inspecting-skills/SKILL.md
Discovers and indexes Python code in skills, enabling cross-skill imports. Use when importing functions from other skills or analyzing skill codebases.
npx skillsauth add oaustegard/claude-skills inspecting-skillsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Discover Python code across skills and enable universal imports. Solves the dash-underscore naming mismatch between skill directories (e.g., browsing-bluesky) and Python imports (e.g., browsing_bluesky).
import sys
sys.path.insert(0, '/home/user/claude-skills')
from inspecting_skills import setup_skill_path, skill_import
from inspecting_skills import skill_import
# Import by skill name (dash or underscore form)
bsky = skill_import("browsing-bluesky")
posts = bsky.search_posts("python")
# Import specific functions
search, profile = skill_import("browsing-bluesky", ["search_posts", "get_profile"])
from inspecting_skills import setup_skill_path
# Configure once at session start
setup_skill_path("/home/user/claude-skills")
# Now import skills directly (underscore form)
from browsing_bluesky import search_posts, get_profile
from remembering import remember, recall
from inspecting_skills import list_importable_skills
skills = list_importable_skills()
for s in skills:
print(f"{s['name']} -> import {s['module_name']}")
| Function | Purpose |
|----------|---------|
| discover_skill(path) | Analyze a single skill directory |
| discover_all_skills(root) | Find all skills with Python code |
| find_skill_by_name(name, root) | Find skill by name (either form) |
| skill_name_to_module(name) | Convert "browsing-bluesky" to "browsing_bluesky" |
| Function | Purpose |
|----------|---------|
| index_skill(layout) | Extract symbols from a discovered skill |
| index_all_skills(root) | Index all skills in repository |
| generate_registry(root, output) | Create registry.json manifest |
| Function | Purpose |
|----------|---------|
| setup_skill_path(root) | Enable transparent skill imports |
| skill_import(name, symbols) | Import skill or specific symbols |
| register_skill(name, path) | Register skill at custom path |
| list_importable_skills() | List all importable skills |
Skills organize Python code in three patterns:
browsing-bluesky/
SKILL.md
__init__.py # Re-exports from scripts/
scripts/
__init__.py
bsky.py # Main implementation
remembering/
SKILL.md
__init__.py # Re-exports functions
memory.py # Core functionality
boot.py
config.py
simple-skill/
SKILL.md
__init__.py # Contains all code
Create a registry.json for offline symbol lookup:
from inspecting_skills import generate_registry
from pathlib import Path
registry = generate_registry(
Path("/home/user/claude-skills"),
output_path=Path("registry.json")
)
# Registry structure:
# {
# "version": "1.0.0",
# "skills": {
# "browsing-bluesky": {
# "module_name": "browsing_bluesky",
# "exports": ["search_posts", "get_profile", ...],
# "modules": [...]
# }
# }
# }
from inspecting_skills import discover_skill, index_skill
from pathlib import Path
# Discover the skill layout
layout = discover_skill(Path("/home/user/claude-skills/remembering"))
print(f"Layout: {layout.layout_type}")
print(f"Has __init__.py: {layout.has_init}")
print(f"Python files: {[f.name for f in layout.python_files]}")
# Index symbols
index = index_skill(layout)
for module in index.modules:
print(f"\n{module.file_path}:")
for sym in module.symbols:
print(f" {sym.kind} {sym.name}{sym.signature or ''}")
This skill complements mapping-codebases which generates _MAP.md files:
Use both together:
mapping-codebases for navigation and code reviewinspecting-skills for actual code imports and execution# If skill_import fails, check:
# 1. Skill exists and has __init__.py
from inspecting_skills import discover_skill
layout = discover_skill(Path("/path/to/skill"))
print(layout.has_init) # Must be True for importing
# 2. Skills root is configured
from inspecting_skills import get_skills_root
print(get_skills_root())
# 3. Symbol is exported in __all__
import ast
init_code = open("/path/to/skill/__init__.py").read()
# Check for __all__ definition
# Manually set skills root
from inspecting_skills import set_skills_root
set_skills_root("/home/user/claude-skills")
@dataclass
class SkillLayout:
name: str # "browsing-bluesky"
path: Path # Full path to skill directory
layout_type: str # "scripts" | "root" | "package" | "none"
python_files: list[Path]
has_init: bool # Can be imported as package
entry_module: str # "browsing_bluesky"
@dataclass
class SkillIndex:
name: str # "browsing-bluesky"
module_name: str # "browsing_bluesky"
layout_type: str
modules: list[ModuleIndex]
exports: list[str] # From __all__
@dataclass
class Symbol:
name: str # Function/class name
kind: str # "function" | "class" | "method"
signature: str | None # "(self, x: int)"
line: int | None # 1-indexed
docstring: str | None # First line
children: list[Symbol] # Methods for classes
testing
Disciplined, validation-gated revision of an EXISTING skill so each edit is a measured improvement rather than a guess. Use when editing, revising, or tuning a skill that already exists and there is evidence it underperforms (observed failures, drift, complaints) — invoke by name, or have versioning-skills / creating-skill defer to it before applying edits. Not for authoring a brand-new skill from scratch (use creating-skill) or one-off prose.
development
Skill-aware orchestration with context routing. Decomposes complex tasks into skill-typed subtasks, extracts targeted context subsets, executes subagents in parallel, and synthesizes results. Self-answers trivial lookups inline. No SDK dependency — uses raw HTTP via httpx. Use when tasks require multiple analytical perspectives, when context is large and subtasks only need portions, or when orchestrating-agents spawns too many redundant subagents.
tools
Orchestrates parallel API instances, delegated sub-tasks, and multi-agent workflows with streaming and tool-enabled delegation patterns. Use for parallel analysis, multi-perspective reviews, or complex task decomposition.
development
Invokes Google Gemini models for structured outputs, image generation, multi-modal tasks, and Google-specific features. Use when users request Gemini, image generation, structured JSON output, Google API integration, or cost-effective parallel processing.