File size: 8,518 Bytes
b5b9c2e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 | #!/usr/bin/env python3
"""Extract skill metadata from SKILL.md files and index caches into JSON."""
import json
import os
from collections import Counter
import yaml
REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
LOCAL_SKILL_DIRS = [
("skills", "built-in"),
("optional-skills", "optional"),
]
INDEX_CACHE_DIR = os.path.join(REPO_ROOT, "skills", "index-cache")
OUTPUT = os.path.join(REPO_ROOT, "website", "src", "data", "skills.json")
CATEGORY_LABELS = {
"apple": "Apple",
"autonomous-ai-agents": "AI Agents",
"blockchain": "Blockchain",
"communication": "Communication",
"creative": "Creative",
"data-science": "Data Science",
"devops": "DevOps",
"dogfood": "Dogfood",
"domain": "Domain",
"email": "Email",
"feeds": "Feeds",
"gaming": "Gaming",
"gifs": "GIFs",
"github": "GitHub",
"health": "Health",
"inference-sh": "Inference",
"leisure": "Leisure",
"mcp": "MCP",
"media": "Media",
"migration": "Migration",
"mlops": "MLOps",
"note-taking": "Note-Taking",
"productivity": "Productivity",
"red-teaming": "Red Teaming",
"research": "Research",
"security": "Security",
"smart-home": "Smart Home",
"social-media": "Social Media",
"software-development": "Software Dev",
"translation": "Translation",
"other": "Other",
}
SOURCE_LABELS = {
"anthropics_skills": "Anthropic",
"openai_skills": "OpenAI",
"claude_marketplace": "Claude Marketplace",
"lobehub": "LobeHub",
}
def extract_local_skills():
skills = []
for base_dir, source_label in LOCAL_SKILL_DIRS:
base_path = os.path.join(REPO_ROOT, base_dir)
if not os.path.isdir(base_path):
continue
for root, _dirs, files in os.walk(base_path):
if "SKILL.md" not in files:
continue
skill_path = os.path.join(root, "SKILL.md")
with open(skill_path) as f:
content = f.read()
if not content.startswith("---"):
continue
parts = content.split("---", 2)
if len(parts) < 3:
continue
try:
fm = yaml.safe_load(parts[1])
except yaml.YAMLError:
continue
if not fm or not isinstance(fm, dict):
continue
rel = os.path.relpath(root, base_path)
category = rel.split(os.sep)[0]
tags = []
metadata = fm.get("metadata")
if isinstance(metadata, dict):
hermes_meta = metadata.get("hermes", {})
if isinstance(hermes_meta, dict):
tags = hermes_meta.get("tags", [])
if not tags:
tags = fm.get("tags", [])
if isinstance(tags, str):
tags = [tags]
skills.append({
"name": fm.get("name", os.path.basename(root)),
"description": fm.get("description", ""),
"category": category,
"categoryLabel": CATEGORY_LABELS.get(category, category.replace("-", " ").title()),
"source": source_label,
"tags": tags or [],
"platforms": fm.get("platforms", []),
"author": fm.get("author", ""),
"version": fm.get("version", ""),
})
return skills
def extract_cached_index_skills():
skills = []
if not os.path.isdir(INDEX_CACHE_DIR):
return skills
for filename in os.listdir(INDEX_CACHE_DIR):
if not filename.endswith(".json"):
continue
filepath = os.path.join(INDEX_CACHE_DIR, filename)
try:
with open(filepath) as f:
data = json.load(f)
except (json.JSONDecodeError, OSError):
continue
stem = filename.replace(".json", "")
source_label = "community"
for key, label in SOURCE_LABELS.items():
if key in stem:
source_label = label
break
if isinstance(data, dict) and "agents" in data:
for agent in data["agents"]:
if not isinstance(agent, dict):
continue
skills.append({
"name": agent.get("identifier", agent.get("meta", {}).get("title", "unknown")),
"description": (agent.get("meta", {}).get("description", "") or "").split("\n")[0][:200],
"category": _guess_category(agent.get("meta", {}).get("tags", [])),
"categoryLabel": "", # filled below
"source": source_label,
"tags": agent.get("meta", {}).get("tags", []),
"platforms": [],
"author": agent.get("author", ""),
"version": "",
})
continue
if isinstance(data, list):
for entry in data:
if not isinstance(entry, dict) or not entry.get("name"):
continue
if "skills" in entry and isinstance(entry["skills"], list):
continue
skills.append({
"name": entry.get("name", ""),
"description": entry.get("description", ""),
"category": "uncategorized",
"categoryLabel": "",
"source": source_label,
"tags": entry.get("tags", []),
"platforms": [],
"author": "",
"version": "",
})
for s in skills:
if not s["categoryLabel"]:
s["categoryLabel"] = CATEGORY_LABELS.get(
s["category"],
s["category"].replace("-", " ").title() if s["category"] else "Uncategorized",
)
return skills
TAG_TO_CATEGORY = {}
for _cat, _tags in {
"software-development": [
"programming", "code", "coding", "software-development",
"frontend-development", "backend-development", "web-development",
"react", "python", "typescript", "java", "rust",
],
"creative": ["writing", "design", "creative", "art", "image-generation"],
"research": ["education", "academic", "research"],
"social-media": ["marketing", "seo", "social-media"],
"productivity": ["productivity", "business"],
"data-science": ["data", "data-science"],
"mlops": ["machine-learning", "deep-learning"],
"devops": ["devops"],
"gaming": ["gaming", "game", "game-development"],
"media": ["music", "media", "video"],
"health": ["health", "fitness"],
"translation": ["translation", "language-learning"],
"security": ["security", "cybersecurity"],
}.items():
for _t in _tags:
TAG_TO_CATEGORY[_t] = _cat
def _guess_category(tags: list) -> str:
if not tags:
return "uncategorized"
for tag in tags:
cat = TAG_TO_CATEGORY.get(tag.lower())
if cat:
return cat
return tags[0].lower().replace(" ", "-")
MIN_CATEGORY_SIZE = 4
def _consolidate_small_categories(skills: list) -> list:
for s in skills:
if s["category"] in ("uncategorized", ""):
s["category"] = "other"
s["categoryLabel"] = "Other"
counts = Counter(s["category"] for s in skills)
small_cats = {cat for cat, n in counts.items() if n < MIN_CATEGORY_SIZE}
for s in skills:
if s["category"] in small_cats:
s["category"] = "other"
s["categoryLabel"] = "Other"
return skills
def main():
local = extract_local_skills()
external = extract_cached_index_skills()
all_skills = _consolidate_small_categories(local + external)
source_order = {"built-in": 0, "optional": 1}
all_skills.sort(key=lambda s: (
source_order.get(s["source"], 2),
1 if s["category"] == "other" else 0,
s["category"],
s["name"],
))
os.makedirs(os.path.dirname(OUTPUT), exist_ok=True)
with open(OUTPUT, "w") as f:
json.dump(all_skills, f, indent=2)
print(f"Extracted {len(all_skills)} skills to {OUTPUT}")
print(f" {len(local)} local ({sum(1 for s in local if s['source'] == 'built-in')} built-in, "
f"{sum(1 for s in local if s['source'] == 'optional')} optional)")
print(f" {len(external)} from external indexes")
if __name__ == "__main__":
main()
|