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Capturer le contexte local de l'écran et de l'audio pour que le…

Utilisez Screenpipe lorsqu'un agent a besoin d'une mémoire privée et locale de ce que vous avez vu ou entendu sur votre ordinateur, y compris le texte d'écran consultable, le contexte des applications et les transcriptions, au lieu de se fier à une couche mémoire basée uniquement sur le chat.

Qu'est-ce que Capturer le contexte local de l'écran et de l'audio pour que le… ?

Cette compétence exploite Screenpipe pour offrir aux agents d'IA une mémoire locale et privée de votre activité sur l'ordinateur. Contrairement à une mémoire de chat standard, elle capture et indexe le texte affiché à l'écran, le contexte des applications et les transcriptions audio, permettant aux agents de rechercher et de référencer des événements passés. Elle est idéale pour les agents fonctionnant sur des clients compatibles MCP comme Claude Code, Cursor ou Codex, et garantit que les données restent sur votre machine, renforçant ainsi la confidentialité. La capture est événementielle, ne traitant que les changements, ce qui minimise la charge CPU (5–10 % sur du matériel moderne). Les prérequis incluent l'application de bureau Screenpipe, les permissions d'écran et audio, et environ 5–10 Go d'espace disque par mois. L'installation suit le dépôt GitHub officiel, et la documentation complète est disponible sur docs.screenpi.pe.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/capture-local-screen-and-audio-context-so-agents-can-search-what-happened-on-your-device
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Documentation

Capturer le contexte local de l'écran et de l'audio pour que les agents puissent rechercher ce qui s'est passé sur votre appareil

Utilisez Screenpipe lorsqu'un agent a besoin d'une mémoire privée et locale de ce que vous avez vu ou entendu sur votre ordinateur, y compris le texte d'écran consultable, le contexte des applications et les transcriptions, au lieu de se fier à une couche mémoire basée uniquement sur le chat.

Prérequis

Application de bureau Screenpipe ou construction à partir des sources, permissions locales d'écran et audio, espace de stockage local suffisant, et éventuellement un client agent compatible MCP

Installation

Utilisez le chemin d'installation en amont ou la configuration qui correspond à votre environnement :

  • Assurez-vous de comprendre que la branche principale évolue rapidement et peut casser des choses. Si vous recherchez une version stable, consultez les versions de l'application https://github.com/screenpipe/screenpipe/releases et utilisez le commit git correspondant...

Notes d'utilisation de base ou de démarrage :

  • Configuration minimale recommandée : 8 Go de RAM. ~5–10 Go d'espace disque par mois. L'utilisation du CPU est typiquement de 5–10 % sur du matériel moderne grâce à la capture événementielle.

  • L'utilisation typique du CPU est de 5–10 % sur du matériel moderne. La capture événementielle ne traite les images que lorsque quelque chose change, et l'extraction de l'arbre d'accessibilité est beaucoup plus légère que l'OCR.

  • Source : https://github.com/screenpipe/screenpipe

  • Extrait des docs en amont : https://raw.githubusercontent.com/screenpipe/screenpipe/HEAD/README.md

Documentation

Source

Individual skills in this repo

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

Academic Paper Citation Network Mapper

Builds citation networks from Semantic Scholar API and CrossRef DOI metadata. Visualizes paper influence graphs using NetworkX, identifies seminal works, and tracks research lineage across fields.

ACF Custom Fields REST Exposer

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ACF Extended for Advanced Custom Fields Workflows

An ASE skill built around ACF Extended, the WordPress enhancement suite for Advanced Custom Fields that adds field types, admin improvements, front-end forms, options pages, and developer tooling. It is a practical fit for agents working inside complex WordPress content models and custom field workflows.

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ActivityWatch Cross-Platform Automated Time Tracker and Producti

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act Local GitHub Actions Runner

act is an open-source CLI tool that runs GitHub Actions workflows locally using Docker, enabling fast feedback on workflow changes without pushing to GitHub. It is a standard tool for local Actions development and testing.

Add a browser review checkpoint before risky agent actions with

Use AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.

Add Claude design-system and accessibility workflows with UX/UI

Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.

Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

Add IDE-style semantic code navigation and symbol-safe refactors

Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

Add managed long-term memory to LangGraph agents with LangMem

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

Add multimodal lifelong memory to MCP and Python agents with Sim

Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

Address GitHub PR review comments from the current branch with g

Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

Calls Adobe Firefly

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