Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/HK-hub/AgentSkillsnpx agentmods add skills/hk-hub/agentskills/build-mcpbWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hk-hub/agentskills/build-mcpb)<a href="https://agentmods.dev/skills/hk-hub/agentskills/build-mcpb"><img src="https://agentmods.dev/badge/skills/hk-hub/agentskills/build-mcpb/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hk-hub/agentskills/build-mcpb"><img src="https://agentmods.dev/badge/skills/hk-hub/agentskills/build-mcpb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00098 | $0.01869 |
| Opus 5 | $0.00049 | $0.00934 |
| Sonnet 5 | $0.00020 | $0.00374 |
| Haiku 4.5 | $0.00010 | $0.00187 |
Grade A, and why
build-mcpb scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to build-mcpb — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build an MCPB (Bundled Local MCP Server)
MCPB is a local MCP server packaged with its runtime. The user installs one file; it runs without needing Node, Python, or any toolchain on their machine. It's the sanctioned way to distribute local MCP servers.
Use MCPB when the server must run on the user's machine — reading local files, driving a desktop app, talking to localhost services, OS-level APIs. If your server only hits cloud APIs, you almost certainly want a remote HTTP server instead (see build-mcp-server). Don't pay the MCPB packaging tax for something that could be a URL.
What an MCPB bundle contains
my-server.mcpb (zip archive)
├── manifest.json ← identity, entry point, config schema, compatibility
├── server/ ← your MCP server code
│ ├── index.js
│ └── node_modules/ ← bundled dependencies (or vendored)
└── icon.png
The host reads manifest.json, launches server.mcp_config.command as a stdio MCP server, and pipes messages. From your code's perspective it's identical to a local stdio server — the only difference is packaging.
Manifest
{
"$schema": "https://raw.githubusercontent.com/anthropics/mcpb/main/schemas/mcpb-manifest-v0.4.schema.json",
"manifest_version": "0.4",
"name": "local-files",
"version": "0.1.0",
"description": "Read, search, and watch files on the local filesystem.",
"author": { "name": "Your Name" },
"server": {
"type": "node",
"entry_point": "server/index.js",
"mcp_config": {
"command": "node",
"args": ["${__dirname}/server/index.js"],
"env": {
"ROOT_DIR": "${user_config.rootDir}"
}
}
},
"user_config": {
"rootDir": {
"type": "directory",
"title": "Root directory",
"description": "Directory to expose. Defaults to ~/Documents.",
"default": "${HOME}/Documents",
"required": true
}
},
"compatibility": {
"claude_desktop": ">=1.0.0",
"platforms": ["darwin", "win32", "linux"]
}
}
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 198 lines · 98 tokens per session scan A f3c04fbc9912
build-mcpb is a skill published in the GitHub repository HK-hub/AgentSkills (6 stars, last pushed 23d ago), licensed MIT. It adds 98 tokens to every session and 1,869 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to build-mcpb, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
mermaid
Create, validate, and repair Mermaid.js diagrams. Use when generating flowcharts, sequence, class, ER, state, or Gantt diagrams, or any visualization.
teach-me
Turn a 'teach me X' request into a single interactive HTML lesson, rendered Arcade-first, by emitting a lesson data model and assembling it with rp1 tooling.
bootstrap
Bootstrap a greenfield project with parent-owned interviews and bounded plan, revision, and apply actions.
pr-stack
Plan and execute splitting a large PR or branch into a reviewable stacked PR sequence.
artifact-templates
Agent-only canonical output templates for rp1 artifacts. Load when producing structured markdown to ensure format consistency and routing metadata.
deep-research
Autonomous deep research on codebases and technical topics with structured report output via map-reduce explorer architecture.