3 440 productivity skills from 510 authors. They keep notes, tasks, calendars and plans in order. Half of them fit into 1 854 tokens or less — that is what one costs your context window when the agent loads it. 478 ship runnable scripts rather than instructions alone. 51 of them cannot work without an MCP server, most often rube. We also found 336 copies of these same skills sitting in other people's repositories — counted once here, not 336 times.
3 440 unique 510 authors 2 011 updated this month 275 from vendors
| AI Native 产品方法论——AI Native 用户体验设计的实操 Skill。 用户提供产品场景，Skill 自动执行 UX 设计流程： 任务分析 → 人机分工设计 → 状态可见性 → 纠偏机制 → 信任设计 → 反馈沉淀 → 输出 UX 方案。 基于《AI Native 产品方法论》第16章。
多元推荐改写清单。当团队想重写推荐系统时，最容易只在原目标函数上加一点随机。 这个 Skill 帮你避免"看起来更多元，底层仍然单一"。 基于《AI rebuild product needs》工具卡。
AI Native 产品方法论——流程重构与任务设计的实操 Skill。 用户提供产品形态建议，Skill 自动执行流程重构设计： 任务拆解 → 人机协作模式选择 → 工作流设计 → 节点标注 → 验证与迭代 → 输出流程重构方案。 基于《AI Native 产品方法论》第08章（试验展开-流程重构与任务设计）。 '
AI Native 产品方法论——目标收敛与产品决策的实操 Skill。 用户提供多轮实验记录，Skill 自动执行收敛分析： 实验记录整理 → 证据对比分析 → 收敛信号识别 → 产品决策 → 输出收敛报告与决策结论。 基于《AI Native 产品方法论》第09章（试验展开-目标收敛与产品决策）。 '
AI Native 产品方法论——AI Native 用户体验设计的实操 Skill。 用户提供产品场景，Skill 自动执行 UX 设计流程： 任务分析 → 人机分工设计 → 状态可见性 → 纠偏机制 → 信任设计 → 反馈沉淀 → 输出 UX 方案。 基于《AI Native 产品方法论》第16章。 '
Write reusable ChatCrystal task memories after substantive work completes. Use when implementation or debugging produced a durable fix, pitfall, pattern, or decision worth preserving, and when the environment can either persist it through `write_task_memory` or emit a structured memory candidate for later save.
Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Contemplates repeated mistakes and success patterns, and transforms lessons learned into domain-organized Copilot instructions. Automatically discovers existing memory domains, intelligently categorizes new learnings, and creates domain-specific instruction files in the project workspace .github/instructions/ folder. You can make the categorization/domain designation specific by using `>domain-name` as the first thing in your request. Like so: `/remember >domain-name lesson content here`
Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). See also: ai-evals (eval only), ai-product-strategy (strategy only).
Create a Delegation Pack (brief, decision rights, context handoff, check-in cadence, debrief).
Produce a Cross-Functional Collaboration Pack (charter, stakeholder map, roles contract, decision log).
Build an Energy Management System: drivers/drains map, energy-aligned schedule, recovery routines.
Make evidence-based hiring decisions: scorecards, work samples, reference checks. See also: conducting-interviews (run interviews).
Create a Technology Evaluation Pack (problem framing, options matrix, build vs buy, pilot plan, decision memo). See also: evaluating-trade-offs (general decisions).
Plan early-stage fundraising: raise decision memo, round design, pitch narrative, investor pipeline, diligence prep.
Plan under uncertainty: uncertainty map, hypotheses + experiments, buffers + triggers, cadence.
Prioritize product roadmap: scoring model, ranked opportunities, decision narrative. See also: technical-roadmaps (engineering roadmap).
Run a decision process end-to-end: RAPID/DACI roles, options matrix, decision log, comms.
Run high-signal design reviews: brief, feedback log, decision record, follow-up plan.
Set aligned OKRs/goals: objectives, key results, guardrails, review cadence.
Align stakeholders and secure buy-in: stakeholder map, pre-brief plan, decision summary.
Pack repositories with Repomix for whole-codebase, cross-file analysis. Default to a whole-repo measurement pass, then a filtered whole-repo pack; if it fits under ~1M tokens, stop and hand off to Gemini. Only if it doesn’t fit after the initial noise filter, reduce scope at folder granularity (avoid file-by-file selection), then iterate based on Gemini feedback.
File-based task tracker for projects: tasks live as markdown files in a tasks/ folder, state = folder location (root = inbox, in-progress/, waiting/, done/, cancelled/, decisions/ for open choices), agents manage tasks by moving the files; plus a zero-dependency local board UI (tasx serve) where the user changes states, answers decisions, and leaves comments that are written straight back into the md files and nudge the owning agent via agent-chat. Use when: (1) the user asks to set up / init a task tracker or tasks folder in a project, (2) the user or agent needs to create, list, move, complete, cancel, or comment on tasks in a repo that has a tasks/ folder, (3) the user asks 'what's in progress', 'what needs me', 'what's stale', or wants a task board / dashboard served, (4) starting a work session in a repo with tasks/ (run tasx doctor and reconcile), (5) a task is blocked on the user's feedback or on a decision — file it as waiting/ or a decision instead of asking and losing the thread, (6) generalizing/migrating older ad-hoc task folders (myhdd-style) onto the shared convention. Triggers on: tasks folder, task tracker, task board, tasx, kanban, what's in progress, needs my feedback, stale tasks, task dashboard.
Brainstorm a game idea through one-question-at-a-time designer interviews. Produces game.md, systems-index.md, and art-direction.md, or runs as discussion-only.
Inspect the repo state and show a status panel across all milestones, recommending the earliest actionable next step in the workflow.
READ this skill when designing or planning any game system architecture — including combat, skills, AI, UI, multiplayer, narrative, or scene systems. Contains paradigm selection guides (DDD / Data-Driven / Prototype), system-specific design references, and mixing strategies. Works as a domain knowledge plugin alongside workflow skills (OpenSpec, SpecKit) or plan mode of an agent.
定时任务管理
REQ-aware Product Owner for backlog and sprint management with full traceability. Use for sprint planning, backlog prioritization, or converting REQs to INVEST stories.
Manages financial planning, OKRs, team resources, vendor relationships, and operational compliance with full traceability. Use for budgeting, OKR tracking, resource planning, vendor management, or operational risk assessment.
Generates periodic executive briefings (weekly/monthly/quarterly) by aggregating health status, critical alerts, key decisions, and upcoming milestones from all C-Suite domain dashboards into a single narrative update.
Chief Executive Officer (CEO) orchestrator for strategic alignment, cross-C-suite coordination, board relations, crisis management, and executive decision governance. Orchestrates all C-suite agents and venture-strategist.
Automates Principle No. 9 (Decision Logging) and Principle No. 5 (Regulatory Readiness). The 'Chronicler' ensuring every choice is backed by a 'Why' and mapped to a requirement for ISO/GxP auditability.