体相关数据和搜索能力。适合公众号内容选题、舆情分析、账号画像、评论研究和媒体监控。默认 read-only，但部分媒体/评论数据具有隐私和版权敏感性；agent 应按 recipes 调用并遵守数据最小化与授权边界。所有请求发送到 https://www.aconfig.cn。
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。
Plan and implement native RayTK (t3kt/raytk) raymarching/SDF toolkit integration into tdmcp — beyond the existing package-manager staging, into AI-driven RayTK operator (ROP) node graphs. Use whenever the user wants to build, plan, design, implement, continue, fix, QA, or ship RayTK support: create/wire RayTK ROPs, a RayTK operator knowledge catalog, a RayTK-native raymarch/SDF scene tool, RayTK recipes, or the RayTK setup/doctor flow — including follow-ups (re-run, continue, refresh after a new RayTK release, re-QA, docs). Also triggers on 'raytk', 'raymarching toolkit', 'SDF toolkit', 't3kt', 'montar um build para o raytk', 'integrar o raytk'. This owns RayTK integration specifically; use it before the generic tdmcp-pipeline for this feature. Simple questions about RayTK can be answered directly.
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。
Drive the ENTIRE tdmcp roadmap-to-1.0 to completion as a resumable, wave-by-wave campaign — Milestone 4 (generative-AI bridge wave), Milestone 5 (mixer scene arming), and the v1.0 consolidation gates G1–G6 — routing each class of work to the right existing sub-harness. Use whenever the user wants to "implement everything / all the milestones / the whole roadmap / finish the road to 1.0", run the next milestone, close the consolidation gates, or build a long autonomous campaign across M4/M5/G1–G6. ALSO use for every follow-up: continue/resume the roadmap campaign, run the next wave, re-run a failed wave, fold in QA results, check campaign status, re-prioritize, or scope to one milestone/gate. This is the CAMPAIGN layer ABOVE tdmcp-pipeline / tdmcp-feature-lead / tdmcp-backlog-campaign — it sequences across MULTIPLE sub-harnesses (tools, mixer, coverage, docs, bridge, recipes, submission), which the generic backlog-campaign does not. For a SINGLE feature use tdmcp-pipeline; for one tool-shaped backlog file use tdmcp-backlog-campaign. Simple questions can be answered directly.
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 a generative recipe DSL so agents emit small validated JSON documents and ONE audited interpreter renders them all — data, not code. Covers the N-artifacts threshold for choosing a DSL over bespoke code, designing roles as style slots so a single recipe renders unchanged in every output style, the validator gate (compile + bounds + real render in all styles, plus a --fast mode for corpus-wide sweeps), hand-authored seed recipes, and the emergent conventions that keep a corpus of hundreds consistent without per-item steering. Use when many similar artifacts (icons, status badges, diagram nodes, test fixtures) must be generated by agents, when per-item bespoke code is multiplying unreviewable code paths, or when a whole corpus must be re-rendered cheaply into new styles.
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。
Conventions for writing justfile recipes in Nix-based projects.
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。
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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。
Conventions for writing justfile recipes in Nix-based projects.
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 this skill when designing or building CRM Analytics Data Prep recipes — including node selection, join patterns, bucket field configuration, formula expressions, and scheduling. Triggers: 'build a recipe', 'join datasets in analytics', 'bucket a measure field', 'schedule a recipe', 'data prep transformation'. NOT for dataflow JSON and its node types — use admin/analytics-dataflow-development. NOT for tuning a slow or oversized dataset — use data/analytics-dataset-optimization.
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, submit and reconcile reviewed Fusion Automation jobs with exact sources, scoped authorization and explicit budgets. Not for arbitrary model-generated cloud scripts.
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。
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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 invoked as $justfile or when creating, editing, reviewing, or adding recipes to a justfile, Justfile, or *.just file.
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。
Continuously detect recurring UI/UX concepts during frontend discussions, track conversation-wide recurrence, and register repeated concepts as structured knowledge assets in a Notion database via MCP. Activates when UI/UX principles, heuristics, laws, or design patterns recur during frontend work, or on manual @ux commands (@ux save, @ux skip, @ux link).
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。
Finalize code changes for production readiness by removing duplicate logic, auditing hardcoded values, verifying branch/worktree intent, verifying build integrity, structuring clean commits with Conventional Commits format, and forcing a post-publish branch/worktree cleanup decision.
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。
Sync reusable frontend decisions into persistent project documentation after UI work. Use when frontend changes introduce or modify component APIs, variants, design-system tokens, styling conventions, layout behavior, accessibility rules, interaction patterns, responsive behavior, or agent operating instructions; when the user asks to reflect decisions in components.md, design system docs, AGENTS.md, CLAUDE.md, GEMINI.md, or similar project memory files; or before finalizing frontend work where future consistency depends on decisions made during 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。
Verify that the current Git branch, upstream state, and dirty working tree match the user's intended task before staging, committing, pushing, or continuing work from another session. Use this to detect stale branch reuse, leftover dirty files from previous sessions, unrelated commits on the current branch, cases where work should be split into a new branch or worktree, and post-publish cleanup decisions after push, PR creation, or PR merge.
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。
Ship an already-committed Git branch through GitHub: verify branch/worktree intent, push, create or mark a pull request ready for review, optionally wait for CI, optionally address review comments automatically, and merge with explicit safety gates. Use when a solo developer repeatedly asks to publish committed work, register a ready PR, handle PR feedback, and complete merge.
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。
Explore open-source GitHub repository source trees via web browsing to analyze and compare feature implementations at the code level. Supports two modes: cross-project comparison and single-project deep dive. Use when evaluating how OSS projects implement a specific feature, choosing architecture patterns, or benchmarking implementation strategies.
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。
Re-audit code changes to identify side effects, regression risks, and unhandled edge cases before merging or deploying. Use after a change is complete and before merging to a shared branch, before deploying a hotfix, after complex multi-module refactoring, or when a previous review may have missed side effects in async, state-mutating, or security-sensitive code paths.
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 architecture and design documents for implemented code changes with risk-based depth selection. Automatically evaluates risk signals, layer spread, and change magnitude to choose documentation level (A/B/C). Use after completing a feature, before merging a PR that touches multiple layers or sensitive areas (auth, infra, migrations, payments), or when an implemented change needs a design record or ADR.
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。