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github/gtm-enterprise-onboarding

Cadre en quatre phases pour l'intégration des clients Enterprise, du contrat à la réalisation de la valeur. À utiliser lors de l'intégration de nouveaux clients Enterprise, pour prévenir le churn pendant l'intégration ou résoudre le gouffre d'adoption qui tue les deals après le go-live. Inclut le modèle de disparition de la Semaine 4.

Qu'est-ce que gtm-enterprise-onboarding ?

Ce skill, nommé 'github/gtm-enterprise-onboarding', fournit un cadre structuré en quatre phases pour l'intégration des clients Enterprise, allant de la signature du contrat à la réalisation concrète de la valeur. Il est particulièrement utile pour les responsables de l'adoption client (Customer Success Managers), les chefs de projet d'intégration et les équipes GTM (Go-To-Market) qui doivent mettre en œuvre de nouveaux clients Enterprise, prévenir le churn pendant la phase d'intégration, ou résoudre le problème du 'gouffre d'adoption' qui fait échouer les déploiements après le go-live. Le skill détaille des patterns spécifiques comme le 'Week 4 ghosting pattern' (modèle de disparition de la semaine 4) et propose des étapes concrètes pour chaque phase. Il est conçu pour être utilisé avec des agents AI comme Claude Code, en fournissant des instructions claires pour guider l'intégration client de manière systématique et efficace.

Compatible avec~Claude Code~Codex CLI~Cursor
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Documentation

Que fait gtm-enterprise-onboarding ?

Cadre en quatre phases pour l'intégration des clients Enterprise, du contrat à la réalisation de la valeur. À utiliser lors de l'intégration de nouveaux clients Enterprise, pour prévenir le churn pendant l'intégration ou résoudre le gouffre d'adoption qui tue les deals après le go-live. Inclut le modèle de disparition de la Semaine 4.

Aperçu

Ce skill fournit un processus structuré pour l'intégration des clients Enterprise. Il aborde les défis courants tels que le manque d'engagement, les objectifs flous et la lente adoption post-lancement.

Phases

  1. Phase 1 : Fondation (Semaines 1-2)

    • Réunion de kickoff avec les parties prenantes clés.
    • Définir les critères de succès et les indicateurs clés de performance (KPI).
    • Mettre en place les canaux de communication et les outils.
    • Attribuer les rôles et responsabilités.

  2. Phase 2 : Construction & Configuration (Semaines 3-6)

    • Intégration technique et configuration.
    • Transfert de connaissances et sessions de formation.
    • Élaboration conjointe d'un plan d'adoption.
    • Identifier et atténuer les risques.

  3. Phase 3 : Go-Live & Activation (Semaines 7-8)

    • Exécution du plan de go-live.
    • Validation de l'activation des utilisateurs.
    • Suivi des premiers indicateurs d'adoption.
    • Résolution rapide des blocages.

  4. Phase 4 : Réalisation de la valeur & Expansion (Semaines 9-12)

    • Révision des objectifs de valeur.
    • Planification de l'expansion.
    • Boucle de feedback pour l'amélioration continue.
    • Préparation de la transition vers le succès client à long terme.

Modèle de disparition de la Semaine 4

Un pattern courant où l'engagement du baisse pendant la Semaine 4. Pour contrer cela :

  • Planifier des points de contact proactifs.
  • Partager des wins rapides et des retours sur investissement (ROI).
  • Impliquer un sponsor exécutif si nécessaire.
  • Réévaluer le plan d'adoption.

Bonnes pratiques

  • Personnaliser l'approche pour chaque client.
  • Documenter tous les engagements et décisions.
  • Célébrer les jalons et les réussites.
  • Itérer en fonction des retours.

Dépendances

  • Aucune dépendance externe. Nécessite un accès à la plateforme et aux outils de communication.

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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