Agent Skills (SKILL.md) parsing, discovery, and evolution tracking for AbstractFramework
Project description
AbstractSkill
AbstractSkill is the shared Python library for Agent Skills (SKILL.md) in the
AbstractFramework ecosystem.
It provides a small, dependency-light foundation for:
- parsing and validating
SKILL.mdfrontmatter and instructions - discovering skills on disk (progressive disclosure: metadata first)
- computing stable content hashes for skill evolution and replay safety
- formatting compact
<available_skills>prompt blocks for hosts and agents
Flows run; skills are activated. AbstractSkill owns the portable skill contract so abstractruntime,
abstractgateway, and thin clients can share identical semantics without duplicating parsers.
Install
pip install abstractskill
Quick start
from pathlib import Path
from abstractskill import FilesystemSkillLoader, format_available_skills_xml, parse_skill_md
# Parse a SKILL.md file
doc = parse_skill_md(Path("my-skill/SKILL.md").read_text(encoding="utf-8"))
print(doc.metadata.name, doc.metadata.description)
# Discover skills under one or more roots (later roots override earlier ones)
loader = FilesystemSkillLoader([Path.home() / ".abstract" / "skills", Path(".abstract/skills")])
skills = loader.discover()
print(format_available_skills_xml(skills))
# Load full instructions when a skill is activated
loaded = loader.load("my-skill")
print(loaded.document.content_hash)
Package scope (v0.1.0)
parse_skill_md— YAML frontmatter + markdown bodyFilesystemSkillLoader— list metadata and load full documentscontent_hash— SHA-256 digest for evolution trackingformat_available_skills_xml— deterministic discovery prompt block
Out of scope for this release: gateway registry APIs, zip .skill packaging, and runtime activation handlers.
Those layers live in abstractgateway and abstractruntime and consume this library.
Development
python -m pip install -e ".[test]"
python -m pytest -q
python -m build
License
MIT — see LICENSE.
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