2,212 件 ・ 関連度順
概要と使いどころ
Use when reviewing someone else's manuscript for a journal, such as after a review invitation or for a revised R1/R2 version. Drafts a structured, constructive review in the journal's format. Never for your own manuscript; that is /self-review.
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 you have a spec, design doc, structured plan, or prompt_plan and need to generate a pipeline Graphviz DOT file for headless autonomous building. The agent reads the spec, extracts build pipeline phases, determines tech stack, asks about human gates and model preferences, generates a validated DOT digraph with rich self-contained prompts, and saves it to the project root. Handles any language or framework.
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 someone wants to build, refine, or audit their personal brand positioning across professional platforms. Triggers on "personal brand", "brand positioning", "brand voice", "how should I position myself", "professional identity", "LinkedIn presence", "build my brand".
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 DIG (Dive into Graphs) to load graph-learning datasets, run graph generation, self-supervised learning, GNN explainability, 3D graph learning, GOOD OOD datasets, graph augmentation, fair graph learning, and large-scale graph workflows.
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。
Operate CVAT for computer-vision annotation, dataset workflows, SDK/CLI automation, auto-annotation, and self-hosted deployment.
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 working with Dagster OSS: assets, jobs, Definitions, config/resources, schedules/sensors, automation, CLI/local development, deployment operations, GraphQL/webserver, Pipes external processes, components/projects, or editing the Dagster repository itself.
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 the DataSciencePython tutorial/example collection through modern self-contained helpers for Python data-science resources, statsmodels logistic regression, scikit-learn Kaggle-style tabular classifiers, and Twitter JSONL extraction.
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 self-contained Browser Use repo skill for Python agents that automate websites, browser sessions, custom tools, LLM/provider setup, persistent CLI sessions, cloud/sandbox production deployment, MCP, skills, and troubleshooting. Prefer ChatBrowserUse by default and route to focused sub-skills for implementation details.
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 CAMEL-AI to build, configure, tool, remember, evaluate, and troubleshoot multi-agent Python systems with self-contained routing across agents, models, tools, RAG, and evaluation workflows.
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。
Route AutoGPT Platform self-hosting, backend, frontend, and legacy Classic agent work to focused operating guidance.
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。
Operate Baichuan-7B model loading, architecture inspection, C-Eval/MMLU evaluation preflights, and DeepSpeed pretraining setup from self-contained repo guidance.
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。
Shared discipline for every hunt-* skill: scope and authorization gating, the two-account rule, the confirmation gate that separates a real finding from a false positive, enumeration limits, stop conditions, marker discipline, wiki-first query and self-heal, FIND output, Deadends, and wiki distillation. ALWAYS LOADED alongside any hunt skill. Also trigger directly on "is this in scope", "is this a real bug", "how do I confirm this", "should I keep going", "how many IDs should I test", "what severity", "how do I report this", "I got someone else's data". Every hunt-* skill assumes this file; without it they run without their safety and quality layer.
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。
CI/CD pipeline attack hunting (GitHub Actions focus) - pwn requests (pull_request_target), script injection, self-hosted runner takeover, cache poisoning, OIDC-to-cloud token theft, poisoned pipeline execution. Wiki-first, FIND schema output.
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。
Coach the user through actually selling: finding the champion and the buyer, running the first sales conversations, demoing their use case, handling "let me think about it," and closing the first paying customers yourself. Use when developers love it but nobody pays, you've never sold anything and freeze on the conversation, or deals keep stalling on "we'll think about it."
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。
Turn hand-made first-50 traction into self-reinforcing acquisition loops, so the next 5,000 users come from usage, not founder hours. Use when growth stalls the moment the user stops pushing, every signup traces back to a DM or one launch spike, or they're reaching for "more channels" when the real gap is that using the product creates no new users.
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。
Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when relationships matter, and finish with an explicit decision boundary and practical next step.
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 "self-publish", "publish my book", "ISBN", "copyright page", "book metadata", "BISAC", "keywords", "KDP", "IngramSpark", "Draft2Digital", "go wide", "Kindle Unlimited", "print interior", "trim size", "paperback", "ebook", "EPUB", "cover wrap", "spine width", "pricing", "launch plan", "ARC team", "advance readers", "newsletter", "reader magnet", "Amazon ads", "BookBub", "rights", "foreign rights", "audio rights", "film rights", "publishing contract", or wants to take a finished manuscript through production, distribution, launch, and rights management. It owns contracts and rights sales; NOT for permission to quote lyrics, epigraphs, or other material in the book (use editorial-review), or for drafting the blurb or retailer description (use submission).
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 "process beta reader feedback", "alpha reader feedback", "feedback round", "synthesize reader feedback", "reader notes", "beta feedback", "reader readiness check", "review copy", "send the draft to readers", "share with readers who don't use GitHub", "triage the reader panel", or wants to collect, reconcile, and act on external reader feedback for a story project. NOT for running the simulated reader panel itself (use reader-panel), rounds with a professional editor (use editorial-review), or for checking a manuscript is ready to query or publish (use submission or 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。
Use when adding hypermedia links to REST responses, building self-describing APIs, or implementing Spring HATEOAS. Use when you see EntityModel, CollectionModel, or RepresentationModel in the project.
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 an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without trusting self-reported success.
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。
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
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 spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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。
Debug, patch, and verify failed Agent QA runs from MCP evidence, artifacts, logs, and local code without hiding product or infrastructure defects.
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。
Schedule AI agent runs with cron, loops, or external clocks while avoiding unsafe tight autonomous timers.
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。