Productivity Skills

Automate 7-phase feature development with specialized agents (code-explorer, code-architect, code-reviewer). Use for multi-file features, architectural decisions, or encountering ambiguous requirements, integration patterns, design approach errors.

| messages, files, schedule, check-ins, timeline, recordings, templates, webhooks, subscriptions, lineup, chat, pings, gauges, assignments, notifications, and accounts. Use for ANY Basecamp question or action.

劳动争议诉讼专业流程编排 Skill。整合17个子Skill：复用14个现有通用Skill + 3个劳动争议专属Skill（劳动争议仲裁程序管理、劳动关系认定与经济补偿计算、劳动争议证据体系）。用于判断劳动争议阶段（协商→调解→仲裁→一审→二审，L0-L15/LM系列）、识别W/E代理角色、区分劳动者代理、用人单位争议代理和用人单位日常劳动合规任务、推荐下一步子Skill、提示关键节点确认和期限；事项建档、路径、文件读取、来源披露、OCR 校正和缺口归档由「法律工作总控」统一处理。

legal 文件夹通用入口 Skill。用于法律咨询、案件办理、合同、产品法务、监管合规、诉讼、刑辩、劳动争议、破产、合规、文书、检索等任务的语义路由、案件隔离、来源披露、文件读取复查、法规/Wiki 校验、OCR 校正、缺口提示和合同偏好学习。用户提出任何法律工作请求、客户编号、案件材料处理、法律文书生成或需要自动匹配 legal 子 Skill 时触发。

中国境内重点监管动态监测与客户合规影响评估 Skill。用于监管动态、新规更新、政策变化、行业监管、法规政策分级、客户合规缺口核查、政策制度修改建议、整改清单、监管合规简报等任务。适用于用户要求监测某行业、某客户、某产品或某主题的法律法规、监管政策、部门规章、规范性文件、征求意见稿、执法动态，并判断新规要求、客户现有合规文件缺口和整改方案。

当用户要写出高质量 Goal Prompt 和交付后继续进化用的 Loop Prompt，把模糊、战略性、多步骤、证据不足、持续/自动化或容易跑偏的请求整理成可执行、可验证、可暂停且带时间参数的目标契约时使用。适用于写 goal、优化任务提示词、明确 done/success criteria、deep research 后定战略、大改前 inventory、修复跑偏计划、识别可委托的重复 workflow、为 Codex 或 Claude Code 准备执行任务；默认只生成提示词，不执行 goal 或 loop，也不创建自动化。

Operate TikTok Shop research and planning with KSS MCP across product discovery, shop analysis, viral commerce videos, creator matching, caption extraction, pagination, sorting, and evidence-based action plans. Use when a user asks to research TikTok Shop products, shops, videos, creators, subtitles, competitors, or a complete commerce operations workflow.

Guides agents through the 3-step experiment creation flow: defining the hypothesis, configuring rollout, and setting up analytics. Delegates rollout decisions to configuring-experiment-rollout and metric setup to configuring-experiment-analytics.\nTRIGGER when: user asks to create a new experiment or A/B test, OR when you are about to call experiment-create.\nDO NOT TRIGGER when: user is updating an existing experiment, managing lifecycle, or only browsing experiments.

> How to explore and make sense of PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to understand what scouts they have, how each one is behaving, and whether the fleet is actually working. Covers surveying the fleet and its schedules, reading recent scout runs and drilling into a single run's reasoning, inspecting the durable scratchpad memory the fleet has built up, tracing a run to the findings it emitted, and assessing a scout's health and performance over time (cadence, success rate, emit rate, signal-to-noise). Read-only and exploratory — to write or tune a scout, use `authoring-signals-scouts` instead. Trigger on "what are my scouts doing", "how is my <x> scout performing", "show me recent scout runs", "why did this scout find/emit nothing", "what has the fleet learned", "explore scout run <id>", "is my scout working".

Guides experiment state transitions: launching, pausing, resuming, ending, shipping variants, archiving, resetting, duplicating, and copying to another project. Covers preconditions, implications for variant assignment and analysis, and the decision framework for when to use each action.\nTRIGGER when: user asks to launch, pause, resume, end, ship, archive, reset, duplicate, or copy an experiment to another project.\nDO NOT TRIGGER when: user is creating an experiment (use creating-experiments), configuring rollout (use configuring-experiment-rollout), or setting up metrics (use configuring-experiment-analytics).

Plan a user interview topic in PostHog — pick who to target (cohort, emails, or PostHog distinct IDs), draft what to ask about, and prepare the voice-agent context plus a question list. Use when the user asks to "talk to users", "check how users feel about X", "interview some customers", "set up a user interview", "run a user-research call", "find users to ask about Y", or otherwise wants qualitative feedback through a conversation. Walks the user through targeting (cohorts-list, persons-list, or accepting emails / distinct IDs directly), captures the topic, and prompts for agent context and questions before calling user-interview-topics-create. Cohort targeting is resolved to explicit emails/distinct_ids at create time — topics snapshot their audience and do not re-evaluate cohort membership later. Do NOT trigger when the user is uploading a recorded interview audio file (that''s the separate UserInterview/transcript flow) or only browsing existing topics with user-interview-topics-list.

> General Signals scout for PostHog projects. Cross-product explorer that scans a team's project and emits findings into the Signals inbox. Sibling signals-scout-* specialists each watch a single product surface in depth; this scout looks for cross-product correlations and explores the surfaces no specialist covers. Each scout runs on its own schedule (default hourly), so general fires independently of the specialists over time.

>- Best practices for agents managing PostHog skills via the MCP `llma-skill-*` tools — how to discover, read, create, update, and refactor skills efficiently, especially large skills with many bundled files. Use whenever you are about to call any `llma-skill-*` tool, asked to author or edit a shared skill, or troubleshoot why a skill write was rejected. Pairs with `skills-store` (which covers the raw tool surface) by adding the decision-tree, efficiency, and pitfall guidance.

Generates professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings — biomarker-stratified patient cohort analyses with outcomes and evidence-based treatment recommendation reports with decision algorithms, supporting GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance, output as publication-ready LaTeX/PDF. Use when building a CDS document, cohort analysis, or treatment recommendation report for drug development, clinical research, or evidence synthesis, or when GRADE grading, hazard ratios, survival/waterfall plots, or biomarker stratification are requested. Part of the AlterLab Academic Skills suite.

Access ClinPGx pharmacogenomics data (the successor to PharmGKB) to query gene-drug interactions, CPIC/DPWG dosing guidelines, drug labels, and pharmacogene records. Use when interpreting pharmacogenes (CYP2D6, CYP2C19, TPMT, DPYD, SLCO1B1), looking up genotype-guided drug dosing, checking PGx drug-safety associations (e.g. HLA-B*57:01 and abacavir), or supporting precision medicine and clinical pharmacogenomics decisions. For star-allele definitions/frequencies see PharmVar; for germline/somatic variant pathogenicity see alterlab-clinvar. Part of the AlterLab Academic Skills suite.

Creative research ideation and exploration for open-ended brainstorming, surfacing interdisciplinary connections, challenging assumptions, and identifying research gaps. Use when starting early-stage research planning with no specific observations yet — for open-ended brainstorming sessions, exploring cross-disciplinary connections, or finding gaps. For formulating testable hypotheses from observations or data use hypothesis-gen; for grading evidence or spotting design flaws use scientific-thinking. Part of the AlterLab Academic Skills suite.

| Schedules and performs root-level Gradle operations for inspecting and operating existing builds. ## Positive Triggers (when to activate) ## Negative Triggers (when NOT to activate)

Review Kafka producer and consumer performance configurations in both the live cluster (via Lenses MCP) and the codebase. Flags un-tuned defaults, anti-patterns and missing best practices. Use when user says "review Kafka performance", "check producer configs", "tune Kafka settings" or asks about throughput, batching or compression. Do NOT use for cluster sizing or capacity planning.

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。 '