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Zhongwei Li
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skills/agent.compose/agent_compose.py
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329
skills/agent.compose/agent_compose.py
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#!/usr/bin/env python3
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"""
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agent_compose.py - Recommend skills for Betty agents based on purpose
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Analyzes skill artifact metadata to suggest compatible skill combinations.
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"""
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import os
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import sys
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import json
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import yaml
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from typing import Dict, Any, List, Optional, Set
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from pathlib import Path
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from betty.config import BASE_DIR
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from betty.logging_utils import setup_logger
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logger = setup_logger(__name__)
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def load_registry() -> Dict[str, Any]:
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"""Load skills registry."""
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registry_path = os.path.join(BASE_DIR, "registry", "skills.json")
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with open(registry_path) as f:
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return json.load(f)
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def extract_artifact_metadata(skill: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Extract artifact metadata from a skill.
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Returns:
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Dict with 'produces' and 'consumes' sets
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"""
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metadata = skill.get("artifact_metadata", {})
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return {
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"produces": set(a.get("type") for a in metadata.get("produces", [])),
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"consumes": set(a.get("type") for a in metadata.get("consumes", []))
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}
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def find_skills_by_artifacts(
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registry: Dict[str, Any],
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produces: Optional[List[str]] = None,
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consumes: Optional[List[str]] = None
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) -> List[Dict[str, Any]]:
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"""
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Find skills that produce or consume specific artifacts.
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Args:
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registry: Skills registry
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produces: Artifact types to produce
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consumes: Artifact types to consume
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Returns:
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List of matching skills with metadata
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"""
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skills = registry.get("skills", [])
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matches = []
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for skill in skills:
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if skill.get("status") != "active":
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continue
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artifacts = extract_artifact_metadata(skill)
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# Check if skill produces required artifacts
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produces_match = not produces or any(
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artifact in artifacts["produces"] for artifact in produces
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)
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# Check if skill consumes specified artifacts
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consumes_match = not consumes or any(
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artifact in artifacts["consumes"] for artifact in consumes
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)
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if produces_match or consumes_match:
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matches.append({
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"name": skill["name"],
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"description": skill.get("description", ""),
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"produces": list(artifacts["produces"]),
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"consumes": list(artifacts["consumes"]),
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"tags": skill.get("tags", [])
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})
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return matches
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def find_skills_for_purpose(
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registry: Dict[str, Any],
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purpose: str,
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required_artifacts: Optional[List[str]] = None
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) -> Dict[str, Any]:
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"""
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Find skills for agent purpose (alias for recommend_skills_for_purpose).
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Args:
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registry: Skills registry (for compatibility, currently unused)
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purpose: Description of agent purpose
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required_artifacts: Artifact types agent needs to work with
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Returns:
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Recommendation result with skills and rationale
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"""
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return recommend_skills_for_purpose(purpose, required_artifacts)
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def recommend_skills_for_purpose(
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agent_purpose: str,
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required_artifacts: Optional[List[str]] = None
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) -> Dict[str, Any]:
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"""
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Recommend skills based on agent purpose and required artifacts.
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Args:
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agent_purpose: Description of agent purpose
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required_artifacts: Artifact types agent needs to work with
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Returns:
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Recommendation result with skills and rationale
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"""
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registry = load_registry()
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recommended = []
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rationale = {}
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# Keyword matching for purpose
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purpose_lower = agent_purpose.lower()
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keywords = {
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"api": ["api.define", "api.validate", "api.generate-models", "api.compatibility"],
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"workflow": ["workflow.validate", "workflow.compose"],
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"hook": ["hook.define"],
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"validate": ["api.validate", "workflow.validate"],
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"design": ["api.define"],
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}
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# Find skills by keywords
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matched_by_keyword = set()
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for keyword, skill_names in keywords.items():
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if keyword in purpose_lower:
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matched_by_keyword.update(skill_names)
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# Find skills by required artifacts
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matched_by_artifacts = set()
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if required_artifacts:
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artifact_skills = find_skills_by_artifacts(
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registry,
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produces=required_artifacts,
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consumes=required_artifacts
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)
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matched_by_artifacts.update(s["name"] for s in artifact_skills)
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# Combine matches
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all_matches = matched_by_keyword | matched_by_artifacts
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# Build recommendation with rationale
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skills = registry.get("skills", [])
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for skill in skills:
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skill_name = skill.get("name")
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if skill_name in all_matches:
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reasons = []
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if skill_name in matched_by_keyword:
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reasons.append(f"Purpose matches skill capabilities")
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artifacts = extract_artifact_metadata(skill)
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if required_artifacts:
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produces_match = artifacts["produces"] & set(required_artifacts)
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consumes_match = artifacts["consumes"] & set(required_artifacts)
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if produces_match:
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reasons.append(f"Produces: {', '.join(produces_match)}")
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if consumes_match:
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reasons.append(f"Consumes: {', '.join(consumes_match)}")
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recommended.append(skill_name)
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rationale[skill_name] = {
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"description": skill.get("description", ""),
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"reasons": reasons,
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"produces": list(artifacts["produces"]),
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"consumes": list(artifacts["consumes"])
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}
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return {
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"recommended_skills": recommended,
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"rationale": rationale,
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"total_recommended": len(recommended)
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}
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def analyze_artifact_flow(skills_metadata: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Analyze artifact flow between recommended skills.
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Args:
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skills_metadata: List of skill metadata
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Returns:
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Flow analysis showing how artifacts move between skills
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"""
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all_produces = set()
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all_consumes = set()
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flows = []
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for skill in skills_metadata:
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produces = set(skill.get("produces", []))
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consumes = set(skill.get("consumes", []))
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all_produces.update(produces)
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all_consumes.update(consumes)
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for artifact in produces:
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consumers = [
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s["name"] for s in skills_metadata
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if artifact in s.get("consumes", [])
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]
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if consumers:
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flows.append({
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"artifact": artifact,
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"producer": skill["name"],
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"consumers": consumers
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})
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# Find gaps (consumed but not produced)
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gaps = all_consumes - all_produces
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return {
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"flows": flows,
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"gaps": list(gaps),
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"fully_covered": len(gaps) == 0
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}
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def main():
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"""CLI entry point."""
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import argparse
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parser = argparse.ArgumentParser(
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description="Recommend skills for a Betty agent"
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)
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parser.add_argument(
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"agent_purpose",
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help="Description of what the agent should do"
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)
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parser.add_argument(
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"--required-artifacts",
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nargs="+",
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help="Artifact types the agent needs to work with"
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)
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parser.add_argument(
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"--output-format",
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choices=["yaml", "json", "markdown"],
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default="yaml",
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help="Output format"
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)
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args = parser.parse_args()
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logger.info(f"Finding skills for agent purpose: {args.agent_purpose}")
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try:
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# Get recommendations
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result = recommend_skills_for_purpose(
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args.agent_purpose,
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args.required_artifacts
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)
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# Analyze artifact flow
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skills_metadata = list(result["rationale"].values())
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for skill_name, metadata in result["rationale"].items():
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metadata["name"] = skill_name
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flow_analysis = analyze_artifact_flow(skills_metadata)
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result["artifact_flow"] = flow_analysis
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# Format output
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if args.output_format == "yaml":
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print("\n# Recommended Skills for Agent\n")
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print(f"# Purpose: {args.agent_purpose}\n")
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print("skills_available:")
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for skill in result["recommended_skills"]:
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print(f" - {skill}")
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print("\n# Rationale:")
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for skill_name, rationale in result["rationale"].items():
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print(f"\n# {skill_name}:")
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print(f"# {rationale['description']}")
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for reason in rationale["reasons"]:
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print(f"# - {reason}")
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elif args.output_format == "markdown":
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print(f"\n## Recommended Skills for: {args.agent_purpose}\n")
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print("### Skills\n")
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for skill in result["recommended_skills"]:
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rationale = result["rationale"][skill]
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print(f"**{skill}**")
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print(f"- {rationale['description']}")
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for reason in rationale["reasons"]:
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print(f" - {reason}")
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print()
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else: # json
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print(json.dumps(result, indent=2))
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# Show warnings for gaps
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if flow_analysis["gaps"]:
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logger.warning(f"\n⚠️ Artifact gaps detected:")
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for gap in flow_analysis["gaps"]:
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logger.warning(f" - '{gap}' is consumed but not produced")
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logger.warning(" Consider adding skills that produce these artifacts")
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logger.info(f"\n✅ Recommended {result['total_recommended']} skills")
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sys.exit(0)
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except Exception as e:
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logger.error(f"Failed to compose agent: {e}")
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result = {
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"ok": False,
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"status": "failed",
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"error": str(e)
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}
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print(json.dumps(result, indent=2))
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sys.exit(1)
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if __name__ == "__main__":
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main()
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