av/harbor is a command-line tool and companion app that uses Docker Compose to start a connected local stack of language-model backends, user interfaces, and supporting AI services. People use it to run services such as Ollama, llama.cpp, vLLM, Open WebUI, search, voice, and image-generation tools without configuring their connections manually, while the catalogue provides agent workflows for operating Harbor.
Borrowing it
Nothing to install: this file belongs to av/harbor. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/av/harbor/main/.agents/skills/new-boost-module/SKILL.mdgit clone --depth 1 https://github.com/av/harborWrote 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/av/harbor/new-boost-module)<a href="https://agentmods.dev/skills/av/harbor/new-boost-module"><img src="https://agentmods.dev/badge/skills/av/harbor/new-boost-module/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/av/harbor/new-boost-module"><img src="https://agentmods.dev/badge/skills/av/harbor/new-boost-module.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 159 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00116 | $0.01798 |
| Opus 5.5 | $0.00046 | $0.00719 |
| Sonnet 5.5 | $0.00023 | $0.00360 |
| Haiku 4.5 | $0.00012 | $0.00180 |
Grade A, and why
new-boost-module 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creating Harbor Boost Modules
Harbor Boost is an optimizing LLM proxy with an OpenAI-compatible API. Modules are Python files that hook into the completion pipeline — they can rewrite prompts, inject system messages, chain multiple LLM calls, stream artifacts, or replace the completion entirely.
A module is activated by prefixing a model name with the module's ID_PREFIX. For example,
a module with ID_PREFIX = "mymod" is invoked via model mymod-llama3.1.
Before You Write Code
- Read the Custom Modules guide:
docs/5.2.1.-Harbor-Boost-Custom-Modules.md - Scan existing modules in
services/boost/src/modules/to find similar functionality you can reuse or learn from. There are 17+ built-in modules covering reasoning chains, prompt rewriting, structured output, artifacts, and more. - Read the Built-in Modules reference:
docs/5.2.3-Harbor-Boost-Modules.md
Understanding the available primitives (chat, llm, config, selection, log) saves
you from reinventing patterns that already exist.
Module Structure
Every module is a single .py file with two required exports:
ID_PREFIX = 'my_module'
async def apply(chat: 'Chat', llm: 'LLM'):
# Module logic here
pass
Required Exports
| Export | Type | Purpose |
|---|---|---|
ID_PREFIX |
str |
Unique identifier. Becomes the model prefix (e.g., mymod-llama3.1). Use lowercase, short, memorable names. |
apply |
async def(chat, llm) |
Entry point called for every matching completion request. |
Recommended Exports
| Export | Type | Purpose |
|---|---|---|
DOCS |
str |
Markdown documentation shown in the Boost modules reference. Include a description, parameters, and usage examples. |
logger |
Logger | Created via log.setup_logger(ID_PREFIX) for consistent, filterable logging. |
Core Primitives
chat — The Conversation
Chat is a linked list of ChatNode objects. The tail is the most recent message.
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.
- 2mo ago First seen · 204 lines · 116 tokens per session scan A bcef6929ce49
new-boost-module is a skill published in the GitHub repository av/harbor (3,263 stars, last pushed 6d ago), licensed Apache-2.0. It adds 116 tokens to every session and 1,798 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
new-boost-module compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| new-boost-moduleav/harbor | Skill | 3.3k | 6d ago | Apache-2.0 | A | 116 | Claude Code, Codex |
| groq-inferencesynthetic-sciences/openscience | Skill | 4.3k | 2d ago | Apache-2.0 | A | 77 | Claude Code, Codex |
| background-taskvstorm-co/full-stack-ai-agent-template | Skill | 2.0k | 7d ago | MIT | A | 62 | Claude Code |
| channel-botvstorm-co/full-stack-ai-agent-template | Skill | 2.0k | 7d ago | MIT | A | 53 | Claude Code |
| bun-file-iosynthetic-sciences/openscience | Skill | 4.3k | 2d ago | Apache-2.0 | A | 49 | Claude Code, Codex |
| protocolsio-integrationsynthetic-sciences/openscience | Skill | 4.3k | 2d ago | Apache-2.0 | B | 85 | Claude Code, Codex |
| opensrcvercel-labs/opensrc | Skill | 3.0k | 1mo ago | Apache-2.0 | A | 103 | Claude Code |