SkillPacks

Pluggable skillsets for AI agents
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Skillpacks provide a means of fine tuning agents on tools, and the ability to hotswap learned skills at inference time.

Install

pip install skillpacks

Quick Start

Create an episode to record agent events

from skillpacks import Episode

episode = Episode(remote="https://foo.bar")

Take an action

from mllm import Router, RoleThread
from skillpacks import V1Action, V1EnvState
from agentdesk import Desktop

router = Router.from_env()
desktop = Desktop.local()

thread = RoleThread()
msg = f"""
I need to open Google to search, your available action are {desktop.json_schema()}
please return your selection as {V1Action.model_json_schema()}
"""
thread.post(role="user", msg=msg)

response = router.chat(thread, expect=V1Action)
v1action = response.parsed

action = desktop.find_action(name=v1action.name)
result = desktop.use(action, **v1action.parameters)

Record the action in the episode

event = episode.record(
    state=V1EnvState(),
    prompt=response.prompt,
    action=v1action,
    tool=desktop.ref(),
    result=result,
)

Mark actions as approved

# approve one
episode.approve_one(event.id)

# approve the event and all actions prior to it
episode.approve_prior(event.id)

# approve all
episode.approve_all()

Get all approved actions in an episode

episode = Episode.find(id="123")[0]
actions = episode.approved_actions()

Get all approved actions in a namespace

from skillpacks import ActionEvent

actions = ActionEvent.find(namespace="foo", approved=True)

Get all approved actions for a tool

actions = ActionEvent.find(tool=desktop.ref(), approved=True)

Tune a model on the actions (In progress)

from skillpacks.model import InternVLChat
from skillpacks.runtime import KubernetesRuntime

runtime = KubernetesRuntime()
model = InternVLChat(runtime=runtime)

result = model.train(actions=actions, follow=True, publish=True)

Integrations

Skillpacks is integrated with:

MLLM A prompt management, routing, and schema validation library for multimodal LLMs
Taskara A task management library for AI agents
Surfkit A platform for AI agents
Threadmem A thread management library for AI agents

Community

Come join us on Discord.

Backends

Thread and prompt storage can be backed by:

Sqlite
Postgresql

Sqlite will be used by default. To use postgres simply configure the env vars:

DB_TYPE=postgres
DB_NAME=skills
DB_HOST=localhost
DB_USER=postgres
DB_PASS=abc123

Metadata

Release files for skillpacks 0.1.128

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for skillpacks 0.1.128
File	Size	Uploaded
skillpacks-0.1.128.tar.gz	44.4 kB	May 29, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for skillpacks 0.1.128
File	Interpreter	ABI	Platform	Reset
skillpacks-0.1.128-py3-none-any.whl 57.2 kB May 29, 2025	Python 3	none	any	Details

Total release size: 101.6 kB

Release files / skillpacks-0.1.128.tar.gz

Download URL	skillpacks-0.1.128.tar.gz
Size	44.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`80a54390002bf81337e60b089921f985125c374ba374bc73a2144fc1eca148ec`
BLAKE2b-256 checksum How to use checksums	`c695f624b491d57f6ce633522be2172e8d616f24c8080f7ddd179700364decfe`
Upload date	May 29, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.8.3 CPython/3.10.1 Darwin/24.3.0`

Release files / skillpacks-0.1.128-py3-none-any.whl

Download URL	skillpacks-0.1.128-py3-none-any.whl
Size	57.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`a0ce4e7cceacc53fd17e7b4136d064fb655e0e366e4520c5a49d0e76205d8ea1`
BLAKE2b-256 checksum How to use checksums	`d8c366cdeb7cca288f81e546fe304da361feb235a8ca30bd3b2ee890930dc232`
Upload date	May 29, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.8.3 CPython/3.10.1 Darwin/24.3.0`