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github/gtm-enterprise-account-planning

엔터프라이즈 딜을 위한 전략적 계정 계획 및 실행. 복잡한 영업 사이클 계획, 다중 이해 관계자 관리, MEDDICC 자격 평가 적용, 딜 건전성 추적, 상호 실행 계획 수립 시 사용합니다. '오래된 MAP은 죽은 딜과 같다' 패턴을 포함합니다.

gtm-enterprise-account-planning란 무엇인가요?

이 스킬은 엔터프라이즈 영업 담당자와 AI 에이전트가 복잡한 거래를 체계적으로 관리하고 성사시킬 수 있도록 설계된 전략적 계정 계획 도구입니다. MEDDICC 자격 평가 프레임워크를 적용하여 딜의 건전성을 평가하고, 다중 이해 관계자 맵을 작성하며, 상호 실행 계획(MAP)을 수립하고 추적할 수 있습니다. 특히 '오래된 MAP은 죽은 딜과 같다' 패턴을 통해 MAP이 업데이트되지 않으면 거래가 위험하다는 신호를 감지하여 적시에 개입할 수 있도록 돕습니다. 이 스킬은 Claude Code와 같은 AI 에이전트에서 사용할 수 있으며, 영업 데이터를 분석하고, 딜 리스크를 평가하며, 자동으로 실행 계획을 생성하여 영업 생산성을 향상시킵니다.

지원 대상~Claude Code~Codex CLI~Cursor
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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

gtm-enterprise-account-planning은(는) 무엇을 하나요?

엔터프라이즈 딜을 위한 전략적 계정 계획 및 실행. 복잡한 영업 사이클 계획, 다중 이해 관계자 관리, MEDDICC 자격 평가 적용, 딜 건전성 추적, 상호 실행 계획 수립 시 사용합니다. '오래된 MAP은 죽은 딜과 같다' 패턴을 포함합니다.

개요

이 스킬은 AI 에이전트(예: Claude Code)가 엔터프라이즈 영업 프로세스에서 전략적 계정 계획을 수립하고 실행할 수 있도록 지원합니다. 주요 기능은 다음과 같습니다:

  • MEDDICC 자격 평가: Metrics(지표), Economic Buyer(경제적 구매자), Decision Criteria(의사 결정 기준), Decision Process(의사 결정 프로세스), Identify Pain(식별된 고통), Champion(챔피언), Competition(경쟁사)를 기반으로 딜을 평가합니다.
  • 이해 관계자 매핑: 다양한 이해 관계자의 역할, 영향력, 입장을 시각화합니다.
  • 상호 실행 계획(MAP): 고객과 공동으로 실행 계획을 수립하고 진행 상황을 추적합니다.
  • 딜 건전성 추적: 정기적으로 딜 상태를 평가하고 리스크를 식별합니다.
  • 오래된 MAP 감지: 마지막 업데이트 이후 일정 시간이 지난 MAP을 식별하여 경고합니다.

사용 방법

  1. plan-account 명령어로 새 계정 계획을 시작합니다.
  2. qualify-deal 명령어로 MEDDICC 기준에 따라 딜을 평가합니다.
  3. map-stakeholders 명령어로 이해 관계자 정보를 입력하고 맵을 생성합니다.
  4. create-map 명령어로 상호 실행 계획을 수립합니다.
  5. check-deal-health 명령어로 딜 건전성을 확인하고 '오래된 MAP' 패턴을 감지합니다.

예시

# 새 계정 계획 시작
plan-account --company=AcmeCorp --deal-value=500000

# MEDDICC 자격 평가
qualify-deal --deal-id=acme-2025-q1 --metrics=ROI,CostSavings

# 이해 관계자 추가
map-stakeholders --deal-id=acme-2025-q1 --name="Jane Doe" --role="CTO" --influence=High

# 상호 실행 계획 생성
create-map --deal-id=acme-2025-q1 --tasks="Product Demo,POC,Contract Review"

# 딜 건전성 체크
check-deal-health --deal-id=acme-2025-q1

구성

변수설명기본값
MAP_STALE_DAYSMAP이 오래된 것으로 간주되는 일수14
DEAL_HEALTH_THRESHOLD딜 건전성 점수 임계값(0-100)60
CRM_API_KEYCRM 시스템 접근을 위한 API 키-

MEDDICC 자격 평가 상세

  • Metrics: 고객이 측정하는 주요 지표(ROI, 비용 절감, 생산성 등)
  • Economic Buyer: 최종 의사 결정권자 식별
  • Decision Criteria: 고객의 의사 결정 기준 이해
  • Decision Process: 의사 결정 프로세스 단계 파악
  • Identify Pain: 고객의 핵심 문제점 식별
  • Champion: 내부 지지자 발굴 및 육성
  • Competition: 경쟁사 분석 및 차별화 전략 수립

라이선스

이 프로젝트는 MIT 라이선스에 따라 사용이 허가됩니다.

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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