功能描述

Integração automática de memória persistente no OpenClaw, salvando conversas no Convex e buscando contexto híbrido com Convex e Obsidian para respostas relev...

使用说明 (SKILL.md)

Convex + Obsidian - Integração de Memória Persistente

Name: Convex Obsidian
Author: andreytsushima

🎯 Objetivo

Memória persistente automática para o OpenClaw:

Convex (Hot): Salva cada conversa automaticamente
Obsidian (Deep): Conhecimento histórico consolidado
Busca Híbrida: Contexto de memória injetado quando relevante

✅ Status: OPERACIONAL

Arquitetura

Usuário fala → OpenClaw processa
     ↓
[Auto-save] → Convex (memória recente)
     ↓
Se detectar referência ao passado:
     ↓
[Busca Híbrida] → Convex + Obsidian
     ↓
[Contexto injetado] → "Lembre-se que ontem..."

Componentes

1. Backend Convex

URL: https://energized-goshawk-977.convex.cloud
Schema: convex/schema.ts
Funções: convex/memory.ts

2. Scripts Python

Script	Função
`memory.py`	CLI completo (save/search/stats)
`search.py`	Busca híbrida (Convex + local)
`hook.py`	Integração automática (chamado pelo OpenClaw)

3. CLI Tools

# Busca híbrida
./search.sh "nvidia" -n 5

# Salvar manualmente
./memory.sh save "Conteúdo" --session main-2026-03-27

# Estatísticas
./memory.sh stats

Integração Automática no OpenClaw

Opção 1: Hook pós-mensagem (Recomendado)

Editar ~/.openclaw/openclaw.json para adicionar um hook:

{
  "skills": {
    "entries": {
      "convex-obsidian": {
        "autoSave": true,
        "autoContext": true,
        "deploymentUrl": "https://energized-goshawk-977.convex.cloud"
      }
    }
  }
}

Comportamento:

Cada mensagem salva automaticamente no Convex
Se usuário mencionar "ontem", "antes", "lembra" → busca contexto
Contexto injetado no início da resposta

Opção 2: Comandos manuais

/memory search \x3Cquery>     # Busca híbrida
/memory save \x3Ctexto>       # Salvar no Convex
/memory context            # Ver contexto atual

Opção 3: Via ferramenta memory_search

O OpenClaw já usa memory_search para buscar em arquivos locais. Para incluir Convex, usar:

# skills/convex-obsidian/search.py
./search.sh "query" --json

Uso Atual (Testado)

cd /home/andrey/.openclaw/workspace/skills/convex-obsidian

# Buscar em ambas as fontes
./search.sh "amw" -n 5
./search.sh "nvidia configuracao" --json

# Salvar memória
./memory.sh save "Cliente pediu orçamento" \
  --session main-2026-03-27 \
  --tags cliente orcamento \
  --importance 8

# Salvar no Obsidian
./memory.sh save-obsidian "Resumo reunião..." \
  --title "Reunião Cliente XYZ" \
  --folder "05-AMW/Reuniões"

Configuração de Variáveis

# ~/.openclaw/.env ou exportar
export CONVEX_DEPLOYMENT_URL="https://energized-goshawk-977.convex.cloud"
export VAULT_PATH="/home/andrey/Vault"

Deploy/Redeploy do Convex

Se precisar atualizar o backend:

cd /home/andrey/.openclaw/workspace/skills/convex-obsidian

# Usar a chave preview
export CONVEX_DEPLOY_KEY="109326a5533f411792dae76dc8ae3f6f"
npx convex@latest deploy --preview-create openclaw-memory

# Ou com a chave completa
export CONVEX_DEPLOY_KEY="preview:andrey-tsushima:openclaw|eyJ2MiI6IjEwOTMyNmE1NTMzZjQxMTc5MmRhZTc2ZGM4YWUzZjZmIn0="
npx convex@latest deploy --preview-create openclaw-memory

Exemplo de Busca Híbrida

$ ./search.sh "amw" -n 5

🔍 Resultados para: 'amw'
   Fontes: 2 Convex + 3 local

1. 🔥 convex://... (score: 3.80)
   [CONVEX - conversation] Cliente AMW pediu orçamento...

2. 📄 memory/2026-03-19.md (score: 0.70)
   Análise Técnica Integral de Inexecução — AMW...

Próximos Passos (Para Integração Total)

Auto-save: Hook no ciclo de vida do OpenClaw
Auto-context: Detectar quando buscar memórias
Sincronização: Mover Convex → Obsidian após 30 dias
Embeddings: Busca semântica além de keyword

Notas

Convex gratuito: 1M operações/mês (suficiente)
Obsidian: Armazenamento local ilimitado
Latência: Convex ~50-100ms, Obsidian ~10ms

安全使用建议

What to consider before installing: - Data exposure: This skill will automatically save conversation turns and can read your Obsidian vault directory (VAULT_PATH). Those contents are posted to a remote Convex deployment URL. If you enable the recommended autoSave/autoContext hook, every user/AI turn can be transmitted. - Unknown remote endpoints & keys: The code and README include two different default Convex URLs and example DEPLOY keys in SKILL.md. Verify who owns the referenced convex.cloud deployments before sending any data. Treat the included keys as sensitive — if you run any deploy commands, rotate or avoid using embedded keys. - Missing declarations: The registry metadata does not declare CONVEX_DEPLOYMENT_URL or VAULT_PATH as required env variables even though the code uses them. That mismatch makes it easy to enable the skill without realizing it will access local files and network endpoints. - Recommendations before enabling auto-save: - Inspect and verify the Convex deployment URL(s). Prefer to host your own Convex instance or point to a backend you control. - Test in a sandbox or throwaway account: try search/save operations with non-sensitive data first and capture network traffic to confirm destination and payloads. - Limit VAULT_PATH to a safe, dedicated test vault (do not point it at your full Obsidian vault) or disable Obsidian scanning entirely. - Remove or ignore embedded deploy keys in SKILL.md; do not run npx deploy commands with those values unless you understand who controls them. - If you need only local search, consider disabling Convex integration and using the local-only parts of the scripts. Summary: the code implements the advertised functionality, but the combination of automatic saving, local-vault scanning, embedded deploy keys, and inconsistent/default remote endpoints creates a meaningful risk of unintended data exfiltration. Proceed only after verifying backend ownership and restricting the vault path or running in a safe sandbox.

功能分析

Type: OpenClaw Skill Name: convex-obsidian Version: 1.0.0 The skill implements a persistent memory system that syncs chat history and local Obsidian vault contents to a hardcoded external Convex cloud endpoint (energized-goshawk-977.convex.cloud). While framed as a legitimate feature, the use of hardcoded absolute paths for a specific user (/home/andrey/) and the default exfiltration of local markdown files to a remote database without prior configuration poses a significant privacy risk. Additionally, SKILL.md contains a sensitive CONVEX_DEPLOY_KEY, and hook.py automatically transmits conversation data to the remote URL by default.

能力评估

ℹ Purpose & Capability

The name/description match the code: the package provides Convex server functions, Python/JS CLIs, and hook code to save conversations and hybrid-search the local Obsidian vault. However, the skill does not declare the environment variables it actually uses (CONVEX_DEPLOYMENT_URL, VAULT_PATH, and the SKILL.md even documents CONVEX_DEPLOY_KEY values). That mismatch (behaviour requiring secrets/paths but not declared) reduces coherence and is unexpected.

⚠ Instruction Scope

Runtime instructions and the Python scripts instruct the agent to automatically save every conversation turn to a remote Convex deployment and to scan a local Obsidian vault path (VAULT_PATH) for context. The hook and CLI read local markdown files and will transmit conversation content and (search previews of) vault files to the remote Convex HTTP endpoints. Auto-save + automatic context injection gives the skill broad discretion to collect and transmit user data beyond a single query.

ℹ Install Mechanism

There is no formal install spec (instruction-only), which minimizes installer risk. The repo contains node files and package-lock.json and suggests using npx convex deploy (network download). The included files let you deploy or run code locally, but there is no verified packaged install source or instructions to restrict runtime network behavior — be cautious about running deploy commands that use the embedded keys.

⚠ Credentials

Although registry metadata lists no required env vars, the code and SKILL.md rely on CONVEX_DEPLOYMENT_URL and VAULT_PATH and the README includes explicit CONVEX_DEPLOY_KEY values. The code will read arbitrary markdowns under the vault path and send their contents (or previews) to the remote endpoints. The presence of plaintext deploy keys in SKILL.md and differing default Convex URLs across files (energized-goshawk-977.convex.cloud vs gallant-jackal-80.convex.cloud) are red flags: they make it unclear who controls the backend and where your data would go.

⚠ Persistence & Privilege

The skill does not force-enable itself (always:false), but SKILL.md explicitly instructs editing ~/.openclaw/openclaw.json to enable autoSave/autoContext so the hook runs after every message — that yields persistent data capture. Because the hook reads local files and posts to remote endpoints, enabling auto-save effectively grants long-term exfiltration capability unless you trust the remote service and configuration. There is no code that modifies other skills' configs, but auto-save is a significant behavioral escalation if enabled.

版本历史

v1.0.0

Initial release of Convex + Obsidian persistent memory integration for OpenClaw. - Automatic, persistent memory using Convex (hot/recent) and deep historical knowledge via Obsidian. - Hybrid search injects relevant context based on conversation history references. - Includes CLI utilities and scripts for saving, searching, and stats across both memory stores. - Deep integration with OpenClaw via hooks and command options for auto-save and context injection. - Detailed setup instructions, configuration options, and usage examples included.

元数据

Slug convex-obsidian

版本 1.0.0

许可证 MIT-0

累计安装 1

当前安装数 1

历史版本数 1

常见问题

Convex Obsidian 是什么？

Integração automática de memória persistente no OpenClaw, salvando conversas no Convex e buscando contexto híbrido com Convex e Obsidian para respostas relev... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 78 次。

如何安装 Convex Obsidian？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install convex-obsidian」即可一键安装，无需额外配置。

Convex Obsidian 是免费的吗？

是的，Convex Obsidian 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

Convex Obsidian 支持哪些平台？

Convex Obsidian 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 Convex Obsidian？

由 Andrey Tsushima（@andreytsushima）开发并维护，当前版本 v1.0.0。

Convex Obsidian