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github/gtm-board-and-investor-communication

董事会会议准备、投资者更新及高管沟通。适用于准备董事会演示文稿、撰写投资者更新报告、处理与董事会的坏消息、组织季度业务回顾(QBR)或建立董事会级别的指标纪律。包含“三件事”叙事模型、四级指标层次结构以及防止董事会意外的预简报模式。

gtm-board-and-investor-communication 是什么?

该技能专注于GTM(市场进入)场景下的董事会沟通与投资者关系管理。它提供了一套结构化的方法论,帮助初创企业和高管团队高效准备董事会会议、撰写投资者更新信函,并妥善处理与董事会之间的负面信息传递。技能的核心组件包括“三件事”叙事模型(聚焦关键优先事项)、四级指标层次结构(从战略指标到运营指标的分层体系)以及“预简报”模式(提前同步信息以消除董事会意外)。该技能适用于Claude等AI代理,能够根据用户输入的上下文(如财务数据、里程碑进展或危机事件)自动生成逻辑清晰、语气得体的董事会汇报草稿或投资者沟通文本。支持的平台包括Claude Code、Cursor等主流AI开发环境。通过系统化的框架,该技能显著提升了高管沟通的专业性和效率,特别适合早期到成长期公司的CEO、CFO及投资者关系负责人使用。

兼容平台~Claude Code~Codex CLI~Cursor
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gtm-board-and-investor-communication 是做什么的?

董事会会议准备、投资者更新及高管沟通。适用于准备董事会演示文稿、撰写投资者更新报告、处理与董事会的坏消息、组织季度业务回顾(QBR)或建立董事会级别的指标纪律。包含“三件事”叙事模型、四级指标层次结构以及防止董事会意外的预简报模式。

简介

该技能帮助GTM团队和高管有效准备董事会会议和投资者沟通。它提供结构化的框架以确保清晰、专业且无意外的沟通。

核心组件

1. “三件事”叙事模型

一种聚焦关键优先事项的沟通方法,确保每次会议或更新都围绕三个核心主题展开。

2. 四级指标层次结构

  • 战略指标:与公司长期目标相关的高层级指标。
  • 运营指标:日常关键绩效指标(KPI)。
  • 财务指标:收入、利润率和现金流。
  • 客户指标:客户获取成本(CAC)、生命周期价值(LTV)和留存率。

3. 预简报模式

一种主动沟通策略,在正式会议前提前分享关键信息(尤其是坏消息),以培养信任并避免意外。

使用场景

  • 准备董事会演示文稿和演讲要点。
  • 编写季度投资者更新报告。
  • 制定季度业务回顾(QBR)议程。
  • 处理负面数据或挑战,同时维护董事会信任。
  • 建立一致的董事会指标报告流程。

如何使用

  1. 提供上下文(如会议类型、最新数据、当前挑战)。
  2. AI将根据所选框架生成结构化的叙述或草稿。
  3. 根据您的声音和品牌风格进行审阅和定制。

最佳实践

  • 在正式会议前使用预简报模式发送敏感信息。
  • 优先选择“三件事”模型以保持叙述简洁。
  • 确保四级指标层次结构在每份材料中保持一致性。
  • 将坏消息与解决方案或缓解计划搭配呈现。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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