Productivité
84
markit
Converts local files, URLs, GitHub resources, and other supported content into Markdown through a CLI or TypeScript SDK.
Productivité
agent-recall
Provides persistent, local memory for AI agents through MCP tools for session context, recall, corrections, decision tracking, cross-project insights, and journal consolidation.
★ 371
GitHub Stars
94
Score de qualité
1
Fichiers
About this Skill
Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.
Cas d’usage
- Load context at session start
- Save session summaries and insights
- Recall decisions and lessons
- Track corrections and decision quality
- Share relevant insights across projects
Fonctionnalités
- Five default MCP tools with an optional 18-tool full mode
- Local Markdown storage compatible with Obsidian
- Correction tracking with predictive watch_for warnings
- Cross-project insight matching and salience-based palace rooms
- Optional Supabase pgvector semantic search
Prérequis
- Node.js
- npx
- An MCP-compatible agent
Agents compatibles
Skill files
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