Complete workflow for building, implementing, and testing goal-driven agents.

Apache-2.0Auto-check passed

Install Hive

skills CLI
$ npx skills add majiayu000/claude-skill-registry --skill hive -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install majiayu000/claude-skill-registry hive --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agent/hive-adenhq-hive .claude/skills/hive && rm -rf skills-src

Use ~/.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/

Facts

Skill name
hive
GitHub stars
666
Used in
1 other repo
Token cost
~4k tokens
SKILL.md length
1,383 words
Files
2
Skills in repo
971
Repo updated
First seen
Licence
Apache-2.0

At a glance

Complete workflow for building, implementing, and testing goal-driven agents.

  • Works in 4 steps: Understand Concepts (Optional) → Build Agent Structure → 5: Optimize Design (Optional) → …
  • Starting a new agent project
  • SKILL.md covers Overview, When to Use This Workflow, Quick Decision Tree and Phase 0: Understand Concepts…, plus 5 more sections
  • Calls uv

What it does

Hive is an agent skill from majiayu000/claude-skill-registry. Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates hive- skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).

The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is Apache-2.0.

When your agent uses it

  • Starting a new agent project
  • Unsure which skill to use
  • Need end-to-end guidance

Example prompts

  • “/hive”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Understand Concepts (Optional)
  2. Build Agent Structure
  3. 5: Optimize Design (Optional)
  4. Test & Validate

What it can do on your machine

Read from SKILL.md and the folder at commit 000116a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hive loads about 4k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 1,383 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~4k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from majiayu000/claude-skill-registry at commit 000116a, republished under its Apache-2.0 licence (© majiayu000). 1,383 words, ~3,960 tokens.

Download SKILL.mdSave it as .claude/skills/hive/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
hive
description
Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates hive-* skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.
license
Apache-2.0
metadata.author
hive
metadata.version
2.0
metadata.type
workflow-orchestrator
metadata.orchestrates
hive-concepts, hive-create, hive-patterns, hive-test, hive-credentials, hive-debugger

Agent Development Workflow

THIS IS AN EXECUTABLE WORKFLOW. DO NOT explore the codebase or read source files. ROUTE to the correct skill IMMEDIATELY.

When this skill is loaded, ALWAYS use the AskUserQuestion tool to present options:

Use AskUserQuestion with these options:
- "Build a new agent" → Then invoke /hive-create
- "Test an existing agent" → Then invoke /hive-test
- "Learn agent concepts" → Then invoke /hive-concepts
- "Optimize agent design" → Then invoke /hive-patterns
- "Set up credentials" → Then invoke /hive-credentials
- "Debug a failing agent" → Then invoke /hive-debugger
- "Other" (please describe what you want to achieve)

DO NOT: Read source files, explore the codebase, search for code, or do any investigation before routing. The sub-skills handle all of that.


Complete Standard Operating Procedure (SOP) for building production-ready goal-driven agents.

Overview

This workflow orchestrates specialized skills to take you from initial concept to production-ready agent:

  1. Understand Concepts → /hive-concepts (optional)
  2. Build Structure → /hive-create
  3. Optimize Design → /hive-patterns (optional)
  4. Setup Credentials → /hive-credentials (if agent uses tools requiring API keys)
  5. Test & Validate → /hive-test
  6. Debug Issues → /hive-debugger (if agent fails at runtime)

When to Use This Workflow

Use this meta-skill when:

  • Starting a new agent from scratch
  • Unclear which skill to use first
  • Need end-to-end guidance for agent development
  • Want consistent, repeatable agent builds

Skip this workflow if:

  • You only need to test an existing agent → use /hive-test directly
  • You know exactly which phase you're in → use specific skill directly

Quick Decision Tree

"Need to understand agent concepts" → hive-concepts
"Build a new agent" → hive-create
"Optimize my agent design" → hive-patterns
"Need client-facing nodes or feedback loops" → hive-patterns
"Set up API keys for my agent" → hive-credentials
"Test my agent" → hive-test
"My agent is failing/stuck/has errors" → hive-debugger
"Not sure what I need" → Read phases below, then decide
"Agent has structure but needs implementation" → See agent directory STATUS.md

Phase 0: Understand Concepts (Optional)

Skill: /hive-concepts Input: Questions about agent architecture

When to Use
  • First time building an agent
  • Need to understand node types, edges, goals
  • Want to validate tool availability
  • Learning about event loop architecture and client-facing nodes
What This Phase Provides
  • Architecture overview (Python packages, not JSON)
  • Core concepts (Goal, Node, Edge, Event Loop, Judges)
  • Tool discovery and validation procedures
  • Workflow overview

Skip this phase if you already understand agent fundamentals.

Phase 1: Build Agent Structure

Skill: /hive-create Input: User requirements ("Build an agent that...") or a template to start from

What This Phase Does

Creates the complete agent architecture:

  • Package structure (exports/agent_name/)
  • Goal with success criteria and constraints
  • Workflow graph (nodes and edges)
  • Node specifications
  • CLI interface
  • Documentation
Process
  1. Create package - Directory structure with skeleton files
  2. Define goal - Success criteria and constraints written to agent.py
  3. Design nodes - Each node approved and written incrementally
  4. Connect edges - Workflow graph with conditional routing
  5. Finalize - Agent class, exports, and documentation
Outputs
  • ✅ exports/agent_name/ package created
  • ✅ Goal defined in agent.py
  • ✅ 3-5 success criteria defined
  • ✅ 1-5 constraints defined
  • ✅ 5-10 nodes specified in nodes/init.py
  • ✅ 8-15 edges connecting workflow
  • ✅ Validated structure (passes uv run python -m agent_name validate)
  • ✅ README.md with usage instructions
  • ✅ CLI commands (info, validate, run, shell)
Success Criteria

You're ready for Phase 2 when:

  • Agent structure validates without errors
  • All nodes and edges are defined
  • CLI commands work (info, validate)
  • You see: "Agent complete: exports/agent_name/"
Common Outputs

The hive-create skill produces:

exports/agent_name/
├── __init__.py          (package exports)
├── __main__.py          (CLI interface)
├── agent.py             (goal, graph, agent class)
├── nodes/__init__.py    (node specifications)
├── config.py            (configuration)
├── implementations.py   (may be created for Python functions)
└── README.md            (documentation)
Next Steps

If structure complete and validated: → Check exports/agent_name/STATUS.md or IMPLEMENTATION_GUIDE.md → These files explain implementation options → You may need to add Python functions or MCP tools (not covered by current skills)

If want to optimize design: → Proceed to Phase 1.5 (hive-patterns)

If ready to test: → Proceed to Phase 2

Phase 1.5: Optimize Design (Optional)

Skill: /hive-patterns Input: Completed agent structure

When to Use
  • Want to add client-facing blocking or feedback edges
  • Need judge patterns for output validation
  • Want fan-out/fan-in (parallel execution)
  • Need error handling patterns
  • Want best practices guidance
What This Phase Provides
  • Client-facing interaction patterns
  • Feedback edge routing with nullable output keys
  • Judge patterns (implicit, SchemaJudge)
  • Fan-out/fan-in parallel execution
  • Context management and spillover patterns
  • Anti-patterns to avoid

Skip this phase if your agent design is straightforward.

Phase 2: Test & Validate

Skill: /hive-test Input: Working agent from Phase 1

What This Phase Does

Guides the creation and execution of a comprehensive test suite:

  • Constraint tests
  • Success criteria tests
  • Edge case tests
  • Integration tests
Process
  1. Analyze agent - Read goal, constraints, success criteria
  2. Generate tests - The calling agent writes pytest files in exports/agent_name/tests/ using hive-test guidelines and templates
  3. User approval - Review and approve each test
  4. Run evaluation - Execute tests and collect results
  5. Debug failures - Identify and fix issues
  6. Iterate - Repeat until all tests pass
Outputs
  • ✅ Test files in exports/agent_name/tests/
  • ✅ Test report with pass/fail metrics
  • ✅ Coverage of all success criteria
  • ✅ Coverage of all constraints
  • ✅ Edge case handling verified
Success Criteria

You're done when:

  • All tests pass
  • All success criteria validated
  • All constraints verified
  • Agent handles edge cases
  • Test coverage is comprehensive
Next Steps

Agent ready for:

  • Production deployment
  • Integration into larger systems
  • Documentation and handoff
  • Continuous monitoring

Phase Transitions

From Phase 1 to Phase 2

Trigger signals:

  • "Agent complete: exports/..."
  • Structure validation passes
  • README indicates implementation complete

Before proceeding:

  • Verify agent can be imported: from exports.agent_name import default_agent
  • Check if implementation is needed (see STATUS.md or IMPLEMENTATION_GUIDE.md)
  • Confirm agent executes without import errors
Skipping Phases

When to skip Phase 1:

  • Agent structure already exists
  • Only need to add tests
  • Modifying existing agent

When to skip Phase 2:

  • Prototyping or exploring
  • Agent not production-bound
  • Manual testing sufficient

Common Patterns

Pattern 1: Complete New Build (Simple)
User: "Build an agent that monitors files"
→ Use /hive-create
→ Agent structure created
→ Use /hive-test
→ Tests created and passing
→ Done: Production-ready agent
Pattern 1b: Complete New Build (With Learning)
User: "Build an agent (first time)"
→ Use /hive-concepts (understand concepts)
→ Use /hive-create (build structure)
→ Use /hive-patterns (optimize design)
→ Use /hive-test (validate)
→ Done: Production-ready agent
Pattern 1c: Build from Template
User: "Build an agent based on the deep research template"
→ Use /hive-create
→ Select "From a template" path
→ Pick template, name new agent
→ Review/modify goal, nodes, graph
→ Agent exported with customizations
→ Use /hive-test
→ Done: Customized agent
Pattern 2: Test Existing Agent
User: "Test my agent at exports/my_agent"
→ Skip Phase 1
→ Use /hive-test directly
→ Tests created
→ Done: Validated agent
Pattern 3: Iterative Development
User: "Build an agent"
→ Use /hive-create (Phase 1)
→ Implementation needed (see STATUS.md)
→ [User implements functions]
→ Use /hive-test (Phase 2)
→ Tests reveal bugs
→ [Fix bugs manually]
→ Re-run tests
→ Done: Working agent
Pattern 4: Agent with Review Loops and HITL Checkpoints
User: "Build an agent with human review and feedback loops"
→ Use /hive-concepts (learn event loop, client-facing nodes)
→ Use /hive-create (build structure with feedback edges)
→ Use /hive-patterns (implement client-facing + feedback patterns)
→ Use /hive-test (validate review flows and edge routing)
→ Done: Agent with HITL checkpoints and review loops

Skill Dependencies

hive (meta-skill)
    │
    ├── hive-concepts (foundational)
    │   ├── Architecture concepts (event loop, judges)
    │   ├── Node types (event_loop, function)
    │   ├── Edge routing and priority
    │   ├── Tool discovery procedures
    │   └── Workflow overview
    │
    ├── hive-create (procedural)
    │   ├── Creates package structure
    │   ├── Defines goal
    │   ├── Adds nodes (event_loop, function)
    │   ├── Connects edges with priority routing
    │   ├── Finalizes agent class
    │   └── Requires: hive-concepts
    │
    ├── hive-patterns (reference)
    │   ├── Client-facing interaction patterns
    │   ├── Feedback edges and review loops
    │   ├── Judge patterns (implicit, SchemaJudge)
    │   ├── Fan-out/fan-in parallel execution
    │   └── Context management and anti-patterns
    │
    ├── hive-credentials (utility)
    │   ├── Detects missing credentials
    │   ├── Offers auth method choices (Aden OAuth, direct API key)
    │   ├── Stores securely in ~/.hive/credentials
    │   └── Validates with health checks
    │
    ├── hive-test (validation)
    │   ├── Reads agent goal
    │   ├── Generates tests
    │   ├── Runs evaluation
    │   └── Reports results
    │
    └── hive-debugger (troubleshooting)
        ├── Monitors runtime logs (L1/L2/L3)
        ├── Identifies retry loops, tool failures
        ├── Categorizes issues (10 categories)
        └── Provides fix recommendations

Troubleshooting

Show full SKILL.md (557 more words)Show less
"Agent structure won't validate"
  • Check node IDs match between nodes/init.py and agent.py
  • Verify all edges reference valid node IDs
  • Ensure entry_node exists in nodes list
  • Run: PYTHONPATH=exports uv run python -m agent_name validate
"Agent has structure but won't run"
  • Check for STATUS.md or IMPLEMENTATION_GUIDE.md in agent directory
  • Implementation may be needed (Python functions or MCP tools)
  • This is expected - hive-create creates structure, not implementation
  • See implementation guide for completion options
"Tests are failing"
  • Review test output for specific failures
  • Check agent goal and success criteria
  • Verify constraints are met
  • Use /hive-test to debug and iterate
  • Fix agent code and re-run tests
"Agent is failing at runtime"
  • Use /hive-debugger to analyze runtime logs
  • The debugger identifies retry loops, tool failures, and stalled execution
  • Get actionable fix recommendations with code changes
  • Monitor the agent in real-time during TUI sessions
"Not sure which phase I'm in"

Run these checks:

bash
# Check if agent structure exists
ls exports/my_agent/agent.py

# Check if it validates
PYTHONPATH=exports uv run python -m my_agent validate

# Check if tests exist
ls exports/my_agent/tests/

# If structure exists and validates → Phase 2 (testing)
# If structure doesn't exist → Phase 1 (building)
# If tests exist but failing → Debug phase

Best Practices

For Phase 1 (Building)
  1. Start with clear requirements - Know what the agent should do
  2. Define success criteria early - Measurable goals drive design
  3. Keep nodes focused - One responsibility per node
  4. Use descriptive names - Node IDs should explain purpose
  5. Validate incrementally - Check structure after each major addition
For Phase 2 (Testing)
  1. Test constraints first - Hard requirements must pass
  2. Mock external dependencies - Use mock mode for LLMs/APIs
  3. Cover edge cases - Test failures, not just success paths
  4. Iterate quickly - Fix one test at a time
  5. Document test patterns - Future tests follow same structure
General Workflow
  1. Use version control - Git commit after each phase
  2. Document decisions - Update README with changes
  3. Keep iterations small - Build → Test → Fix → Repeat
  4. Preserve working states - Tag successful iterations
  5. Learn from failures - Failed tests reveal design issues

Exit Criteria

You're done with the workflow when:

✅ Agent structure validates ✅ All tests pass ✅ Success criteria met ✅ Constraints verified ✅ Documentation complete ✅ Agent ready for deployment

Additional Resources

  • hive-concepts: See .claude/skills/hive-concepts/SKILL.md
  • hive-create: See .claude/skills/hive-create/SKILL.md
  • hive-patterns: See .claude/skills/hive-patterns/SKILL.md
  • hive-test: See .claude/skills/hive-test/SKILL.md
  • Agent framework docs: See core/README.md
  • Example agents: See exports/ directory

Summary

This workflow provides a proven path from concept to production-ready agent:

  1. Learn with /hive-concepts → Understand fundamentals (optional)
  2. Build with /hive-create → Get validated structure
  3. Optimize with /hive-patterns → Apply best practices (optional)
  4. Configure with /hive-credentials → Set up API keys (if needed)
  5. Test with /hive-test → Get verified functionality
  6. Debug with /hive-debugger → Fix runtime issues (if needed)

The workflow is flexible - skip phases as needed, iterate freely, and adapt to your specific requirements. The goal is production-ready agents built with consistent, repeatable processes.

Skill Selection Guide

Choose hive-concepts when:

  • First time building agents
  • Need to understand event loop architecture
  • Validating tool availability
  • Learning about node types, edges, and judges

Choose hive-create when:

  • Actually building an agent
  • Have clear requirements
  • Ready to write code
  • Want step-by-step guidance
  • Want to start from an existing template and customize it

Choose hive-patterns when:

  • Agent structure complete
  • Need client-facing nodes or feedback edges
  • Implementing review loops or fan-out/fan-in
  • Want judge patterns or context management
  • Want best practices

Choose hive-test when:

  • Agent structure complete
  • Ready to validate functionality
  • Need comprehensive test coverage
  • Testing feedback loops, output keys, or fan-out

Choose hive-debugger when:

  • Agent is failing or stuck at runtime
  • Seeing retry loops or escalations
  • Tool calls are failing
  • Need to understand why a node isn't completing
  • Want real-time monitoring of agent execution

© 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

Files

SKILL.md and 1 other file in skills/agent/hive-adenhq-hive of majiayu000/claude-skill-registry.

  • SKILL.md
  • metadata.json

Open the folder on GitHubat commit 000116a

Used in 1 other repository

We found 2 copies 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.

Compare with similar skills

Hive 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.

Hive compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
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Team Agent Orchestrationaffaan-m/ECC274k1 repos~1.2kAutomated safety check: PassMIT
Orca Orchestrationstablyai/orca87k—~916Automated safety check: PassMIT
Agent Orchestrator Taskruvnet/ruflo74k3 repos~1kAutomated safety check: PassMIT
Plan Orchestrateaffaan-m/ECC274k1 repos~4.5kAutomated safety check: PassMIT

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Questions about Hive

What does Hive do?

Complete workflow for building, implementing, and testing goal-driven agents. Hive is an agent skill from majiayu000/claude-skill-registry. Complete workflow for building, implementing, and testing goal-driven agents.

When should I use Hive?

Hive fits situations like: starting a new agent project; unsure which skill to use; need end-to-end guidance.

How do I install Hive in Claude Code?

Run `npx skills add majiayu000/claude-skill-registry --skill hive -a claude-code`. Or copy the skill folder (skills/agent/hive-adenhq-hive in majiayu000/claude-skill-registry) into .claude/skills/hive in your project. Claude Code loads it when a task matches its description.

How do I install Hive in Codex?

Run `npx skills add majiayu000/claude-skill-registry --skill hive -a codex`. Or copy the skill folder (skills/agent/hive-adenhq-hive in majiayu000/claude-skill-registry) into .agents/skills/hive in your project. Codex loads it when a task matches its description.

Can I use Hive in Cursor, Gemini CLI or GitHub Copilot?

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 hive -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hive, .gemini/skills/hive, .github/skills/hive and .opencode/skills/hive in your project.

What does Hive need to run?

Going by SKILL.md and its folder, Hive needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Hive access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Hive safe to install?

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.

What licence does Hive use?

Hive 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.

How many tokens does Hive use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Hive?

Skills that share tags, products or a category with Hive: Goals (codewhale-hq/Codewhale, 41k stars), Team Agent Orchestration (affaan-m/ECC, 274k stars), Orca Orchestration (stablyai/orca, 87k stars) and Agent Orchestrator Task (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hive?

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.