Skill-uri pentru agenți pe care le poți verifica înainte să le instalezi
Skill-uri SKILL.md publice din depozite GitHub cu peste 50 de stele și de la producători, scanate și evaluate. Instalează oricare dintre ele cu o singură comandă în Claude Code, Cursor, Codex, GitHub Copilot sau Gemini CLI.
17.527 de skill-uri din 611 depozite · actualizat acum 26 de minute
1.229 de skill-uri
- narco-checkopen-gitagent/opengapMemory integrity audit. Detects hallucinations, circular confirmations, and state poisoning. Runs automatically after 2 consecutive failures or at nightly deep dive. Uses Opus 4.6 as the auditor model.ProductivitateFără avertismente★ 3 K↓ —
- packet-factoryopen-gitagent/opengapCreates, reviews, and dispatches atomic work packets. Every task with a side effect requires an APPROVED packet before execution. Scope is immutable after approval — expansion requires a new packet.ProductivitateFără avertismente★ 3 K↓ —
- aer-workflowbrycewang-stanford/Auto-Empirical-Research-SkillsUse when deciding which AER-skills sub-skill to use next, before choosing a specialized skill or after a user asks how to sequence manuscript work from topic selection through rebuttal for AER, AER:Insights, or AEJ journals. Routes — does not replace — the specialized skills.ProductivitateFără avertismente★ 4,7 K↓ —
- rebuttalbrycewang-stanford/Auto-Empirical-Research-SkillsWorkflow 4: Submission rebuttal pipeline. Parses external reviews, enforces coverage and grounding, drafts a safe text-only rebuttal under venue limits, and manages follow-up rounds. Use when user says "rebuttal", "reply to reviewers", "ICML rebuttal", "OpenReview response", or wants to answer external reviews safely.ProductivitateFără avertismente★ 4,7 K↓ —
- tablebrycewang-stanford/Auto-Empirical-Research-SkillsEconometrics skill for creating publication-quality LaTeX regression and summary tables. Activates when the user asks about: "regression table", "LaTeX table", "esttab", "stargazer", "modelsummary", "publication table", "format results", "multi-panel table", "journal table", "export regression results", "table formatting", "回归表格", "LaTeX表格", "结果导出", "论文表格", "回归结果格式化", "多模型表格"ProductivitateFără avertismente★ 4,7 K↓ —
- memory-detectiveLC044/TrailSnap根据模糊的时间、地点、同行人、画面和文字线索找回可能的照片事件，并在用户确认后生成可追溯的回忆故事。ProductivitateFără avertismente★ 802↓ —
- statsbrycewang-stanford/Auto-Empirical-Research-SkillsEconometrics skill for descriptive statistics and summary tables. Activates when the user asks about: "descriptive statistics", "summary statistics", "summary table", "Table 1", "balance table", "means and standard deviations", "correlation matrix", "data summary", "sample characteristics", "variable distributions", "描述性统计", "描述统计", "汇总统计", "统计表", "均值标准差", "平衡性检验", "相关矩阵", "样本特征", "变量分布"ProductivitateFără avertismente★ 4,7 K↓ —
- c5brycewang-stanford/Auto-Empirical-Research-SkillsMeta-Analysis Master with Data Integrity, Effect Size, Error Prevention & Sensitivity Multi-gate validation and workflow orchestration for meta-analysis. Absorbed C6 (Data Integrity Guard), C7 (Error Prevention Engine), B3 (Effect Size Extractor), E5 (Sensitivity Analysis - Meta) capabilities Triggers: meta-analysis, pooled effect, heterogeneity, forest plot, funnel plot, Hedges g, data integrity, effect size extraction, sensitivity analysisProductivitateFără avertismente★ 4,7 K↓ —
- stata-logbrycewang-stanford/Auto-Empirical-Research-SkillsTail, read, or search a Stata log file from a previous command or background task.ProductivitateFără avertismente★ 4,7 K↓ —
- next-ideabreaking-brake/cc-wf-studioRun one unattended IDEATION iteration of the autonomous value-creation loop — invent improvements a user of cc-wf-studio would notice, judge them against the value bar, and file the winners as locked `idea` issues. Never implements anything; the next-task skill builds from the queue this skill fills. Use when the user says "アイデア出して", "next idea", or wants proposals without implementation.ProductivitateFără avertismente★ 5,4 K↓ —
- workflow-schema-tuningbreaking-brake/cc-wf-studioUse when modifying `resources/workflow-schema.json` in cc-wf-studio to influence how AI agents generate workflows via the cc-workflow-ai-editor skill. Triggers include "AIが特定のノードタイプを選んでくれない", "ワークフロー生成のバイアスを調整したい", "スキーマの description を変えたい", "新しいノードタイプを追加したい", "嘘の制約がスキーマに混じっていないか確認したい". Covers what the schema actually does (instructions to AI, not runtime constraints), the design philosophy (align direction, do not prescribe rules), the build pipeline (.json → .toon auto-generated), and known bias sources to audit.ProductivitateFără avertismente★ 5,4 K↓ —
- diverga-memorybrycewang-stanford/Auto-Empirical-Research-SkillsDiverga Memory System v7.0 - Context-persistent research support with checkpoint auto-trigger and cross-session continuity. Triggers: memory, remember, context, recall, checkpoint, decision, persist, 기억, 맥락, 세션, 체크포인트ProductivitateFără avertismente★ 4,7 K↓ —
- next-taskbreaking-brake/cc-wf-studioRun one unattended IMPLEMENTATION iteration of the autonomous value-creation loop — steward any in-flight PR, fix interrupts (red CI / security / human bugs), or else build ONE queued `idea` issue on a branch off auto-dev and open a PR that squash-merges on green CI. Ideation lives in the next-idea skill; this skill consumes its queue. Use when the user says "次のタスク", "next task", "続きをやって", or wants autonomous progress without specifying what to do.ProductivitateFără avertismente★ 5,4 K↓ —
- obsidian-basesbrycewang-stanford/Auto-Empirical-Research-SkillsCreate and edit Obsidian Bases (.base files) with views, filters, formulas, and summaries. Use when working with .base files, creating database-like views of notes, or when the user mentions Bases, table views, card views, filters, or formulas in Obsidian.ProductivitateFără avertismente★ 4,7 K↓ —
- aer-tables-figuresbrycewang-stanford/Auto-Empirical-Research-SkillsUse when constructing or revising regression tables, descriptive statistics tables, or figures after results are estimated and before submission for an AER, AER:Insights, or AEJ manuscript. Implements AER booktabs house style, regression-table layout, and figure-note conventions.ProductivitateFără avertismente★ 4,7 K↓ —
- estimatebrycewang-stanford/Auto-Empirical-Research-SkillsRun a structural estimation pipeline — routes to /workflows:work with estimation context from empirical-playbookProductivitateFără avertismente★ 4,7 K↓ —
- rebuttalbrycewang-stanford/Auto-Empirical-Research-SkillsWorkflow 4: Submission rebuttal pipeline. Parses external reviews, enforces coverage and grounding, drafts a safe text-only rebuttal under venue limits, and manages follow-up rounds.ProductivitateFără avertismente★ 4,7 K↓ —
- jetson-inference-mem-tuneNVIDIA/skillsPick the serving stack and per-runtime memory flags (vLLM, SGLang, llama.cpp, TensorRT Edge-LLM) for an LLM/VLM workload on any NVIDIA Jetson.ProductivitateFără avertismente★ 3,7 K↓ —
- json-canvasanbeime/skill产品经理与内容创作者在构建Obsidian知识库或梳理复杂逻辑时，当需要创建思维导图、流程图等可视化画布，使用此技能可自动生成标准.canvas文件，精准配置节点、连线与层级，一键生成完美排版，彻底告别手写JSON的繁琐与报错。ProductivitateFără avertismente★ 8,6 K↓ —
- obsidian-basesanbeime/skill内容创作者与技术写作员在管理海量笔记时，当需要构建 Obsidian 数据库视图、配置过滤器或编写 YAML 公式，使用此技能可自动生成标准 .base 文件，轻松实现笔记的表格/卡片多维展示与数据汇总，让知识库管理效率翻倍。ProductivitateFără avertismente★ 8,6 K↓ —
- obsidian-markdownanbeime/skill技术写作员与内容创作者在构建Obsidian知识库时，当需要编写带双向链接、Callout或Frontmatter的复杂笔记，使用此技能可精准生成标准Markdown文档，完美支持wikilinks与嵌入，大幅提升知识管理与排版效率！ProductivitateFără avertismente★ 8,6 K↓ —
- xhs-note-creatorZJU-REAL/Easel小红书内容总入口：生成标题、正文、caption、hashtags，以及 3-9 张图文卡片或短视频分镜，覆盖素材分析、卖点评估、去 AI 味和质检。 当用户说“写/做小红书笔记、小红书图文/种草/文案、出一套卡片、小红书视频”时使用。 整套笔记用本 SKILL；仅渲染卡片用 card-xiaohongshu；其他平台的通用文案用 social-content。ProductivitateFără avertismente★ 4 K↓ —
- skill-event-calendarZJU-REAL/Easel查询未来 N 天的节日、纪念日、电商节点、行业事件，为创作者提供内容蹭点。 覆盖中国节假日、国际节日、电商大促、行业展会、考试节点、体育赛事等。 当用户说"未来有什么节日"、"下个月有什么可以蹭"、"节点日历"、"营销日历"、 "有什么热点可以提前准备"、"内容日历节点"、"最近有什么节日"、 "什么时候有大促"、"节日营销"时触发。 和 skill-content-calendar 的区别：content-calendar 排内容发布日程， event-calendar 提供节日和事件的数据查询，是选题灵感的上游数据源。ProductivitateFără avertismente★ 4 K↓ —
- pufferlibforyourhealth111-pixel/Vibe-SkillsThis skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.ProductivitateFără avertismente★ 3,8 K↓ —