Skills

Bundle instructions and a tool scope into a reusable folder using the open Agent Skills layout — skills/<name>/SKILL.md with YAML frontmatter plus markdown instructions:

---
name: city-guide
description: Answer questions about cities
allowed-tools: [city_weather, city_population]
disallowed-tools: [secret_tool]
---

You are a city guide.  When asked to compare cities, call BOTH
`city_weather` and `city_population` in the SAME turn.

Mount it on any LLM-capable call — the LLM node or react() / harness():

from teff.flow import Flow
from teff.node import LLM
from teff.provider import ProviderRegistry

flow = Flow(
    "city-bot",
    providers=ProviderRegistry.from_presets("ollama"),
    default_provider="ollama",
    default_model="llama3.1:8b",
)
flow.harness(
    input_key="query",
    output_key="answer",
    skills=["city-guide"],
    skill_dir="skills",
)

# same for a plain LLM node
flow.step(LLM(skills=["city-guide"], use_tools=True))

A mounted skill:

• merges its instructions into the system prompt;
• narrows the visible tools: allowed-tools intersects with the node's set, disallowed-tools removes tools outright — so secret_tool above stays out of the model's reach even though it is registered for the run.

Bare names resolve against skill_dir; you can also pass skill paths or already-loaded Skill objects. use_tools gives the same per-node scope without skills: True (all), False (none), or a list of names.

Core skills

Teff ships built-in system skills (prefixed teff-, marked [system]), e.g. teff-tool-discipline, teff-structured-output, teff-verification. They are loaded by name and visible in prompts as [system]:

from teff import core_skills, get_core_skill

for skill in core_skills():
    print(skill.name, skill.description)

skill = get_core_skill("teff-verification")

Custom skills (with a SKILL.md) shadow a core skill of the same name. See the API reference for resolve_skills and skills_instructions.