Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/av/harbor/boost-modulesnpx skills add av/harbor --skill boost-modulesgit clone --depth 1 https://github.com/av/harborWhat 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 | $0.00000 | $0.01517 |
| Opus 5 | $0.00000 | $0.00758 |
| Sonnet 5 | $0.00000 | $0.00303 |
| Haiku 4.5 | $0.00000 | $0.00152 |
Grade A, and why
boost-modules scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
content += requests.get(url).text[:5000] How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
---
name: boost-modules
description: Create custom modules for Harbor Boost, an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.
---
# Harbor Boost Custom Modules
Boost modules are Python files that intercept chat completions and can transform, augment, or replace LLM responses.
## Module Structure
```python
ID_PREFIX = 'mymodule' # Models prefixed with this trigger the module
async def apply(chat, llm):
# chat: conversation history (linked list of ChatNodes)
# llm: interface to downstream LLM and output streaming
await llm.stream_final_completion()
Quick Reference
Output Methods
# Stream text to client
await llm.emit_message("Hello")
# Status indicator (formatted per HARBOR_BOOST_STATUS_STYLE)
await llm.emit_status("Processing...")
# Internal completion (not streamed to client)
result = await llm.chat_completion(prompt="Summarize: {text}", text=content, resolve=True)
# Streamed completion (visible to client)
await llm.stream_chat_completion(prompt="Explain {topic}", topic="quantum")
# Final completion (always streamed, even when intermediate output disabled)
await llm.stream_final_completion()
await llm.stream_final_completion(prompt="Reply to: {msg}", msg=chat.tail.content)
# Structured output
from pydantic import BaseModel, Field
class Response(BaseModel):
answer: str = Field(description="The answer")
result = await llm.chat_completion(prompt="...", schema=Response, resolve=True)
# Artifacts (for clients like Open WebUI)
await llm.emit_artifact("<h1>Interactive content</h1>")
Chat Manipulation
# Read conversation
chat.text() # Full conversation as string
chat.message # Last user message content
chat.tail # Last ChatNode
chat.tail.content # Content of last message
chat.tail.role # Role of last message
chat.history() # List of messages from tail
chat.plain() # List of ChatNodes from tail
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.
- 2d ago First seen · 208 lines · 0 tokens per session scan A 938edeb18490
boost-modules is a skill published in the GitHub repository av/harbor (3,198 stars, last pushed 3d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,517 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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