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Agent Skill · 研究检索

convit-setup(确认设置)

convit-setup 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

convit-setup 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 72,083。

总安装 190周安装 8GitHub Stars 1下载 67核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:convit-setup(确认设置)
来源仓库:https://github.com/kareem-aez/convit
仓库路径:skills/convit-setup
安装命令:
npx skills add https://github.com/kareem-aez/convit --skill convit-setup
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/kareem-aez/convit --skill convit-setup

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

convit Setup 根据代码库结构生成 .convitrc.json 配置文件。适用于初始化 convit 工具链,自动适配项目类型与依赖关系。严禁直接读取 .env 类敏感文件,必须使用 scripts/ensure-convit-env.mjs 安全检测变量。

遵循零凭证日志政策,不得输出或提交任何密钥、令牌等认证信息。convit-setup 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/convit-setup

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/kareem-aez/convit --skill convit-setup;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

convit-setup 怎么安装?

在终端执行 npx skills add https://github.com/kareem-aez/convit --skill convit-setup。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

convit-setup 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

convit-setup 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

convit-setup 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

convit-setup 用的人多吗?

公开数据是累计安装 190 次,近一周安装 8 次,下载 67 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

convit-setup 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
convit-setup

确认设置 · 本页

1901接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

convit Setup

🚨 Security Mandate (CRITICAL)

  • Direct Environment Access Forbidden: Never use read_file, grep_search, or any other tool to read .env, .env.local, or any file containing secrets.
  • Environment Verification: To check for required CONVIT_* variables, you MUST ONLY use the ensure-convit-env.mjs script (located in the skill's scripts/ folder). This script safely checks variable names without exposing values.
  • Zero-Credential Policy: Never log, print, or commit any credentials or API keys.

convit Setup Overview

Generates a .convitrc.json tailored to the actual structure of the codebase. The config should capture Intent, not just file paths.

Quick start: (1) Apply the Hierarchy Principle to find the Primary Structural Boundary. (2) Run Structural Analysis (Full-Scan, Depth, Gitignore, Noise). (3) Apply Weighting Engine rules. (4) Synthesize patterns with user patterns first. (5) Ask user to confirm. (6) Write config.

Never include a provider block. API credentials (CONVIT_URL, CONVIT_KEY, CONVIT_MODEL) belong in .env only. The config file is safe to commit.

For the full semantics of every variable, see config-reference.md.


The Mental Model (The Hierarchy Principle)

Before scanning folders, identify the Primary Structural Boundary. This is the deepest semantic unit that defines how the codebase is organized. The agent must reason from first principles, not from a lookup table.

Surgical Core (Weight 10/8). Where the primary application logic lives. Common candidates: src/, lib/, app/. Domain-driven: src/features/auth/, src/modules/payments/, lib/domains/orders/. Layered: controllers/, models/, views/, or handlers/, services/, repositories/. Monorepo: packages/ or apps/ contains distinct projects. Each subdirectory is a top-level scope. Weight 10 for deepest semantic boundary, 8 for broad organizational folders.

Functional Layers (Weight 5). Cross-cutting concerns: ui/, db/, api/. The first segment under the core is the scope when fixed. Example: db/.* → db, prisma/.* → db.

Auxiliary Support (Weight 3/5). The tools and documentation that support the project. Weight 5: assets/, docs/, images/. Weight 3: .cursor/, .github/, scripts/.

Apply the Hierarchy Principle to any codebase. A 10-year-old COBOL project with cobol/programs/, cobol/copybooks/, cobol/data/ is layered. A brand-new Next.js app with src/app/(auth)/, src/app/(dashboard)/ is domain-driven. The reasoning engine adapts.


Phase 1 — Structural Analysis (Principles, not Folders)

Run this protocol. Do not rely on hardcoded paths.

Full-Scan Protocol

Scan every top-level directory. If a directory is not build noise (e.g. .cursor, .github, assets, docs, scripts), propose it as a scope. Total coverage: the generated config must capture the entire repo, not just application code.

Gitignore Intelligence

Primary Check. If a directory is in .gitignore, skip it. Git already handles it. Do not propose it as a scope or add it to exclude.

Exclusion Candidate. If a directory looks like build output (e.g. dist/, build/, out/, target/) but is NOT in .gitignore, it is a high-priority candidate for the exclude list. Propose adding it.

Detect Depth

Walk paths. If they go 3+ levels deep (e.g. src/features/auth/internal/), the middle segment is likely the scope. The pattern should capture that segment with ([^/]+). Shallow structures (e.g. src/auth.ts, src/user.ts) suggest a single src/([^/]+) pattern. Deep structures suggest domain-specific patterns like src/features/([^/]+)/.* or src/modules/([^/]+)/.*.

Identify Noise

Look for lockfiles, .cache, dist, target, build, out, __pycache__, .pytest_cache, *.egg-info, generated output (e.g. src/generated/, .prisma/). Add these to exclude. Do not add node_modules — it is already excluded by default.


Phase 2 — The Weighting Engine Rules

Apply these weights when architecting scope patterns. Higher weight wins ties.

WeightBoundary TypeExample Pattern
10Deepest semantic boundarysrc/modules/([^/]+)/.*, packages/([^/]+)/.*, crates/([^/]+)/.*
8Broad organizational folderssrc/([^/]+)/.*, lib/([^/]+)/.*
7Universal Semantic Scopespackage.json → deps, .gitignore → git, README.md → docs
5Transversal layersui/.* → ui, db/.* → db, prisma/.* → db
5Documentation and Mediaassets/.*, docs/.*, images/.*
3Tooling and Infrastructure.cursor/.*, .github/.*, scripts/.*, config/.* → config

The primary boundary gets 10. Fallback patterns (catch-all under core) get 6–8. Universal root files (package.json, gitignore, readme) get 7 to ensure they are more specific than the root catch-all but subservient to deep domain logic. Cross-cutting concerns (UI, DB, API) and auxiliary docs/media get 5. Tooling, scripts, and config get 3.

Transversal layers stay at 5 so the primary boundary (10) overrides when a file lives in both. Example: src/features/auth/ui/button.tsx should scope to auth, not ui. The domain wins.


Phase 3 — Synthesis Logic

Generate regex patterns using ([^/]+) to dynamically capture directory names as scopes. The scope field is "$1" when the pattern captures a segment, or a literal string (e.g. "db", "ui") when the layer is fixed.

Universal Semantic Patterns should be proposed for common root files:

  • package.json and lockfiles → deps
  • .gitignore, .cursorignore, .dockerignore → git
  • README.md, LICENSE, CONTRIBUTING.md → docs
  • .env.example, .env.local → env
  • .convitrc.json → convit

User-specific patterns go at the top of the scopePatterns array. They override built-in defaults. If the user has custom domains or conventions, place those first.

Always include a fallback pattern at the end unless the user opts out:

{ "pattern": "src/([^/]+)/.*", "scope": "$1", "weight": 6 }

Adjust the fallback to match the core (e.g. lib/([^/]+)/.* if the core is lib/).


Phase 4 — Ask

Present findings and ask the user to confirm or adjust. Only ask about signals that actually fired. Group patterns by hierarchy so the user sees how the project is being analyzed.

Scope pattern prompts

Group proposed scope patterns under three headers:

Primary Boundary (Weight 8–10)

"Proposed scope pattern: {"pattern": "src/features/([^/]+)/.*", "scope": "$1", "weight": 10} Use this? (yes / adjust / skip)"

Transversal Layers (Weight 5)

"Proposed scope pattern: {"pattern": "ui/.*", "scope": "ui", "weight": 5} Use this? (yes / adjust / skip)"

**Auxiliary Support (Weight 3)"

"Proposed scope pattern: {"pattern": ".cursor/.*", "scope": "cursor", "weight": 3} Use this? (yes / adjust / skip)"

For each pattern, show the proposal and ask. If the user wants to adjust, ask for the corrected pattern, scope, or weight.

Exclude path prompts

For each detected build/generated output:

"Found dist/. Add to exclude list? (yes / skip)"

Rules prompts

Ask these three regardless of signals:

  1. "Max subject line length? (default: 50, range: 40–72)"
  2. "Min bullet points in commit body? (default: 1, range: 0–5)"
  3. "Max bullet line length? (default: 72, range: 60–100)"

If the user accepts the default, omit that key from rules. convit uses defaults automatically — only write keys the user explicitly customized.


Phase 5 — Write

Produce .convitrc.json at the project root with only the keys that have non-default or user-confirmed values. Shape must match exactly:

{
  "rules": {
    "maxSubjectLength": 50,
    "maxBulletLength": 72,
    "minBullets": 1
  },
  "scopePatterns": [
    { "pattern": "src/features/([^/]+)/.*", "scope": "$1", "weight": 10 },
    { "pattern": "src/([^/]+)/.*", "scope": "$1", "weight": 6 }
  ],
  "exclude": ["src/generated/prisma"]
}

Rules:

  • temperature is always omitted unless the user explicitly requests it (0.2 is the default and rarely needs changing)
  • Empty arrays ([]) should be omitted entirely
  • rules should be omitted entirely if no rule was customized
  • Never add a provider, apiKey, baseUrl, or model key

After writing, print a summary of what was configured. Offer to run the env setup script, or remind the user to add these to .env or .env.local:

Optional: Run the env setup script to append convit vars if missing.

Before running the script, tell the user explicitly:

I will run the ensure-convit-env.mjs script. It reads your .env or .env.local to check which CONVIT_* var names exist (by name only). It does not read, store, or transmit any values — no keys, no URLs, no secrets. If vars are missing, it appends placeholder lines to the file. The script output may mention local vs cloud (inferred from URL pattern) but never prints the actual URL or any credentials. You can review the script source before I run it.

Only run the script after the user has seen this disclosure and has not objected.

node .agents/skills/convit-setup/scripts/ensure-convit-env.mjs [path-to-project-root]

From project root, omit the path. The script checks .env.local first, then .env, and appends a dev-only block (with a box comment) that these vars should not be committed or uploaded. If the skill is installed globally, use the path to the skill's scripts/ folder.

Or add manually:

# Base URL of the OpenAI-compatible API — used for all LLM requests
CONVIT_URL="https://api.openai.com/v1"
# CONVIT_URL="http://localhost:1234/v1"   # LM Studio
# CONVIT_URL="http://localhost:11434/v1"  # Ollama

# API key — required for cloud providers (OpenAI, Anthropic). Omit for local models
CONVIT_KEY="sk-..."

# Model ID — which model to call. Omit to auto-detect from LM Studio
CONVIT_MODEL="gpt-4o-mini"
# CONVIT_MODEL="llama-3.2-3b-instruct"  # local

# Cost per 1M input tokens (USD) — enables cost display in terminal
# CONVIT_INPUT_COST="0.15"

# Cost per 1M output tokens (USD) — enables cost display in terminal
# CONVIT_OUTPUT_COST="0.60"

# Request timeout in ms — how long to wait for LLM response before aborting
# CONVIT_TIMEOUT="60000"

Example

Input: Next.js app with src/features/auth/, src/features/dashboard/, src/components/ui/. Primary boundary: src/. Depth is 3 levels. Noise: .next/.

Output:

{
  "scopePatterns": [
    { "pattern": "src/features/([^/]+)/.*", "scope": "$1", "weight": 10 },
    { "pattern": "src/components/ui/.*", "scope": "ui", "weight": 5 },
    { "pattern": "src/([^/]+)/.*", "scope": "$1", "weight": 6 }
  ],
  "exclude": [".next/"]
}

Primary boundary is src/features/([^/]+). Transversal layer components/ui/ gets fixed scope ui. Fallback src/([^/]+) catches files outside features. Noise excluded.

平台分布

Codex

38.48%
按下载量换算26

Claude

28.81%
按下载量换算19

Cursor

17.47%
按下载量换算12

Gemini CLI

9.24%
按下载量换算6

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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