Productivity Claude Skills
3 457 productivity skills from 510 authors. They keep notes, tasks, calendars and plans in order. Half of them fit into 1 851 tokens or less — that is what one costs your context window when the agent loads it. 479 ship runnable scripts rather than instructions alone. 51 of them cannot work without an MCP server, most often rube. We also found 364 copies of these same skills sitting in other people's repositories — counted once here, not 364 times.
3 457 unique 510 authors 6 updated this month 293 from vendors
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Route You.com integration planning through the you-discover MCP tool, Docs MCP, and direct API options.
> Create, schedule, and manage social media posts via Typefully. ALWAYS use this skill when asked to draft, schedule, post, or check tweets, posts, threads, or social media content for Twitter/X, LinkedIn, Threads, Bluesky, Mastodon, or Substack Notes, or when the user drops a Typefully draft URL such as
Git-as-knowledge-graph workflow for traceability. Use when planning work, brainstorming designs, creating/managing issues and PRs, tracking architectural decisions, or resuming prior sessions. Slash command /shiplog.
Create custom tools for Vapi voice assistants including function tools, API request tools, transfer call tools, end call tools, and integrations with Google Calendar, Sheets, Slack, and more. Use when adding capabilities to voice agents, building tool servers, or integrating external APIs.
> Spawns an Agent Team to collaboratively plan Power Platform / Dataverse applications. Three specialists (Data Architect, UX Designer, The Skeptic) debate and refine the plan before any code is written. Falls back to structured single-agent planning if agent teams "architect this app", "plan the schema", "team planning", "agent team plan", "plan power app", "plan dataverse app", "design the data model".
| Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.
| Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.
Search your conversation history using ripgrep. Use when you need to find previous messages, file edits, tool calls, or decisions from earlier in the session.
| This skill conducts discovery conversations to understand user intent and agree on approach before taking action. It should be used when the user explicitly calls /interview, asks for recommendations, needs brainstorming, wants to clarify, or when the request could be misunderstood. Prevents building the wrong thing by uncovering WHY behind WHAT.
> Лёгкий советник «раздвигающий шторки» — premortem-сессия которая находит дыры в плане с разных углов, предлагает варианты решений по каждой и помогает юзеру быстро принять осознанные решения. История запусков сохраняется в ./docs/premortem/. Use when user types «премортем», «premortem», «найди дыры в плане», «посмотри со стороны на план», «what could kill this plan». questions; creative editing of a draft; already-irreversible decisions.
> Options strategy analysis for Indian F&O markets (NSE). Use when user requests options strategy recommendations, P/L analysis, Greeks calculation, risk management, or F&O strategy planning for Nifty, Bank Nifty, or stock options.
Track and analyze Indian stock market news, corporate announcements, SEBI circulars, bulk/block deals, and earnings calendars. Auto-fetches headlines from MoneyControl, Economic Times, LiveMint, BSE/NSE filings. Use when the user asks about recent news, corporate actions, upcoming events, or wants a daily market news briefing for NSE/BSE.
> Weekly F&O trade planning skill for Indian markets. Analyzes news/macro events, identifies trending sectors and instruments, determines probable direction, suggests option strategy with entry/exit levels, and manages stop-loss and profit booking after position entry. Use when user wants a weekly trade idea, F&O direction call, or ongoing position management for Nifty, Bank Nifty, or stock options.
Use before ending any turn that used tools or produced a deliverable — when tempted to ask "Want me to…?", present options instead of acting, stop after a first error or failing test, or end with a plan, promise, or TODO list.
Automated review-fix loop that spawns 8 reviewers in parallel, fixes quick-fix items automatically, and accumulates strategic items for user decision. Iterates until no issues remain or max iterations reached.
Automate the full Jira bug-fix pipeline end-to-end. Use when the user says 'fix ticket', 'fix PROJ-123', 'fix this bug', 'resolve PROJ-XXX', 'fix and ship this ticket', or passes a Jira ticket ID for autonomous bug resolution. Reads the Jira ticket, implements the fix, reviews it, commits, moves the ticket to QA, assigns to QA engineer, and comments with a summary. Also use when the user wants to automate the fix-review-commit-handoff cycle for any Jira bug ticket.
This skill should be used when the user asks to "develop a feature", "implement a ticket", "build PROJ-123", "run the development pipeline", "develop this ticket end to end", or wants fully autonomous feature implementation with parallel research agents, planning, phased implementation, review, and PR creation. Zero checkpoints; pauses only on blockers.
| brainstorm, ideate, generate ideas, run a design thinking session, create How Might We statements, use SCAMPER, do Crazy 8s, mind map, storyboard, define a problem statement, create empathy maps, build prototypes, test assumptions, run design sprints, reframe problems, do "worst possible idea", use "yes and" building, explore analogous inspiration, conduct user interviews, synthesize insights, create POV statements, plan user tests, iterate on concepts, or facilitate any creative problem-solving session.
Make responses easy to start, scan, and complete - action first, numbered steps, minimal cognitive load, no repetition or filler. Use for ANY request that would otherwise get a long or multi-part answer: how-to and setup questions, multi-step tasks, comparing options, planning work, debugging walkthroughs, or explaining a system. Also use when the user invokes it directly, says they have ADHD, asks to be brief or to cut the fluff, or shows signs of executive dysfunction, overwhelm, decision paralysis, or task paralysis ("I don't know where to start", "this is too much", "I keep putting this off", "just tell me what to do").
Design a MotherDuck-backed customer-facing analytics app. Use for embedded analytics, multi-tenant SaaS reporting, or product analytics for external users -- whenever the decision depends on per-customer isolation, backend routing, service-account boundaries, read scaling, or Hypertenancy-style patterns.
Plan a migration onto MotherDuck. Use when moving from Snowflake, BigQuery, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, source-vs-target validation, rollback, and native-versus-DuckLake posture.
Design a MotherDuck-backed customer-facing analytics app. Use for embedded analytics, multi-tenant SaaS reporting, or product analytics for external users -- whenever the decision depends on per-customer isolation, backend routing, service-account boundaries, read scaling, or Hypertenancy-style patterns.
Plan a migration onto MotherDuck. Use when moving from Snowflake, BigQuery, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, source-vs-target validation, rollback, and native-versus-DuckLake posture.
> Create, validate, import, execute, and export CrowdStrike Falcon Fusion SOAR action IDs BEFORE writing any YAML. NEVER write PLACEHOLDER values for action IDs — resolve every ID via the live API first. Templates and example files in this repo contain PLACEHOLDER markers that are structural guides only — do NOT copy them into output YAML. For plugin config_id values, ask the user. Use this skill when asked to create a CrowdStrike workflow, Fusion workflow, Falcon Fusion automation, SOAR playbook, build a workflow for CrowdStrike, automate CrowdStrike actions, or anything involving CrowdStrike Fusion SOAR.
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。 '
| 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.