Path-Scoped Skill Activation¶

Register reasoning-graph skills with glob patterns so they only activate when the agent is working in a matching directory. A TypeScript review skill stays hidden when the agent operates in a Python repo.

from promptise.engine import Skill, SkillRegistry
from promptise.engine.skills import code_reviewer

reg = SkillRegistry()
reg.register(
    Skill(
        name="ts-reviewer",
        description="Reviews TypeScript / React code.",
        paths=["**/*.tsx", "**/*.ts"],
        factory=lambda **kw: code_reviewer("ts_review", **kw),
    )
)

# Only activates when cwd contains .tsx or .ts files
active = reg.activate_for(cwd=".")

Why path scoping?¶

Modern agents have many skills (web research, code review, data analysis, summarization, etc.). Including all of them in every prompt wastes tokens and confuses the LLM. Path scoping is a cheap, declarative way to keep the active set small and contextual — no code changes needed when the agent moves between projects.

Skill dataclass¶

@dataclass
class Skill:
    name: str                          # unique identifier
    description: str = ""              # shown to the agent
    paths: list[str] = []             # glob patterns — empty = always active
    factory: Callable[..., Any] = None # returns a node, tool, or any effect
    metadata: dict[str, Any] = {}     # extra frontmatter fields

matches_cwd(cwd) returns True if any file under cwd matches any of the globs. Empty paths = always active (global skill).

SkillRegistry¶

reg = SkillRegistry()
reg.register(skill)                    # add a skill
reg.unregister("name")                 # remove by name
reg.get("name")                        # look up
reg.all()                              # all registered
reg.activate_for(cwd=".")             # path-filtered subset
reg.names_active_for(cwd=".")         # just the names, sorted

File-based loading with frontmatter¶

Write a .py file with a YAML-ish docstring header and a create() function:

# skills/ts_review.py
"""
name: ts-reviewer
description: Reviews TypeScript / React code.
paths:
  - src/**/*.tsx
  - src/**/*.ts
"""

def create(**kwargs):
    from promptise.engine.skills import code_reviewer
    return code_reviewer("ts_review", **kwargs)

Load an entire directory:

reg = SkillRegistry()
loaded = reg.load_directory("./.promptise/skills")
print(f"Loaded {loaded} skills")

The frontmatter parser is built-in — no YAML library required. It recognizes name, description, paths (as a list), and passes any extra keys through as metadata.

Using with PromptGraph¶

from promptise.engine import PromptGraph

reg = SkillRegistry()
reg.load_directory("./skills")

graph = PromptGraph("assistant")
for skill in reg.activate_for(cwd="."):
    graph.add_node(skill.create())

Skills Library — built-in skill factories (web_researcher, code_reviewer, etc.)
Tool Optimization — semantic tool selection for agents with large tool sets