AgentForge

Python 3.11+ License: MIT No API Key Powered by Claude Code Claude Code Skill

What to Build in the Agent Era — AI 에이전트 시대의 선택장애 해결 프레임워크

No API key needed. AgentForge runs entirely through your locally installed Claude Code.

AgentForge helps Claude Code users and AI power-users overcome decision paralysis about what to build next. It combines personal ontology mapping, local environment scanning, GitHub trend analysis, and Claude-powered recommendations to produce a personalized, immediately actionable MVP — all using your existing Claude Code session.

You use Claude Code every day. You see the potential. But when it comes to what to build, you freeze.

• Too many ideas, no framework to evaluate them
• You don't know what gaps exist in the ecosystem
• You don't know which opportunities fit your specific skills
• Even when you pick something, starting from scratch is slow

AgentForge solves all of this in one automated pipeline.

 AgentForge Pipeline
 ===================
 [You]
 |
 v
+-------------------+
| 1. Ontology Survey| 20 questions about skills, domain,
| (20 questions) | pain points, time, risk, motivation
+-------------------+
 |
 | Personal Ontology JSON
 v
+-------------------+
| 2. Env Scanner | pip packages, Claude skills, MCP servers,
| | VS Code extensions, npm globals, git repos
+-------------------+
 |
 | ToolProfile
 v
+-------------------+
| 3. GitHub Search | Trending AI/agent repos across 5 categories
| | (1000+ stars, active in last 90 days)
+-------------------+
 |
 | RepoInfo[]
 v
+-------------------+
| 4. Gap Analysis | Claude analyzes ecosystem for gaps,
| (Claude Code) | pain themes, and opportunities
+-------------------+
 |
 | GapAnalysis
 v
+-------------------+
| 5. Recommender | Combines ontology + tools + gaps
| (Claude Code) | -> Top 5 personalized recommendations
+-------------------+
 |
 | [User selects #N]
 v
+-------------------+
| 6. Data Collector | Fetches GitHub issues, competitive analysis,
| | integration opportunities, MVP scope
+-------------------+
 |
 | ProjectBrief
 v
+-------------------+
| 7. MVP Builder | Claude generates complete working codebase:
| (Claude Code) | Python files, README, tests, Makefile
+-------------------+
 |
 v
 [Runnable MVP in your output directory]

• Python 3.11+
• Claude Code installed and logged in
• (Optional) GITHUB_TOKEN for higher GitHub API rate limits

No ANTHROPIC_API_KEY needed. AgentForge uses your Claude Code session directly.

git clone https://github.com/AlexAI-MCP/AgentForge
cd AgentForge
pip install -e .

# Run the full pipeline (no API key needed!)
agentforge run
# Answer 20 questions, get personalized recommendations, build your MVP

That's it. AgentForge handles everything else using your local Claude Code.

Real output from AlexLee's profile (A- · 91/100):

#1 ConstructIQ Korea FIT:9.8 MARKET:8.5 Medium 8weeks
 건설 현장의 모든 지식을 AI로 연결하는 건설 특화 GraphRAG 플랫폼
#2 OntologyMe FIT:9.2 MARKET:7.8 Medium 6weeks
 한국 최초 퍼스널 지식 그래프 SaaS
#3 MCPHub Korea FIT:8.7 MARKET:8.2 Hard 10weeks
 한국어 MCP 스킬 마켓플레이스
#4 GraphRAG Enterprise FIT:9.0 MARKET:9.0 Hard 12weeks
 한국 기업 문서를 위한 엔터프라이즈 GraphRAG 레이어
#5 AI Community OS FIT:9.5 MARKET:7.2 Easy 4weeks
 1200명 Discord 커뮤니티를 위한 지식 운영체제

agentforge run

Runs all 6 steps interactively. At step 5 you choose which recommendation to build. All intermediate results are saved so you can re-run individual steps.

Options:

--skip-scan Skip the local environment scan
--skip-github Skip GitHub search (use cached results)
--auto-build Automatically build the top recommendation

# Run just the 20-question survey and build your Personal Ontology
agentforge survey
# Scan your local development environment
agentforge scan
# Search GitHub for trending AI repos and run gap analysis
agentforge search
agentforge search --days 60 --limit 15 # custom parameters
# Generate recommendations (requires prior survey)
agentforge recommend
# Build MVP for a specific recommendation
agentforge build 1 # build recommendation #1
agentforge build # interactive prompt to choose

Twenty questions covering:

Claude then synthesizes these into a Personal Ontology — a structured JSON map of your strengths, gaps, opportunities, builder style, and ideal project traits.

Non-destructive local scan that detects:

• Python packages (pip list)
• Anthropic, OpenAI, LangChain SDKs
• Claude Code skills (~/.claude/skills/)
• MCP server configurations
• VS Code extensions
• npm global packages
• Nearby git repositories
• Available CLI tools (git, docker, node)

Searches 5 categories across 4 queries each (20 total searches):

• agent-frameworks — multi-agent orchestration
• llm-tools — prompt engineering, RAG, embeddings
• automation — workflow, RPA, computer use
• developer-tools — code gen, AI assistants
• data-pipelines — vector DBs, knowledge graphs

Returns up to 50 deduplicated repos sorted by stars, filtered to repos active in the last N days.

Claude reads the full repo list and identifies:

• Recurring pain themes in open issues
• "Good but incomplete" frameworks
• High-demand / low-quality-supply areas
• Saturated areas to avoid
• Ranked opportunities with potential scores

Combines all three inputs (ontology + tools + gaps) and Claude generates 5 personalized recommendations, each with:

• Project name and concept
• Why it fits you specifically (not generic advice)
• Market opportunity score (1-10)
• Technical difficulty score (1-10)
• Estimated MVP timeline in weeks
• Similar existing projects to differentiate from
• Concrete first steps (immediately actionable)
• Monetization path and risk factors

Once you choose a recommendation:

• Fetches open issues from similar/competing projects
• Classifies issue themes (missing features, bugs, doc gaps)
• Claude synthesizes a detailed ProjectBrief including:
  • Precise MVP scope (what to build first)
  • Pain-point evidence from real users
  • Competitive landscape with specific weaknesses
  • Integration opportunities
  • Success metrics
  • Risk/mitigation pairs

Claude generates a complete, runnable Python project:

• Full package structure with working code
• CLI entry point
• README with setup instructions
• pyproject.toml / requirements.txt
• .env.example
• Makefile
• pytest test suite
• All files written to a timestamped output directory

agentforge/
├── __init__.py
├── cli.py Click CLI with 5 commands
├── config.py Pydantic config, env var loading
├── ontology/
│ ├── survey.py 20-question interactive survey
│ └── builder.py Claude-powered ontology synthesis
├── scanner/
│ └── tools.py Local environment scanner
├── github/
│ ├── searcher.py Async GitHub API client
│ └── analyzer.py Claude-powered gap analysis
├── recommender/
│ └── engine.py Personalized recommendation engine
├── collector/
│ └── data.py GitHub issue research + brief generation
└── mvp/
 └── builder.py Claude-powered MVP code generation

All data flows as typed Pydantic/dataclass objects. Claude is called at 4 stages (ontology, gap analysis, recommendations, MVP generation). All intermediate outputs are persisted as JSON in ./agentforge_output/ so individual steps can be re-run without repeating expensive API calls.

After a full run, ./agentforge_output/ contains:

agentforge_output/
├── survey.json Your survey answers
├── ontology.json Your Personal Ontology
├── tool_profile.json Environment scan results
├── github_repos.json Fetched GitHub repositories
├── gap_analysis.json Ecosystem gap analysis
├── recommendations.json Your 5 recommendations
├── <project>_brief.md Project brief (Markdown)
├── <project>_brief.json Project brief (JSON)
└── <project>_YYYYMMDD_HHMMSS/ Generated MVP codebase
 ├── README.md
 ├── pyproject.toml
 ├── requirements.txt
 ├── .env.example
 ├── Makefile
 ├── <package>/
 │ └── *.py
 └── tests/
 └── *.py

All settings via environment variables or .env file:

Get your Anthropic API key at console.anthropic.com.

Get a GitHub token at github.com/settings/tokens (no permissions needed — public repo read is sufficient).

• Python 3.11+
• anthropic>=0.40.0
• click>=8.1.0
• rich>=13.0.0
• httpx>=0.27.0
• pydantic>=2.0.0
• python-dotenv>=1.0.0
• jinja2>=3.1.0

AgentForge는 Claude Code를 사용하는 한국 개발자들을 위해 특별히 설계되었습니다.

설문조사는 한국어로 진행됩니다 (영어 부제목 포함).

추천 시스템은 한국 시장의 특성을 고려합니다:

• 한국어 우선 시장의 기회
• 국내 개발자 커뮤니티의 미충족 수요
• 글로벌 진출을 위한 영어 도구화 전략

AI 에이전트 시대에 무엇을 만들어야 할지 더 이상 고민하지 마세요. AgentForge가 당신의 강점, 도구, 시장 갭을 분석하여 가장 적합한 프로젝트를 추천합니다.

Contributions are welcome. Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Write tests for new functionality
4. Ensure all tests pass (pytest)
5. Run linting (ruff check . && black --check .)
6. Submit a pull request

git clone https://github.com/agentforge/agentforge
cd agentforge
python -m venv .venv
source .venv/bin/activate # or .venv\Scripts\activate on Windows
pip install -e ".[dev]"
cp .env.example .env
# Add your API keys to .env
pytest

MIT License — see LICENSE file.

Built for Claude Code users who want to spend less time deciding and more time building. Powered by Anthropic Claude.

AgentForge — Stop deciding. Start building. 더 이상 고민하지 말고, 지금 바로 만드세요.