What it does: A full-featured AI assistant powered by Gemini, delivered via Telegram with a 12-page real-time web dashboard. 7 built-in plugins (including Google ecosystem) turn it into a unified personal productivity hub — search Drive files, manage Calendar events, sync Tasks, and query a hybrid RAG knowledge base, all through natural conversation.

Key differentiators vs NanoClaw (Claude-based):

Highlights:

• grammY Bot Framework — Type-safe, event-driven Telegram integration with rate limiting and message consolidation
• MCP Client Bridge — Model Context Protocol support with per-tool whitelist and Zod schema validation
• Smart Routing — preferredPath intelligently switches between fast path (direct API) and container execution
• Google Ecosystem — 7 plugins: OAuth hub, Drive, Calendar (full CRUD), Tasks (bidirectional sync), Drive Knowledge RAG (hybrid search), Discord Reporter, Memorization Service
• Plugin System — 6 extension points: Gemini Tools, Message Hooks, Express Routes, IPC Handlers, Background Services, Dashboard Extensions
• Fast Path — Direct Gemini API streaming with context caching (75-90% token cost reduction) and native function calling
• Hybrid Drive RAG — Pre-indexed embeddings via physical file approach + live Drive search fallback
• 12-page Dashboard — Overview, Tasks, Calendar, Drive Browser, Knowledge, Analytics, Memory, Logs, Activity, Settings, Schedule, MCP Management
• i18n 100% — Full interface support for 8 languages
• 1,244 tests across 54 files — 92% statement coverage, 84% branch coverage

Monorepo

nanogemclaw/
├── packages/
│   ├── core/
│   ├── db/
│   ├── gemini/
│   ├── telegram/
│   ├── server/
│   ├── plugin-api/
│   ├── event-bus/
│   └── dashboard/
├── plugins/
│   ├── google-auth/
│   ├── google-drive/
│   ├── google-tasks/
│   ├── google-calendar-rw/
│   ├── drive-knowledge-rag/
│   ├── discord-reporter/
│   └── memorization-service/
├── app/
├── container/
└── docs/

Google Tools (16)

Drive    → search, read, summarize
Tasks    → create, complete, list
Calendar → create, list, update,
           delete, check_availability
RAG      → search_knowledge
Discord  → daily/weekly reports

What it does: Claudistotle takes you end-to-end from a rough research question to a polished academic paper — searching 8+ academic databases, synthesizing a literature review with verified citations (never fabricates references), writing structured academic prose, and simulating three-round peer review. Built as a Claude Code plugin.

Pipeline:

Topic idea → Research design → Literature review → Draft → Peer review → Paper

Highlights:

• 8+ academic databases — Semantic Scholar, OpenAlex, CORE, arXiv, PhilPapers, Stanford Encyclopedia, Internet Encyclopedia, Notre Dame Philosophical Reviews
• Citation integrity — every reference verified against CrossRef; fabricated citations are impossible by design (BibTeX hooks validate metadata provenance before any .bib is written)
• 6-phase literature review — environment check → domain decomposition → parallel database search → synthesis planning → section writing → assembly with DOCX export
• Three-round peer review simulation — two AI personas (Athena the reviewer, Calliope the reviser) iterate on argument validity, evidence quality, and writing clarity
• Autopilot with self-healing — cautious / moderate / full autonomy modes; automatic retry on quality gate failures
• Text commentary — close reading and logical annotation of primary philosophical texts, auto-integrated into your draft
• Resumable — interrupted sessions resume from PROGRESS.md
• Extends PhilLit — the multi-agent research workflow from Syracuse University & University of Hamburg

Commands (12)

setup
help
research-design
literature-review
text-commentary
draft
peer-review
refine
validate
feedback
autopilot
philosophy-research

Output

reviews/[project]/
├── research-proposal.md
├── literature-review-final.md
├── literature-all.bib
├── argument-skeleton.md
├── paper-draft.md
├── change-record.md
├── PROGRESS.md
└── sources/
    ├── primary/
    └── secondary/

Problem it solves: Most AI agent failures come from choosing patterns based on technical appeal rather than real business requirements. Teams jump to "multi-agent system" before understanding what problem they're solving.

Approach: A structured five-phase pipeline based on Anthropic's Building Effective Agents framework. It runs a Socratic deep interview across four clarity dimensions, scores ambiguity in real time, and only produces a recommendation once the problem is actually understood.

Highlights:

• Five-phase pipeline — Deep Interview → Recommendation → Blueprint → Functional Modules → Execution Bridge, each with a quality gate
• Four-dimension scoring — Goal (35%) / Constraints (25%) / Domain (25%) / Success Criteria (15%), live progress bars every round
• Ambiguity gate — pipeline won't exit Phase 1 until ambiguity ≤ 20% (soft cap Round 8, hard cap Round 12)
• Challenge modes — auto-triggered Contrarian / Simplifier / Ontologist questions break circular answers and surface hidden assumptions
• Decision matrix — four-axis evaluation (control × complexity × resources × expertise) selects from 8 architecture patterns including hybrid strategies
• Single-agent first — multi-agent systems (10–15× more tokens) only recommended when interview evidence clearly shows a single agent is insufficient
• Greenfield & brownfield — automatically shifts questioning when you mention migration, legacy integration, or rollback criteria
• Implementation-ready output — blueprint with Mermaid architecture diagram, PRD, technical spec, cost analysis spreadsheet, risk matrix, evaluation plan, and starter prototype

Phases

Phase 1 · Deep Interview
  └ gate: ambiguity ≤ 20%
Phase 2 · Recommendation
  └ gate: user confirms
Phase 3 · Blueprint
Phase 4 · Functional Modules
Phase 5 · Execution Bridge

Patterns (8)

Single Agent
Routing Workflow
Sequential Workflow
Parallel Workflow
Hierarchical Multi-Agent
Collaborative Multi-Agent
Evaluator-Optimizer
Hybrid Strategies

Artifacts

interview-spec.md
ai-agent-blueprint.md
prd.md
tech-spec.md
cost-analysis.xlsx
risk-matrix.md
eval-plan.md
prototype/

Problem: Migrating a mid-size ROS1 package (~5K-10K LoC) takes a senior engineer 2-4 weeks of manual refactoring. With ROS1 Noetic EOL (May 2025), thousands of packages face abandonment.

Solution: One command to reforge your legacy robotics packages. ROSForge uses LLMs to understand code semantics — not just regex-replace — and delivers end-to-end migration with automatic build verification and fix loops.

What makes it unique:

• BYOM (Bring Your Own Model) — freely switch between Claude, Gemini, and OpenAI
• Full-spectrum migration — C++ (roscpp→rclcpp), Python (rospy→rclpy), Launch files (XML→Python), CMakeLists.txt, package.xml, msg/srv definitions
• Validate-Fix loop — auto colcon build + AI-driven error repair
• Interactive mode — pause at critical steps for human review
• Workspace-level batch migration with cross-package dependency resolution
• Custom rules via .rosforge/rules.yaml

Stack

Python 3.14 | Click CLI
Pipeline Architecture
├─ Ingest (Parsers)
├─ Analyze (Deps)
├─ Transform (AI)
├─ Validate (Build)
└─ Report (Diff)

Pipeline

rosforge migrate ./pkg
  ↓ Parse AST/IR
  ↓ Resolve dependencies
  ↓ AI transform (BYOM)
  ↓ colcon build
  ↓ Auto-fix if failed
  ↓ Generate report

What it does: Input a stock ticker, get fair value estimates from 7 independent models — weighted by industry classification, validated by backtesting, and auto-adjusted through feedback loops.

7 Valuation Models:

System features:

• 3 interfaces — CLI (colored terminal), HTTP API (Express), n8n automation workflow
• LLM smart classification — auto-categorize stocks by industry with guardrails
• Backtest engine — 90d/180d accuracy validation with hit rate, MAE metrics
• Adaptive weights — feedback loop adjusts model weights based on backtest accuracy per category
• Portfolio management — sector allocation, risk metrics, performance tracking
• Alert system — price/valuation/classification change triggers → Telegram/Email push

Architecture

src/
├── api/         # FinMind API
├── models/      # 7 valuation models
│   ├── dcf.js
│   ├── dividend.js
│   ├── per.js
│   ├── pbr.js
│   ├── capex.js
│   ├── ev-ebitda.js
│   └── psr.js
├── llm/         # LLM guardrails
├── backtest/    # Accuracy engine
├── feedback/    # Adaptive weights
├── portfolio/   # Analytics
└── report/      # Synthesis

Data Pipeline

FinMind API (8 datasets)
  ↓ Parallel fetch
7 Models (parallel calc)
  ↓ Smart weighting
LLM Classification
  ↓ Backtest feedback
Final Recommendation
  ↓ n8n automation
Telegram / Email