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 336 copies of these same skills sitting in other people's repositories — counted once here, not 336 times.
3 457 unique 510 authors 2 017 updated this month 293 from vendors
Design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.
> Audits any domain's AI-readiness by using curl to directly probe robots.txt, llms.txt, and llms-full.txt, then scores each file against a structured checklist and delivers a formatted report with pass/warn/fail findings and actionable fixes. Use this skill whenever a user provides a domain or URL and wants to know if llms.txt or llms-full.txt is available, discoverable, or properly structured. Trigger on phrases like "check llms.txt for", "does this site have llms.txt", "find llms.txt", "check llms for this url", "audit llms.txt", "is llms-full.txt available", or any time a user shares a domain/docs URL and wants AI-readiness checked. Also trigger when the user wants to verify GEO/AEO readiness of a documentation site.
Draft, audit, or activate a compact /goal when the user asks for a persistent objective or wants Codex to work until a verifiable outcome is true. Defines Done, evidence, constraints, stop conditions, optional one-question-at-a-time clarification, and only necessary worker use. Not for ordinary implementation, Q&A, one-off edits, loose brainstorming, or subjective work with no rubric.
Generate LinkedIn post ideas from external sources (files, URLs, research). Use when the user provides source material (PDFs, URLs, articles) to brainstorm topics. NOT for writing or developing drafts - use write-linkedin-post instead.
Generate opinion piece ideas from recent LinkedIn posts (last 30 days). Use when asked to find opinion topics, brainstorm article ideas, or cross-pollinate content between LinkedIn and opinion pieces.
Helps managers cut through noise and identify their highest-leverage actions for the day or week. Aggregates signals from calendar, triage, team context, and OKRs/goals. Presents a suggested focus list grouped by urgency, importance, and investment. The manager reviews and adjusts. Supports effective execution and prioritisation.
Use when the user asks to invoke, delegate to, or collaborate with Codex on any task. Also use PROACTIVELY when an independent, non-Claude perspective from Codex would add value — second opinions on code, plans, architecture, or design decisions.
Use @aleabitoreddit ("Serenity")'s full mention archive (built by the follow-aleabito skill) to anticipate where her attention is moving and generate candidate ideas in her style. Two modes — (1) RADAR reads the live mention data for attention momentum (which tickers she is heating up on, new entrants, conviction core, theme rotation) via scripts/radar.js; (2) GENERATOR applies her empirically-mined patterns (theme-rotation logic, selection signature, catalyst playbook) to propose her likely next focus. Every candidate is gated through the serenity-method checklist. This is a CANDIDATE GENERATOR + CHECKLIST, never an oracle or buy/sell signal. Trigger on "what is Serenity ramping on / her next pick / aleabito radar / predict her next move / generate ideas like her / 她下一个可能看什么".
Challenges AI-generated plans, code, designs, and decisions before you commit. Pairs with any other skill as a review layer. Uses pre-mortem analysis, inversion thinking, and Socratic questioning to find what AI missed — blind spots, hidden assumptions, failure modes, and optimistic shortcuts. The skill that asks 'are you sure about that?' so you don't have to.
Triage Monte Carlo alerts interactively or build an automated workflow. Fetch, score, and troubleshoot alerts using MCP tools now, or design a reusable workflow that runs on a schedule.
Calendar and scheduling management. Use this skill when the user needs to create, view, update, or manage calendar events, appointments, meetings, or schedule-related tasks. Supports ICS file format, recurring events, and timezone handling.
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
Manage Google Calendar - search, create, update events and answer calendar questions. Use when user wants to interact with their Google Calendar for scheduling and calendar operations.
Automate payer review of prior authorization (PA) requests. This skill should be used when users say \"Review this PA request\", \"Process prior authorization for [procedure]\", \"Assess medical necessity\", \"Generate PA decision\", or when processing clinical documentation for coverage policy validation and authorization decisions.
Mock-first, layer-by-layer feature development. Instead of building a feature end-to-end and hoping the interface works, start by mocking at the user-facing surface with realistic data, get user acceptance on the experience, then deepen one complexity layer at a time with TDD. Everything is anchored on disk so work survives across sessions. Use whenever building a new feature, adding significant UI, planning a multi-layer change, or when the user mentions 'mock first', 'let me see it first', 'prototype this', 'simulate this feature', 'build this layer by layer', or /hk-mock-first.
Run a scout wave over outside sources (links, blogs, trending repos) into a progressive-disclosure research wiki in the current project, then distill findings into the project's ideas/backlog file. Generic across projects. Use when the user shares links/blogs to research, asks to "scout", "build a wiki from these", or wants outside ideas funneled into planning. Triggers on - 'scout wave', 'wiki scout', 'research these links', 'update the wiki', or /hk-scout-wiki.
将具体执行委派给 pi coding agent + ZenMux 便宜执行模型（默认强档 deepseek/deepseek-v4-pro：1M 上下文、推理型；廉价快档 inclusionai/ling-3.0-flash：256K、非思考、更省），Claude 负责任务-模型匹配判断、任务书编写、驱动（一次性 pi -p 或 tmux 交互长程）、硬超时重试、独立验收与失败裁决。含双模型选型、3D 素材来源、Spec-Driven、强类型可验证节点、推理档位开关等最佳实践。触发词：pi 委派、把执行外包给便宜模型、deepseek、deepseek-v4-pro、v4 pro 执行、ling、ling-3.0-flash、ling flash 执行、pi sub agent
A relentless interview to sharpen a plan or design — one question at a time until every branch of the decision tree is resolved.
Automatic history search — checks past sessions before web research, planning, and debugging, siblings deepen coverage
Automatic tool discovery across 17 sources — hooks search before planning and after errors, siblings avoid redundant searches
Load when Codex 即将要求用户在 2-3 个互斥方案中做真实决定、准备询问阻塞性澄清问题或需要确认高影响取舍；将选择转换为宿主原生可点击 UI，并在工具不可用时诚实文本回退。不要用于低风险实现细节、只有一个合理答案的问题、普通信息采集或不需要用户决策的完成汇报。
Load when a task needs to collaboratively draft, restructure, or reader-test docs, PRDs, RFCs, proposals, specs, or decision records; skip implementation itself.
Load when agent pauses to report relatively complex information needing Chinese-first clear complex communication, alignment, multi-option choice, status/incident, long-task fact ledgers, implementation plans, reviews, maps, explainers, evidence, risks, validation, handoff; choose plain text/Markdown/visual Markdown/HTML by decision cost; do not load merely because answer is long; skip trivial chat/bundled apps.
Load when beginning every repository mutation task to run one dependency-layered batch interview; a fully aligned task takes the zero-question path, while durable documentation work uses grill-with-docs to reuse the same decision graph.
Load when a task needs to archive a completed OpenSpec change after implementation and spec sync decisions are resolved.
Load when accepted product intent must become a concise, implementation-ready local capability specification with explicit constraints, non-goals, test seams, and unresolved decisions.
>- Run an honest market-fit and viability audit of any project or idea and produce a decision doc, not code. Use when someone asks "is this viable as a business", "audit the market fit", "make the case and compare to competitors", "should I apply to YC or bootstrap", or wants to validate a project, product, or market before investing more time. Gathers verifiable traction data first, researches the competitive field, gets an independent cold read, writes a doc with pre-committed pass/middle/kill criteria and a concrete week-1 assignment, then hardens it with an adversarial review loop.
Find contacts stalled at funnel stages (abandoned cart, signed-up-but-no-trial, trial-but-no-convert), draft stage-appropriate re-engagement emails, and schedule sends via your ESP — with dedupe so weekly reruns never double-send
Proactively decompose and coordinate substantial development, research, analysis, planning, document, data, and content work with the smallest useful parallel set of custom subagents. Use when independent execution, parallelism, context isolation, or fresh review can improve speed or quality. Keep unresolved decisions and final acceptance in the main thread. Do not use for casual or simple tasks.
Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.
Multi-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across OpenAI Codex, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development workflows.
Translate PRD intent, roadmap asks, or product discussions into an implementation-ready capability plan that exposes constraints, invariants, interfaces, and unresolved decisions before multi-service work starts. Use when the user needs an ecc-native PRD-to-SRS lane instead of vague planning prose.
Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.
コーディングセッション中にアーキテクチャ決定を構造化ADRとして記録し、自動的に決定の瞬間を検出し、コンテキスト、検討された代替案、根拠を記録します。今後の開発者がコードベースの形成理由を理解するためのADRログを維持します。
Multi-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across OpenAI Codex, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development workflows.
在庫管理、需要予測、補充戦略、およびサプライチェーン最適化。 Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.
Jira チケットの取得、要件分析、チケットステータスの更新、コメントの追加、またはイシューのトランジションを行う際に使用します。MCP または直接 REST 呼び出しによる Jira API パターンを提供します。
Translate PRD intent, roadmap asks, or product discussions into an implementation-ready capability plan that exposes constraints, invariants, interfaces, and unresolved decisions before multi-service work starts. Use when the user needs an ecc-native PRD-to-SRS lane instead of vague planning prose.
Capture architectural decisions made during OpenAI Codex sessions as structured ADRs. Auto-detects decision moments, records context, alternatives considered, and rationale. Maintains an ADR log so future developers understand why the codebase is shaped the way it is.
Convene a four-voice council for ambiguous decisions, tradeoffs, and go/no-go calls. Use when multiple valid paths exist and you need structured disagreement before choosing.
Security checklist for Solidity AMM contracts, liquidity pools, and swap flows. Covers reentrancy, CEI ordering, donation or inflation attacks, oracle manipulation, slippage, admin controls, and integer math.
Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows.
EMR/EHR development patterns for healthcare applications. Clinical safety, encounter workflows, prescription generation, clinical decision support integration, and accessibility-first UI for medical data entry.
> Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.
Use this skill when retrieving Jira tickets, analyzing requirements, updating ticket status, adding comments, or transitioning issues. Provides Jira API patterns via MCP or direct REST calls.
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
> Codified expertise for returns authorization, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims management. Informed by returns operations managers with 15+ years experience. Includes grading frameworks, disposition economics, fraud pattern recognition, and vendor recovery processes. Use when handling product returns, reverse logistics, refund decisions, return fraud detection, or warranty claims.
建议在逻辑间隔处手动压缩上下文，以在任务阶段中保留上下文，而非任意的自动压缩。