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Autopsie de Désabonnement

Analyse post-mortem en cas de perte d'un client. Utilise l'historique client, les données d'engagement, les tickets de support, les journaux d'utilisation et les retours de sortie pour produire une autopsie complète du désabonnement avec classification de la cause racine, chronologie du déclin et mesures préventives.

Qu'est-ce que Autopsie de Désabonnement ?

Ce skill, 'Autopsie de Désabonnement', permet à un agent IA (comme Claude Code ou Cursor) de réaliser une analyse médico-légale rigoureuse lorsqu'un client se désabonne. Il guide l'agent à travers sept étapes structurées : collecte des données (historique client, engagement, tickets, logs, feedback), organisation chronologique, analyse en six phases (référence, chronologie du déclin, classification des causes racines, signes avant-coureurs manqués, analyse contrefactuelle, leçons apprises), classification selon une taxonomie dédiée, rédaction d'un rapport structuré selon un template, remise en question des conclusions (pour garantir l'objectivité), et finalisation du fichier `churn-autopsy.md`. Le skill s'appuie sur des fichiers de référence (inputs, framework d'analyse, taxonomie, template de sortie, standards) pour assurer une enquête complète, impartiale et exploitable. Il transforme ainsi une perte client en une opportunité d'apprentissage organisationnel, en identifiant les causes profondes, les signaux faibles, et les actions correctives.

Compatible avec✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/OneWave-AI/claude-skills/tree/HEAD/churn-autopsy

Demander à votre IA préférée

Ouvre une nouvelle conversation avec cette compétence d'agent déjà préchargée.

Documentation

Que fait Autopsie de Désabonnement ?

Disséquez le départ d'un client avec une rigueur médico-légale et produisez churn-autopsy.md : un enregistrement historique et un playbook de rétention qui transforme une perte en apprentissage organisationnel.

Contenu

  • references/inputs.md -- données obligatoires et facultatives à rassembler avant l'analyse
  • references/analysis-framework.md -- la méthode médico-légale en six phases
  • references/root-cause-taxonomy.md -- catégories et sous-catégories de causes racines
  • references/output-template.md -- structure complète du rapport churn-autopsy.md
  • references/standards.md -- règles d'objectivité, de rigueur, de sensibilité, de données incomplètes et de fonctionnement

Workflow

  1. Collecter. Rassemblez toutes les entrées disponibles selon references/inputs.md. Lisez directement les fichiers fournis ; interrogez les outils CRM et d'analyse MCP connectés pour les données de compte, d'utilisation et de support. Notez toute lacune.
  2. Organiser. Construisez la chronologie complète, du premier contact jusqu'à l'annulation, avant de tenter l'analyse.
  3. Analyser. Appliquez systématiquement les six phases décrites dans references/analysis-framework.md, sans sauter d'étape : référence, chronologie du déclin, classification des causes racines, signes avant-coureurs manqués, analyse contrefactuelle, leçons apprises.
  4. Classifier. Attribuez une cause racine principale et toute cause contributive en utilisant references/root-cause-taxonomy.md.
  5. Rédiger. Écrivez le rapport selon la structure définie dans references/output-template.md.
  6. Contester. Examinez chaque conclusion par rapport à references/standards.md ; essayez de réfuter chaque constat avant de le conserver.
  7. Finaliser. Produisez churn-autopsy.md avec toutes les sections complètes dans le répertoire de travail actuel ou un emplacement spécifié par l'utilisateur.

Individual skills in this repo

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

OneWave-AI/claude-skills

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OneWave-AI/claude-skills

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OneWave-AI/claude-skills

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OneWave-AI/claude-skills

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OneWave-AI/claude-skills

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OneWave-AI/claude-skills

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OneWave-AI/claude-skills

Turn a website URL, product docs, or a founder's rambling description into a tight 60-90 second explainer video built in HyperFrames -- problem/solution narrative, animated product walkthrough scenes, captions, and voiceover, ready for the homepage hero, sales emails, and onboarding.

OneWave-AI/claude-skills

Build promo videos for local businesses in HyperFrames -- the new-menu drop, the seasonal offer, the event announcement, the "we're hiring" post, the Google Business Profile clip. Restaurant, salon, gym, trades: 15-30 seconds, brand-clean, captioned, sized for Instagram, Facebook, and the profile.

OneWave-AI/claude-skills

Build a personalized product-demo or sales-walkthrough video in HyperFrames for a specific prospect or account — narrated screen-by-screen, branded to them, ending in a clear next step. Turns a generic demo into a "this was made for me" asset for outbound, follow-up, or deal acceleration. Use when you want to send a tailored video instead of booking another live call.

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Turn real customer reviews, quotes, and case-study results into polished social-proof videos built in HyperFrames -- single-testimonial spotlights, review-wall montages, and stat-driven proof reels with captions and voiceover, cut for vertical, square, and wide. The highest-converting content most businesses never make.

OneWave-AI/claude-skills

Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.

OneWave-AI/claude-skills

Build and run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold.

OneWave-AI/claude-skills

Wire a System One model (Jev, or an open reproduction like Von) into a product feature — routing, guardrails, scoring, classification. Use when replacing an LLM call that returns a label rather than prose, when adding a typed decision to an agent loop, or when deciding between the hosted Jev API and a local open model. Covers question design, the eval-set-first workflow, threshold calibration, confidence gates, and the traps measured on real data.

OneWave-AI/claude-skills

Optimize landing pages for conversions, performance, and SEO. Use when improving landing pages, increasing conversions, or optimizing page performance.

OneWave-AI/claude-skills

TAM/SAM/SOM calculator with deep market research. Produces comprehensive market-sizing.md with top-down and bottom-up estimates, methodology, data sources, assumptions, sensitivity ranges, growth projections, competitive landscape, and Mermaid visualizations. Use when user needs market size estimates, addressable market analysis, go-to-market sizing, investor-ready market analysis, or business plan market validation.

OneWave-AI/claude-skills

Generate unique personalized first lines for hundreds of prospects using company news, LinkedIn activity, and mutual connections. Saves 10+ hours of manual research per campaign. Use when you need personalized outreach at volume.

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OneWave-AI/claude-skills

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