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ivangdavila

Convex

作者 Iván · GitHub ↗ · v1.0.0
linuxdarwinwin32 ✓ 安全检测通过
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在 OpenClaw 中安装
/install convex
功能描述
Build and maintain Convex backends with schema-safe modeling, query and mutation patterns, auth guards, and production rollout checks.
使用说明 (SKILL.md)

Setup

On first use, read setup.md for integration guidelines.

When to Use

User is building, debugging, or scaling a Convex backend and needs reliable patterns for data modeling, queries, mutations, actions, auth, and deployments.

This skill focuses on implementation quality and operational safety, not generic framework tutorials.

Architecture

Memory lives in ~/convex/. See memory-template.md for structure and status fields.

~/convex/
|- memory.md               # Durable project context and technical decisions
|- schema-notes.md         # Table design and index rationale
|- rollout-notes.md        # Deploy and incident learnings
`- auth-notes.md           # Auth model and permission edge cases

Quick Reference

Use the smallest relevant file for the current task.

Topic File
Setup flow setup.md
Memory template memory-template.md
Schema and indexes schema-and-indexes.md
Deploy and operations operations-playbook.md

Requirements

  • No API keys or external credentials are required by this skill itself.
  • TOKEN/KEY: Not required by this skill.
  • If a project uses third-party integrations, treat those credentials as user-managed secrets and never persist raw values in memory files.

Data Storage

This skill stores reusable context only under ~/convex/:

  • memory file for durable project context and decisions
  • schema notes for modeling and index rationale
  • rollout notes for deployment and incident lessons
  • auth notes for permission and boundary edge cases

Do not store secrets, access tokens, or personal data unless the user explicitly requests it.

Core Rules

1. Model Access Patterns Before Writing Schema

Define tables and indexes from real read paths first:

  • Which fields are filtered most often
  • Which sort order is needed
  • Which uniqueness guarantees are required

Do not rely on table scans in production paths.

2. Keep Function Boundaries Strict

Use each function type for its intended purpose:

  • Query: read-only and deterministic
  • Mutation: validated state changes
  • Action: external side effects and network calls

Do not mix external calls inside deterministic data paths.

3. Enforce Auth and Authorization at Every Entry Point

Treat every query, mutation, action, and HTTP entrypoint as untrusted input:

  • Resolve identity explicitly
  • Check workspace or tenant boundaries
  • Validate ownership before returning or mutating records

Never trust client-provided identifiers without server checks.

4. Design Indexes for Stability, Not Just Speed

Create indexes that match long-term product workflows:

  • Primary user lookup paths
  • Admin and backoffice paths
  • Background processing queues

Document index intent so future changes do not break critical queries.

5. Make Writes Idempotent for Retries

For webhooks and external callbacks:

  • Use stable idempotency keys
  • Upsert safely when replayed
  • Record processing status

A retry should not create duplicate side effects.

6. Ship with Safe Rollout Discipline

Before deploying schema or logic changes:

  • Verify backward compatibility for current clients
  • Prepare migration steps for renamed fields or tables
  • Confirm failure mode and rollback path

Never deploy unreviewed breaking data changes.

7. Preserve Debuggability in Production

When fixing incidents:

  • Capture a minimal reproduction query
  • Keep structured logs around actor, function, and record ids
  • Record final root cause and preventive rule in memory

Fast diagnosis compounds over time.

Common Traps

  • Building schema from entities only, not query paths -> slow reads and rework.
  • Putting network side effects in deterministic logic -> nondeterministic failures.
  • Treating auth as a UI concern -> cross-tenant data leaks.
  • Adding indexes reactively during outages -> unstable rollout under pressure.
  • Shipping breaking schema changes without migration staging -> runtime failures.
  • Ignoring idempotency for callbacks -> duplicate writes and billing errors.

Security & Privacy

Data that leaves your machine:

  • None by default from this skill itself.

Data that stays local:

  • Convex project context and decisions under ~/convex/.

This skill does NOT:

  • Automatically call external services.
  • Manage or store secrets outside user-approved files.
  • Apply destructive schema changes without explicit confirmation.
  • Modify files outside ~/convex/ for memory.

Related Skills

Install with clawhub install \x3Cslug> if user confirms:

  • backend - Service architecture and operational reliability patterns.
  • typescript - Type-safe design and implementation for app and backend code.
  • javascript - Runtime behavior and language-level debugging workflows.

Feedback

  • If useful: clawhub star convex
  • Stay updated: clawhub sync
安全使用建议
This skill appears coherent and local-only, but before installing: (1) Confirm you're comfortable with the skill creating and writing files under ~/convex/ (it will store memory.md, notes, and playbooks). (2) Never paste secrets or raw API keys into those memory files; follow the skill's own admonition to treat secrets as user-managed. (3) Add ~/convex/ files to your .gitignore if you store code repositories nearby to avoid accidental commits. (4) Review the created files after first use to verify no sensitive data was stored and to set appropriate file permissions. (5) If you want tighter control, run the skill in an environment (VM/container) where home-directory writes are isolated.
功能分析
Type: OpenClaw Skill Name: convex Version: 1.0.0 The skill bundle is benign. All files, including SKILL.md and setup.md, consistently provide instructions for the AI agent to follow secure development practices for Convex backends. Explicit instructions are given to avoid storing secrets, prevent data exfiltration, and ensure data integrity and authorization checks. There is no evidence of malicious prompt injection, unauthorized file access, external communication, or any other harmful behavior. The skill's scope is clearly defined and limited to local context management within the `~/convex/` directory.
能力评估
Purpose & Capability
Name/description (Convex backend modeling, queries, auth, rollouts) match the instructions and supporting docs. The only declared external footprint is a local config path (~/convex/) used for project memory — this is appropriate for a developer-focused assistant that keeps project notes and checklists.
Instruction Scope
SKILL.md and auxiliary docs limit actions to reading/writing project context under ~/convex/, offering guidance on schema, indexes, auth, and rollouts. Instructions explicitly forbid storing secrets and calling external services by default. There is no instruction to read unrelated files or credentials.
Install Mechanism
Instruction-only skill with no install spec and no code files — nothing is downloaded or installed. This is the lowest-risk pattern and consistent with the described behavior.
Credentials
The skill declares no required env vars, no credentials, and only requires a config path under the user's home. Those requirements are proportional to a memory/ops playbook assistant and match the content of the SKILL.md.
Persistence & Privilege
The skill persists reusable project context to ~/convex/ (memory.md, notes). This is expected for its purpose, and always:false (not force-enabled) reduces risk. Users should be aware these files live on disk and confirm when the skill saves activation preferences or project context.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install convex
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /convex 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release with schema, auth, indexing, and rollout guidance for Convex projects.
元数据
Slug convex
版本 1.0.0
许可证
累计安装 3
当前安装数 3
历史版本数 1
常见问题

Convex 是什么?

Build and maintain Convex backends with schema-safe modeling, query and mutation patterns, auth guards, and production rollout checks. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 374 次。

如何安装 Convex?

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

Convex 是免费的吗?

是的,Convex 完全免费(开源免费),可自由下载、安装和使用。

Convex 支持哪些平台?

Convex 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(linux, darwin, win32)。

谁开发了 Convex?

由 Iván(@ivangdavila)开发并维护,当前版本 v1.0.0。

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