tukuy 0.0.13

A flexible data transformation library with a plugin system

🌀 Tukuy

Portable agent skills library and data transformation toolkit for Python.

---

Tukuy (meaning "to transform" or "to become" in Quechua) is a cross-platform skills layer that any Python agent framework can use. It provides typed skill descriptors, agent-framework bridges (OpenAI, Anthropic), async-first execution, smart composition, and runtime safety enforcement — all built on top of a proven plugin-based transformation engine.

from tukuy import skill

@skill(name="parse_date", description="Parse a date string into ISO format")
def parse_date(text: str) -> str:
    from dateutil import parser
    return parser.parse(text).isoformat()

result = parse_date.__skill__.invoke("January 15, 2025")
print(result.value)  # "2025-01-15T00:00:00"

Installation

pip install tukuy

Quick Start

Define a skill

from tukuy import skill

@skill(
    name="parse_date",
    description="Parse a date string into ISO format",
    category="date",
    tags=["parsing", "datetime"],
)
def parse_date(text: str, format: str = "auto") -> str:
    """Parse date from text, return ISO 8601."""
    from dateutil import parser
    return parser.parse(text).isoformat()

The @skill decorator infers input/output schemas from type hints, detects async functions automatically, and attaches a Skill instance as fn.__skill__.

Invoke a skill

result = parse_date.__skill__.invoke("January 15, 2025")
print(result.value)      # "2025-01-15T00:00:00"
print(result.success)    # True
print(result.duration_ms) # 0.42

Use with an agent framework

from tukuy import to_openai_tools, to_anthropic_tools

skills = [parse_date, extract_entities, summarize]

# OpenAI function-calling format
tools = to_openai_tools(skills)

# Anthropic tool_use format
tools = to_anthropic_tools(skills)

Dispatch tool calls back to skills:

from tukuy import dispatch_openai, dispatch_anthropic

# OpenAI
result_msg = dispatch_openai(tool_call, skills)

# Anthropic
result_block = dispatch_anthropic(tool_use, skills)

---

Core Concepts

Skill Descriptors

Every skill has a declared-upfront contract via SkillDescriptor:

from tukuy import SkillDescriptor

descriptor = SkillDescriptor(
    name="web_scraper",
    description="Scrape and extract text from a URL",
    input_schema=str,
    output_schema=str,
    category="web",
    tags=["scraping", "extraction"],
    is_async=True,
    requires_network=True,
    required_imports=["aiohttp", "beautifulsoup4"],
    idempotent=True,
    side_effects=False,
)

Descriptors carry identity, typed I/O schemas, discovery metadata, operational hints, and safety declarations — everything an agent framework needs to discover and invoke a skill.

Async Support

Skills work with both sync and async functions:

@skill(name="fetch_page", requires_network=True)
async def fetch_page(url: str) -> str:
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as resp:
            return await resp.text()

# Async invocation
result = await fetch_page.__skill__.ainvoke("https://example.com")

Sync skills also work with ainvoke() — they're called normally without blocking the event loop.

Composition

Chain, branch, and fan-out skills with Chain, Branch, and Parallel:

from tukuy import Chain, branch, parallel

# Sequential pipeline
chain = Chain(["strip", "lowercase", parse_date])
result = chain.run("  January 15, 2025  ")

# Conditional branching
chain = Chain([
    "strip",
    branch(
        on_match=lambda v: "@" in v,
        true_path=["email_validator"],
        false_path=["url_validator"],
    ),
])

# Parallel fan-out with merge
chain = Chain([
    parallel(
        steps=["extract_dates", "extract_emails", "extract_phones"],
        merge="dict",  # {"extract_dates": [...], "extract_emails": [...], ...}
    ),
])

# Async execution with asyncio.gather for parallel steps
result = await chain.arun(input_text)

Steps can be transformer names (strings), parametrized transforms (dicts), Skill instances, @skill-decorated functions, plain callables, or nested Chain objects.

Context

Skills share state through a typed, scoped SkillContext:

from tukuy import skill, SkillContext

@skill(name="extract_entities")
def extract_entities(text: str, ctx: SkillContext) -> dict:
    entities = do_extraction(text)
    ctx.set("last_entities", entities)
    return entities

@skill(name="format_entities")
def format_entities(ctx: SkillContext) -> str:
    entities = ctx.get("last_entities")
    return format_them(entities)

Context supports namespaced scoping (for parallel branches), parent-child delegation, snapshot/merge, and bridging to plain dicts.

Safety Policy

Each skill declares what resources it needs. The runtime enforces these declarations against an active policy:

from tukuy import SafetyPolicy, set_policy

# Define what the environment allows
policy = SafetyPolicy(
    allowed_imports={"json", "re", "datetime"},
    blocked_imports={"os", "subprocess"},
    allow_network=False,
    allow_filesystem=False,
)

# Activate globally (async-safe via contextvars)
set_policy(policy)

# Skills that violate the policy are blocked before execution
@skill(name="web_scraper", requires_network=True)
async def web_scraper(url: str) -> str: ...

result = web_scraper.__skill__.invoke("https://example.com")
# result.success == False
# result.error == "Safety policy violated: Skill requires network access but policy denies it"

Convenience constructors for common scenarios:

SafetyPolicy.restrictive()    # No imports, no network, no filesystem
SafetyPolicy.permissive()     # Everything allowed
SafetyPolicy.network_only()   # Network yes, filesystem no
SafetyPolicy.filesystem_only() # Filesystem yes, network no

Policies can be exported/imported as sandbox configurations for integration with external sandbox runtimes:

config = policy.to_sandbox_config()
# {"allowed_imports": ["json", "re"], "network": False, "filesystem": False}

policy = SafetyPolicy.from_sandbox_config(config)

---

Data Transformations

Tukuy includes a full transformation engine with six built-in plugins. This is the foundation that the skills layer is built on.

from tukuy import TukuyTransformer

t = TukuyTransformer()

# Text
t.transform("  Hello World!  ", ["strip", "lowercase"])
# "hello world!"

# HTML
t.transform("<div>Hello <b>World</b>!</div>", ["strip_html_tags", "lowercase"])
# "hello world!"

# Chained with parameters
t.transform("  Hello World!  ", [
    "strip",
    "lowercase",
    {"function": "truncate", "length": 5},
])
# "hello..."

Built-in Plugins

Text — strip, lowercase, uppercase, truncate, replace, regex_replace, split, join, normalize

HTML — strip_html_tags, extract_text, select, extract_links, extract_tables, clean_html

JSON — parse_json, stringify, extract, flatten, merge, validate_schema

Date — parse_date, format_date, age_calc, add_days, diff_days, is_weekend

Numerical — round, format_number, to_currency, percentage, math_eval, scale, statistics

Validation — email_validator, url_validator, phone_validator, length_validator, range_validator, regex_validator, type_validator

Custom Plugins

Extend Tukuy with your own transformer plugins:

from tukuy import TransformerPlugin
from tukuy.base import ChainableTransformer

class ReverseTransformer(ChainableTransformer):
    def validate(self, value):
        return isinstance(value, str)

    def _transform(self, value, context=None):
        return value[::-1]

class MyPlugin(TransformerPlugin):
    def __init__(self):
        super().__init__("my_plugin")

    @property
    def transformers(self):
        return {"reverse": lambda _: ReverseTransformer("reverse")}

t = TukuyTransformer()
t.register_plugin(MyPlugin())
t.transform("hello", ["reverse"])  # "olleh"

Plugins support lifecycle hooks (initialize() / cleanup()) and can expose skills alongside transformers via the skills property.

---

Pattern-based Extraction

HTML

pattern = {
    "properties": [
        {"name": "title", "selector": "h1", "transform": ["strip", "lowercase"]},
        {"name": "links", "selector": "a", "attribute": "href", "type": "array"},
    ]
}
data = t.extract_html_with_pattern(html, pattern)

JSON

pattern = {
    "properties": [
        {
            "name": "user",
            "selector": "data.user",
            "properties": [
                {"name": "name", "selector": "fullName", "transform": ["strip"]},
            ],
        }
    ]
}
data = t.extract_json_with_pattern(json_str, pattern)

---

Error Handling

from tukuy.exceptions import ValidationError, TransformationError, ParseError

try:
    result = t.transform(data, transformations)
except ValidationError as e:
    print(f"Validation failed: {e}")
except ParseError as e:
    print(f"Parsing failed: {e}")
except TransformationError as e:
    print(f"Transformation failed: {e}")

---

Architecture

tukuy/
    skill.py          Skill descriptors, @skill decorator, invoke/ainvoke
    context.py        SkillContext with scoping, snapshot, merge
    safety.py         SafetyPolicy, manifest validation, sandbox integration
    bridges.py        OpenAI and Anthropic tool format bridges
    chain.py          Chain, Branch, Parallel composition
    async_base.py     Async transformer base classes
    base.py           Sync transformer base classes
    plugins/          Built-in plugins (text, html, json, date, numerical, validation)
    core/             Registration, introspection, unified registry
    transformers/     Transformer implementations

Contributing

Contributions are welcome.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Make your changes
4. Run tests with pytest
5. Commit and push
6. Open a Pull Request

License

See LICENSE for details.