Mg CLI
modelguide/modelguide
Generate YAML configuration files and run CLI commands to onboard organizations into ModelGuide.
Best practices, patterns, and examples for building goal-driven agents.
$ npx skills add majiayu000/claude-skill-registry --skill building-agents-patterns -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install majiayu000/claude-skill-registry building-agents-patterns --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agent/building-agents-patterns .claude/skills/building-agents-patterns && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "building-agents-patterns" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/agent/building-agents-patterns into .claude/skills/building-agents-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-agents-patterns", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/majiayu000/claude-skill-registry/tree/main/skills/agent/building-agents-patternsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add majiayu000/claude-skill-registry --skill building-agents-patterns -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install majiayu000/claude-skill-registry building-agents-patterns --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/agent/building-agents-patterns .agents/skills/building-agents-patterns && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "building-agents-patterns" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/agent/building-agents-patterns into .agents/skills/building-agents-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-agents-patterns", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add majiayu000/claude-skill-registry --skill building-agents-patterns -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install majiayu000/claude-skill-registry building-agents-patterns --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/agent/building-agents-patterns .cursor/skills/building-agents-patterns && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "building-agents-patterns" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/agent/building-agents-patterns into .cursor/skills/building-agents-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-agents-patterns", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/majiayu000/claude-skill-registry.git --path skills/agent/building-agents-patterns--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add majiayu000/claude-skill-registry --skill building-agents-patterns -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install majiayu000/claude-skill-registry building-agents-patterns --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/agent/building-agents-patterns .gemini/skills/building-agents-patterns && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "building-agents-patterns" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/agent/building-agents-patterns into .gemini/skills/building-agents-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-agents-patterns", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install majiayu000/claude-skill-registry building-agents-patternsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add majiayu000/claude-skill-registry --skill building-agents-patterns -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/agent/building-agents-patterns .github/skills/building-agents-patterns && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "building-agents-patterns" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/agent/building-agents-patterns into .github/skills/building-agents-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-agents-patterns", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add majiayu000/claude-skill-registry --skill building-agents-patterns -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install majiayu000/claude-skill-registry building-agents-patterns --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/agent/building-agents-patterns .opencode/skills/building-agents-patterns && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "building-agents-patterns" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/agent/building-agents-patterns into .opencode/skills/building-agents-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-agents-patterns", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
building-agents-patternsBest practices, patterns, and examples for building goal-driven agents.
Building Agents Patterns is an agent skill from majiayu000/claude-skill-registry. Best practices, patterns, and examples for building goal-driven agents. Includes pause/resume architecture, hybrid workflows, anti-patterns, and handoff to testing. Use when optimizing agent design.
Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).
It sits in AI & LLM Engineering, covering Building AI agents. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is Apache-2.0.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 000116a. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Building Agents Patterns loads about 3.2k tokens when it runs. Until then it costs about 56 tokens; SKILL.md has 329 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from majiayu000/claude-skill-registry at commit 000116a, republished under its Apache-2.0 licence (© majiayu000). 329 words, ~3,214 tokens.
.claude/skills/building-agents-patterns/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Design patterns, examples, and best practices for building robust goal-driven agents.
Prerequisites: Complete agent structure using building-agents-construction.
How to build a node using both direct file writes and optional MCP validation:
# 1. WRITE TO FILE FIRST (Primary - makes it visible)
node_code = '''
search_node = NodeSpec(
id="search-web",
node_type="llm_tool_use",
input_keys=["query"],
output_keys=["search_results"],
system_prompt="Search the web for: {query}",
tools=["web_search"],
)
'''
Edit(
file_path="exports/research_agent/nodes/__init__.py",
old_string="# Nodes will be added here",
new_string=node_code
)
print("✅ Added search_node to nodes/__init__.py")
print("📁 Open exports/research_agent/nodes/__init__.py to see it!")
# 2. OPTIONALLY VALIDATE WITH MCP (Secondary - bookkeeping)
validation = mcp__agent-builder__test_node(
node_id="search-web",
test_input='{"query": "python tutorials"}',
mock_llm_response='{"search_results": [...mock results...]}'
)
print(f"✓ Validation: {validation['success']}")User experience:
This combines visibility (files) with validation (MCP tools).
For agents needing multi-turn conversations with user interaction:
# Define pause nodes - execution stops at these nodes
pause_nodes = ["request-clarification", "await-approval"]
# Define entry points - where to resume from each pause
entry_points = {
"start": "analyze-request", # Initial entry
"request-clarification_resume": "process-clarification", # Resume from clarification
"await-approval_resume": "execute-action", # Resume from approval
}# Nodes
nodes = [
NodeSpec(id="analyze-request", ...),
NodeSpec(id="request-clarification", ...), # PAUSE NODE
NodeSpec(id="process-clarification", ...),
NodeSpec(id="generate-results", ...),
NodeSpec(id="await-approval", ...), # PAUSE NODE
NodeSpec(id="execute-action", ...),
]
# Edges with resume flows
edges = [
EdgeSpec(
id="analyze-to-clarify",
source="analyze-request",
target="request-clarification",
condition=EdgeCondition.CONDITIONAL,
condition_expr="needs_clarification == true",
),
# When resumed, goes to process-clarification
EdgeSpec(
id="clarify-to-process",
source="request-clarification",
target="process-clarification",
condition=EdgeCondition.ALWAYS,
),
EdgeSpec(
id="results-to-approval",
source="generate-results",
target="await-approval",
condition=EdgeCondition.ALWAYS,
),
# When resumed, goes to execute-action
EdgeSpec(
id="approval-to-execute",
source="await-approval",
target="execute-action",
condition=EdgeCondition.ALWAYS,
),
]
# Configuration
pause_nodes = ["request-clarification", "await-approval"]
entry_points = {
"start": "analyze-request",
"request-clarification_resume": "process-clarification",
"await-approval_resume": "execute-action",
}# Initial run - will pause at first pause node
result1 = await agent.run(
context={"query": "research topic"},
session_state=None
)
# Check if paused
if result1.paused_at:
print(f"Paused at: {result1.paused_at}")
# Resume with user input
result2 = await agent.run(
context={"user_response": "clarification details"},
session_state=result1.session_state # Pass previous state
)❌ Don't rely on export_graph - Write files immediately, not at end
# BAD: Building in session state, exporting at end
mcp__agent-builder__add_node(...)
mcp__agent-builder__add_node(...)
mcp__agent-builder__export_graph() # Files appear only now
# GOOD: Writing files immediately
Write(file_path="...", content=node_code) # File visible now
Write(file_path="...", content=node_code) # File visible now❌ Don't hide code in session - Write to files as components approved
# BAD: Accumulating changes invisibly
session.add_component(component1)
session.add_component(component2)
# User can't see anything yet
# GOOD: Incremental visibility
Edit(file_path="...", ...) # User sees change 1
Edit(file_path="...", ...) # User sees change 2❌ Don't wait to write files - Agent visible from first step
# BAD: Building everything before writing
design_all_nodes()
design_all_edges()
write_everything_at_once()
# GOOD: Write as you go
write_package_structure() # Visible
write_goal() # Visible
write_node_1() # Visible
write_node_2() # Visible❌ Don't batch everything - Write incrementally
# BAD: Batching all nodes
nodes = [design_node_1(), design_node_2(), ...]
write_all_nodes(nodes)
# GOOD: One at a time with user feedback
write_node_1() # User approves
write_node_2() # User approves
write_node_3() # User approvesMCP tools OK for:
✅ test_node - Validate node configuration with mock inputs
✅ validate_graph - Check graph structure
✅ create_session - Track session state for bookkeeping
✅ Other validation tools
Just don't: Use MCP as the primary construction method or rely on export_graph
# After writing a node
print("✅ Added analyze_request_node to nodes/__init__.py")
print("📊 Progress: 1/6 nodes added")
print("📁 Open exports/my_agent/nodes/__init__.py to see it!")# Encourage file inspection
print("✅ Goal written to agent.py")
print("")
print("💡 Tip: Open exports/my_agent/agent.py in your editor to see the goal!")# Good flow
write_package_structure()
show_user("Package created")
write_goal()
show_user("Goal written")
for node in nodes:
get_approval(node)
write_node(node)
show_user(f"Node {node.id} written")# After adding several nodes
print("💡 You can test current state with:")
print(" PYTHONPATH=core:exports python -m my_agent validate")
print(" PYTHONPATH=core:exports python -m my_agent info")# Clear status updates
print("🔨 Creating package structure...")
print("✅ Package created: exports/my_agent/")
print("")
print("📝 Next: Define agent goal")For agents that run continuously without terminal nodes:
# No terminal nodes - loops forever
terminal_nodes = []
# Workflow loops back to start
edges = [
EdgeSpec(id="monitor-to-check", source="monitor", target="check-condition"),
EdgeSpec(id="check-to-wait", source="check-condition", target="wait"),
EdgeSpec(id="wait-to-monitor", source="wait", target="monitor"), # Loop
]
# Entry node only
entry_node = "monitor"
entry_points = {"start": "monitor"}
pause_nodes = []Example: File Monitor
nodes = [
NodeSpec(id="list-files", ...),
NodeSpec(id="check-new-files", node_type="router", ...),
NodeSpec(id="process-files", ...),
NodeSpec(id="wait-interval", node_type="function", ...),
]
edges = [
EdgeSpec(id="list-to-check", source="list-files", target="check-new-files"),
EdgeSpec(
id="check-to-process",
source="check-new-files",
target="process-files",
condition=EdgeCondition.CONDITIONAL,
condition_expr="new_files_count > 0",
),
EdgeSpec(
id="check-to-wait",
source="check-new-files",
target="wait-interval",
condition=EdgeCondition.CONDITIONAL,
condition_expr="new_files_count == 0",
),
EdgeSpec(id="process-to-wait", source="process-files", target="wait-interval"),
EdgeSpec(id="wait-to-list", source="wait-interval", target="list-files"), # Loop back
]
terminal_nodes = [] # No terminal - runs foreverrouter_node = NodeSpec(
id="decision-router",
node_type="router",
input_keys=["analysis_result"],
output_keys=["decision"],
system_prompt="""
Based on the analysis result, decide the next action:
- If confidence > 0.9: route to "execute"
- If 0.5 <= confidence <= 0.9: route to "review"
- If confidence < 0.5: route to "clarify"
Return: {"decision": "execute|review|clarify"}
""",
)
# Edges for each route
edges = [
EdgeSpec(
id="router-to-execute",
source="decision-router",
target="execute-action",
condition=EdgeCondition.CONDITIONAL,
condition_expr="decision == 'execute'",
priority=1,
),
EdgeSpec(
id="router-to-review",
source="decision-router",
target="human-review",
condition=EdgeCondition.CONDITIONAL,
condition_expr="decision == 'review'",
priority=2,
),
EdgeSpec(
id="router-to-clarify",
source="decision-router",
target="request-clarification",
condition=EdgeCondition.CONDITIONAL,
condition_expr="decision == 'clarify'",
priority=3,
),
]# Primary node with error handling
nodes = [
NodeSpec(id="api-call", max_retries=3, ...),
NodeSpec(id="fallback-cache", ...),
NodeSpec(id="report-error", ...),
]
edges = [
# Success path
EdgeSpec(
id="api-success",
source="api-call",
target="process-results",
condition=EdgeCondition.ON_SUCCESS,
),
# Fallback on failure
EdgeSpec(
id="api-to-fallback",
source="api-call",
target="fallback-cache",
condition=EdgeCondition.ON_FAILURE,
priority=1,
),
# Report if fallback also fails
EdgeSpec(
id="fallback-to-error",
source="fallback-cache",
target="report-error",
condition=EdgeCondition.ON_FAILURE,
priority=1,
),
]# Use multiple edges from same source for parallel execution
edges = [
EdgeSpec(
id="start-to-search1",
source="start",
target="search-source-1",
condition=EdgeCondition.ALWAYS,
),
EdgeSpec(
id="start-to-search2",
source="start",
target="search-source-2",
condition=EdgeCondition.ALWAYS,
),
EdgeSpec(
id="start-to-search3",
source="start",
target="search-source-3",
condition=EdgeCondition.ALWAYS,
),
# Converge results
EdgeSpec(
id="search1-to-merge",
source="search-source-1",
target="merge-results",
),
EdgeSpec(
id="search2-to-merge",
source="search-source-2",
target="merge-results",
),
EdgeSpec(
id="search3-to-merge",
source="search-source-3",
target="merge-results",
),
]When agent is complete, transition to testing phase:
print("""
✅ Agent complete: exports/my_agent/
Next steps:
1. Switch to testing-agent skill
2. Generate and approve tests
3. Run evaluation
4. Debug any failures
Command: "Test the agent at exports/my_agent/"
""")Before handing off to testing-agent:
python -m agent_name validatefrom exports.agent_name import default_agentRemember: Agent is actively constructed, visible the whole time. No hidden state. No surprise exports. Just transparent, incremental file building.
© majiayu000, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in skills/agent/building-agents-patterns of majiayu000/claude-skill-registry.
Open the folder on GitHubat commit 000116a
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.
Building Agents Patterns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Building Agents Patterns this skillmajiayu000/claude-skill-registry | 666 | 1 repos | ~3.2k | Automated safety check: Pass | Apache-2.0 | |
| Mg CLImodelguide/modelguide | 108 | — | ~3.5k | Automated safety check: Pass | MIT | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 6 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Create Agent Skillsglittercowboy/taches-cc-resources | 2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Agenticx Agent BuilderDemonDamon/AgenticX | 279 | — | ~893 | Automated safety check: Pass | Apache-2.0 | |
| Langgraph Agent Patternssoba-labs/langchain-agent-skills | 107 | — | ~3.6k | Automated safety check: Pass | MIT |
modelguide/modelguide
Generate YAML configuration files and run CLI commands to onboard organizations into ModelGuide.
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
glittercowboy/taches-cc-resources
Expert guidance for creating, writing, building, and refining Claude Code Skills.
DemonDamon/AgenticX
Guide for creating persistent Near desktop digital avatars (数字分身) via natural-language interview and the createavatar tool.
soba-labs/langchain-agent-skills
Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications.
Orchestra-Research/AI-Research-SKILLs
Guidance for using A-Evolve to improve an AI agent automatically, evolving its prompts, skills and memory against a benchmark through solve, observe and evolve cycles.
majiayu000/claude-skill-registry
Multi-source deep research using firecrawl and exa MCPs. An agent skill from majiayu000/claude-skill-registry.
majiayu000/claude-skill-registry
Neural search via Exa MCP for web, code, and company research.
majiayu000/claude-skill-registry
Unified media generation via fal.ai MCP — image, video, and audio.
majiayu000/claude-skill-registry
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server.
majiayu000/claude-skill-registry
Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner.
majiayu000/claude-skill-registry
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis.
Best practices, patterns, and examples for building goal-driven agents. Building Agents Patterns is an agent skill from majiayu000/claude-skill-registry. Best practices, patterns, and examples for building goal-driven agents.
Building Agents Patterns fits situations like: optimizing agent design; tasks that involve Building AI agents.
Run `npx skills add majiayu000/claude-skill-registry --skill building-agents-patterns -a claude-code`. Or copy the skill folder (skills/agent/building-agents-patterns in majiayu000/claude-skill-registry) into .claude/skills/building-agents-patterns in your project. Claude Code loads it when a task matches its description.
Run `npx skills add majiayu000/claude-skill-registry --skill building-agents-patterns -a codex`. Or copy the skill folder (skills/agent/building-agents-patterns in majiayu000/claude-skill-registry) into .agents/skills/building-agents-patterns in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add majiayu000/claude-skill-registry --skill building-agents-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/building-agents-patterns, .gemini/skills/building-agents-patterns, .github/skills/building-agents-patterns and .opencode/skills/building-agents-patterns in your project.
Going by SKILL.md and its folder, Building Agents Patterns needs the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Building Agents Patterns is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Building Agents Patterns: Mg CLI (modelguide/modelguide, 108 stars), Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Create Agent Skills (glittercowboy/taches-cc-resources, 2k stars) and Agenticx Agent Builder (DemonDamon/AgenticX, 279 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 971 skills in this directory. The repository was last updated on October 7, 2026.
Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.