new-boost-module

Create new Harbor Boost modules — the Python plugins that run inside Harbor's LLM proxy. Use this skill whenever the user wants to build a Boost module, write a custom module for Harbor Boost, add a new feature to the Boost proxy pipeline, or create any kind of middleware that transforms, augments, or intercepts LLM chat completions in Harbor. Also triggers when the user mentions "boost module", "boost plugin", "custom module for boost", or wants to add prompt engineering, reasoning chains, or output transforms to Harbor's proxy layer.

Quality

100%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

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.

chat.tail.content      # last message text
chat.text()            # full conversation as plain text
chat.history()         # list of dicts from tail upward
chat.user("...")       # append a user message
chat.assistant("...")  # append an assistant message

# Insert a system message before the first message
chat.tail.ancestor().add_parent(
    ch.ChatNode(role="system", content="You are helpful.")
)

# Create a fresh chat for a side-conversation
side = ch.Chat.from_conversation([
    {"role": "user", "content": "Summarize this."}
])

`llm` — The Downstream Model

llm talks to the actual LLM backend configured in Boost.

Output to the client (streamed in real time):

await llm.emit_message("text")           # raw text chunk
await llm.emit_status("Working...")       # status indicator
await llm.emit_artifact("<html>...</html>")  # Open WebUI artifact

Internal completions (not streamed, returned to you):

result = await llm.chat_completion(prompt="...", resolve=True)
result = await llm.chat_completion(messages=[...], resolve=True)
result = await llm.chat_completion(chat=some_chat, resolve=True)
# With structured output
result = await llm.chat_completion(prompt="...", schema=MyModel, resolve=True)

Streamed completions (sent to client AND returned):

text = await llm.stream_chat_completion(prompt="...")
text = await llm.stream_chat_completion(messages=[...])

Final completion (always reaches the client, even with intermediate output disabled):

await llm.stream_final_completion()                    # from current chat
await llm.stream_final_completion(prompt="Custom prompt")

Boost parameters — clients can pass @boost_* params in the request body:

llm.boost_params  # dict of params without the @boost_ prefix

`config` — Service Configuration

For configurable modules, define config entries in services/boost/src/config.py using the same pattern as existing modules. Config keys follow the HARBOR_BOOST_<MODULE>_<PARAM> convention and are exposed via harbor boost <module> <param> CLI.

`selection` — Message Selection

For modules that operate on specific messages rather than the whole chat, use the selection module. It provides strategy-based filtering (all, first, last, match, etc.) — see klmbr, eli5, or rcn modules for usage patterns.

Writing Good DOCS

The DOCS string is the user-facing documentation. It should include:

A brief description of what the module does and why it's useful
Parameter descriptions if the module is configurable
Usage examples showing harbor boost CLI commands and/or standalone Docker usage

Format example:

DOCS = """
Explains complex questions simply before answering them, improving accuracy
on nuanced prompts.

**Parameters**

- `strat` - message selection strategy. Default: `match`
- `strat_params` - selection filter. Default: matches last user message

```bash
harbor boost modules add eli5
harbor boost eli5 strat match
harbor boost eli5 strat_params role=user,index=-1

Standalone

docker run \\
  -e "HARBOR_BOOST_MODULES=eli5" \\
  -e "HARBOR_BOOST_OPENAI_URLS=http://172.17.0.1:11434/v1" \\
  -p 34131:8000 \\
  ghcr.io/av/harbor-boost:latest

"""

## Common Patterns

**Prompt injection** — add context before the final completion:
```python
async def apply(chat, llm):
    chat.tail.add_parent(ch.ChatNode(role="system", content="Extra context"))
    await llm.stream_final_completion()

Multi-step reasoning — chain internal completions, then produce a final answer:

async def apply(chat, llm):
    await llm.emit_status("Analyzing...")
    analysis = await llm.chat_completion(prompt="Analyze: {q}", q=chat.tail.content, resolve=True)
    await llm.stream_final_completion(prompt="Given analysis:\n{analysis}\n\nAnswer: {q}",
                                       analysis=analysis, q=chat.tail.content)

Pass-through with side effects — do something extra while preserving normal behavior:

async def apply(chat, llm):
    logger.info(f"Processing: {chat.tail.content[:50]}")
    await llm.stream_final_completion()

Checklist

Before considering the module done:

ID_PREFIX is unique, lowercase, and concise
DOCS export explains functionality, parameters, and includes usage examples
logger = log.setup_logger(ID_PREFIX) is defined
async def apply(chat, llm) is the entry point
Module file is placed in services/boost/src/modules/ (built-in) or boost/src/custom_modules/ (custom)
If configurable, config entries added to services/boost/src/config.py
Documentation in docs/5.2.3-Harbor-Boost-Modules.md is updated

Repository: av/harbor
Path: .agents/skills/new-boost-module/SKILL.md
Commit: ed20a18

Last updated: about 12 hours ago
First committed: 4 months ago

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.