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界面增强 #agent-skills#ai-plugin#claude-code#claude-plugin#cli#codex

ai-plugin (dsh-plugin)

aipx 工具包:技能之外还有 Hub Console 控制台——用约 4 个元工具代理全部 MCP 服务器,含服务器池健康、工具级启停、工具目录,以及直观展示 mcp_search 返回结果的搜索试验场。

zhangliang0115 @zhangliang0115 ⬇ 1 ★ 0 main

安装

dsh plugin --profile web add github:zhangliang0115/ai-plugin
下载安装清单

需要可复现安装时,可在仓库后追加 #commit 固定提交。

aipx 工具包:技能之外还有 Hub Console 控制台——用约 4 个元工具代理全部 MCP 服务器,含服务器池健康、工具级启停、工具目录,以及直观展示 mcp_search 返回结果的搜索试验场。

该插件未提供要点说明,请参考仓库 README。

agent-skillsai-pluginclaude-codeclaude-pluginclicodex
  1. 安装并启动 DeepSeek Harness:npx @deepseek-ai/dsh web
  2. 在终端执行上面的安装命令(CLI 会解析插件并核验来源)
  3. 用 dsh plugins list 确认已安装,必要时重启 Harness 生效

插件以当前 dsh 进程的权限运行,安装时可能执行代码。请先通读仓库源码与许可证,确认无破坏性命令与越权访问;本站只做索引,不对第三方插件安全性作担保。

代码仓库github.com/zhangliang0115/ai-plugin/tree/main/dsh-plugin
许可证MIT
主要语言main
下载量1
GitHub 星标0
最近推送2026-09-14
收录日期2026-09-19
分类界面增强

事实信息来自公开插件目录快照(2026-10-01),介绍文案由本站再加工。

以下为插件仓库 README 全文(原始内容,由公开目录抓取整理)。

# ai-plugin

**Skills into the shared standard root every major harness reads — plus an MCP hub that fronts every server with ~4 meta tools, and a console to manage it all.**

Claude Code · DeepSeek Harness (dsh) · Codex CLI · Gemini CLI · GitHub Copilot · Cursor · OpenClaw

[![CI](https://github.com/zhangliang0115/ai-plugin/actions/workflows/ci.yml/badge.svg)](https://github.com/zhangliang0115/ai-plugin/actions/workflows/ci.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Node](https://img.shields.io/node/v/aipx.svg)](package.json)
[![GitHub stars](https://img.shields.io/github/stars/zhangliang0115/ai-plugin?style=social)](https://github.com/zhangliang0115/ai-plugin/stargazers)

English | [简体中文](README.zh-CN.md)

---

Your machine runs 4 different AI agents. Your favorite skill exists as a
GitHub repo. Now what? Copy folders into `~/.claude/skills`, then
`~/.agents/skills`, then `~/.gemini/skills`, then `~/.copilot/skills`… and
re-do it after every upstream update. And once every agent has twenty MCP
servers wired up, their tool definitions eat your context window alive.

**`aipx` fixes both.** One command installs a skill into the shared standard
root (`~/.agents/skills` — read natively by dsh and Codex, linkable by the
rest). And the **aipx MCP hub** fronts ALL your MCP servers with 4 meta
tools — search, call, status, refresh — so the model sees one server instead
of fifty. The bundled skills teach you (and your agents) how to publish for
every harness from a single repo.

```bash
npx github:zhangliang0115/ai-plugin install /
```

  [图片: aipx MCP hub console — server pool, tool toggles, search playground (inside dsh settings)]

## Why

Every agent harness converged on the same skill format — `SKILL.md` — but
*not* on the same install location:

| Agent | Reads skills from |
|---|---|
| DeepSeek Harness (`dsh`) | `~/.agents/skills/` + `/.agents/skills/` |
| Codex CLI | `~/.agents/skills/` + `/.agents/skills/` |
| Claude Code | `~/.claude/skills/` + `/.claude/skills/` |
| Gemini CLI | `~/.gemini/skills/` + `/.gemini/skills/` |
| GitHub Copilot CLI | `~/.copilot/skills/` + `/.github/skills/` |
| Cursor / OpenCode / OpenClaw | their own roots ([full matrix](docs/compatibility-matrix.md)) |

Plugins fragment even further: Claude Code wants
`/plugin marketplace add`, dsh wants
`dsh plugin --profile web add "github:o/r#path:/dsh-plugin"`, Gemini wants
`gemini extensions`. `aipx` is the missing common denominator: one installer,
one registry, one list, for all of them.

## Commands

```bash
aipx install owner/repo                          # repo root or skills/ auto-detected
aipx install owner/repo#path:/skills/their-skill # subdirectory (same syntax as dsh)
aipx install https://github.com/owner/repo/tree/v1.2/skills/x   # pinned ref
aipx install ./my-skill                          # local directory
aipx install owner/mcp-server                    # .mcp.json repos add MCP servers too
aipx install owner/repo --project                # project-scoped: .claude/skills,
                                                 # .agents/skills, .github/skills, …
                                                 # committed with the repo for the team

aipx upgrade         # re-install recorded skills from their source (--force semantics)
aipx list            # what's installed, per agent
aipx search deepseek # curated registry; add --github for live GitHub topics
aipx lint skills     # validate SKILL.md quality (frontmatter, triggers, links, nesting)
aipx new my-skill    # scaffold a publish-ready dual-target skill repo
aipx collection                    # browse curated capability bundles
aipx collection deepseek-coding --run   # install a whole stack in one go
aipx mcp list        # inventory MCP servers across every agent's config
aipx mcp import      # register discovered MCP servers into the aipx hub
aipx mcp add fs -- npx -y @modelcontextprotocol/server-filesystem /tmp   # register one more
aipx mcp serve       # run the hub: one MCP server, 4 meta tools, zero context bloat
aipx remove    # uninstall everywhere
aipx doctor          # environment + agent detection + version check
```

Example:

```console
$ aipx install JimmyLv/bibigpt-skill#path:/skills/bibi
✔ detected skill with 1 skill(s):
    bibi — Summarize YouTube, Bilibili videos and podcasts…
✔ target roots:
    ~/.agents/skills (Shared skills root — read natively by dsh & Codex)
✔ installed bibi into shared root ~/.agents/skills
```

## MCP hub — every server, ~4 tools, one context

Every downstream MCP server dumps its full tool catalog into your context.
With 20 servers × 10 tools that's tens of thousands of tokens of tool
definitions the model must wade through on every turn.

The aipx hub flips it: one MCP server (the hub) fronts all of them and
exposes ~4 meta tools. The model **searches** for a capability, gets the
matching tool's `inputSchema` back, then **calls** it — loading only what it
uses.

```bash
aipx mcp import        # pull every MCP server found in your agent configs
aipx mcp add fs -- npx -y @modelcontextprotocol/server-filesystem /tmp   # register one more
aipx mcp serve         # speak MCP over stdio; wire this into any agent:
#   { "mcpServers": { "aipx": { "command": "aipx", "args": ["mcp", "serve"] } } }
```

| Meta tool | Purpose |
|---|---|
| `mcp_search` | keyword-search every downstream tool; returns id + description + `inputSchema` |
| `mcp_call` | execute a downstream tool by `server/tool` id from `mcp_search` |
| `mcp_status` | registered servers, tool counts, health |
| `mcp_refresh` | re-scan servers after you add or remove one |

Downstream servers are spawned on demand and reused. Both transports are
supported — local stdio and remote streamable-HTTP servers — and search runs
through a pluggable index with four engines, picked automatically: a
zero-dep lexical scorer; zvec full-text (BM25-style, Chinese-aware); a
**zero-config hybrid** that fuses full-text with a free local embedding
model (~220 MB, auto-installed and auto-downloaded on first build — no API
key); and a remote-embeddings hybrid for teams that already run one. On the
bundled 20-query eval, hybrid ranks 14/20 top-1 vs 8/20 lexical — Chinese
phrasings go 0/10 → 9/10. Docs: [MCP hub guide](docs/mcp-hub.md) · [search
engines + eval](docs/mcp-hub-vector-search.md).

### Hub console — manage the hub from dsh's settings

In DeepSeek Harness, the bundle adds a **Hub Console** tab under Settings →
Plugins: the server pool with live health, add/remove servers, per-tool
enable/disable (disabled tools leave the model-visible catalog), the tool
catalog with filtering, and a search playground that shows exactly what
`mcp_search` would hand the model — type 中文, see which tools surface.
Tui profiles skip the console; skills work everywhere.

  [图片: Hub Console — server pool, per-tool toggles, tool catalog]

  [图片: Search playground — what mcp_search returns for a Chinese query]

## What's bundled (the toolkit)

This repo is itself a plugin payload — use it three ways:

```bash
# 1. Plain skills, every agent:
aipx install zhangliang0115/ai-plugin

# 2. Claude Code marketplace:
#    /plugin marketplace add zhangliang0115/ai-plugin
#    /plugin install ai-plugin-toolkit@ai-plugin

# 3. DeepSeek Harness bundle:
dsh plugin --profile web add "github:zhangliang0115/ai-plugin#path:/dsh-plugin"
```

| Skill | Teaches your agent to |
|---|---|
| `skill-author` | write SKILL.md skills that load in every harness — incl. the tier-shadowing and discovery gotchas generic guides miss |
| `skill-portability-audit` | audit "works in Claude but not in dsh" failures: collisions, shadowing, trigger quality, per-agent smoke matrix |
| `dsh-plugin-dev` | package & publish DeepSeek Harness bundles (cordis.patch.yml, ctx.skills.register, the git-install gotchas) |
| `claude-plugin-dev` | publish Claude Code plugins & marketplaces with the dual-target pattern (one repo → every agent) |
| `deepseek-cost-router` | route work between deepseek-chat / deepseek-reasoner to cut API cost |
| `deepseek-migration` | migrate an agent setup from OpenAI/Anthropic to DeepSeek — caching, tool-calling, cost levers, dsh option |

## Design principles

- **Zero dependencies.** One JS file per concern, `node:test` suite, no
  supply-chain surface.
- **Non-destructive.** Installs skip existing targets unless `--force`;
  `--dry-run` previews; removal goes through a manifest.
- **One canonical root.** `~/.agents/skills` is the shared standard (read
  natively by dsh and Codex) — install writes one copy there and nothing else.
  No duplicate trees, no drift.
- **Context-first MCP.** The hub fronts every downstream MCP server with a
  handful of meta tools; the model searches and calls on demand instead of
  loading every tool definition into context.

## Docs

- [Compatibility matrix](docs/compatibility-matrix.md) — every root, every tier
- [Install into DeepSeek Harness (dsh)](docs/install-into-dsh.md) — researched guide: skill roots, tiers, bundle format, gotchas
- [Install into Claude Code](docs/install-into-claude-code.md) — marketplaces & plugins
- [MCP config sync](docs/mcp-sync.md) — 简体中文版:[docs/mcp-sync.zh-CN.md](docs/mcp-sync.zh-CN.md)
- [Publish once, target every agent](docs/publish-dual-target.md) — the dual-target repo layout
- [Quick actions in dsh](docs/dsh-quick-actions.md) — dsh-native custom prompts and what we deliberately don't rebuild
- [MCP ecosystem](docs/mcp-ecosystem.md) — use/reference/build decisions for MCP managers
- [Troubleshooting](docs/troubleshooting.md) — common failures and fixes

## Requirements

Node.js ≥ 20 and `tar` (built into macOS, Linux, Windows 10+). No `npm install`
step — `npx github:zhangliang0115/ai-plugin` runs straight from the repo, or install globally with `npm i -g @zhangliang0115/aipx`.
Optional: `GITHUB_TOKEN` for higher API rate limits.

## Roadmap

- [x] v0.1 — install / list / search / remove / doctor
- [x] v0.2 — project-scope installs, `aipx new` scaffolder, `aipx upgrade`, lint
- [x] v0.3 — MCP server config sync, registry validation bot + website + install smoke
- [x] v0.4 — **MCP hub** (`mcp import` / `mcp add` / `mcp serve`), skills toolkit (6 skills)
- [x] next — vector search contract + pluggable sidecar index, registry collections (`aipx collection`)
- [ ] then — zvec sidecar wiring (Python), npm registry publish, registry expansion

See [ROADMAP.md](ROADMAP.md) and [CHANGELOG.md](CHANGELOG.md).

## Contributing

PRs welcome — especially new curated registry entries and community-tier root
confirmations. See [CONTRIBUTING.md](CONTRIBUTING.md) and the
[plugin submission template](.github/ISSUE_TEMPLATE/plugin-submission.md).

## License

[MIT](LICENSE) © 2026 zhangliang0115

数据来源:公开的 DeepSeek Harness 插件目录与各插件 GitHub 仓库。本站为独立第三方目录,与 DeepSeek、幻方(High-Flyer)及插件作者均无隶属或背书关系。

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