Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ArieGoldkin/claude-forge --skill ai-native-developmentgit clone --depth 1 https://github.com/ArieGoldkin/claude-forgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ariegoldkin/claude-forge/ai-native-development)<a href="https://agentmods.dev/skills/ariegoldkin/claude-forge/ai-native-development"><img src="https://agentmods.dev/badge/skills/ariegoldkin/claude-forge/ai-native-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ariegoldkin/claude-forge/ai-native-development"><img src="https://agentmods.dev/badge/skills/ariegoldkin/claude-forge/ai-native-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00108 | $0.01213 |
| Opus 5.5 | $0.00043 | $0.00485 |
| Sonnet 5.5 | $0.00022 | $0.00243 |
| Haiku 4.5 | $0.00011 | $0.00121 |
Grade A, and why
ai-native-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Native Development
Core Concepts
Embeddings & Vector Search
See ${CLAUDE_SKILL_DIR}/references/vector-databases.md for embeddings setup, cosine similarity, chunking strategies, and vector database guides (Pinecone, Chroma, Weaviate, Qdrant).
RAG (Retrieval-Augmented Generation)
See ${CLAUDE_SKILL_DIR}/references/rag-patterns.md for basic and advanced RAG patterns, citation strategies, hybrid search with Reciprocal Rank Fusion, conversation memory patterns, and error handling.
Function Calling & Tool Use
See ${CLAUDE_SKILL_DIR}/references/function-calling.md for tool definition patterns (OpenAI and Anthropic), function calling loops, parallel and streaming tool execution, input validation with Zod, and error handling.
Agentic Workflows
Enable LLMs to reason, plan, and take autonomous actions.
Patterns:
- ReAct: Reasoning + Acting loop with observations
- Tree of Thoughts: Explore multiple reasoning paths
- Multi-Agent: Specialized agents collaborating on complex tasks
- Autonomous Agents: Self-directed goal achievement
- ML Experiment Loops: Autonomous iteration on ML metrics using /experiment for hyperparameter tuning, evaluation improvement, and model selection. See
${CLAUDE_SKILL_DIR}/references/ml-experiment-loops.mdfor patterns and worked examples.
Detailed Implementation: See ${CLAUDE_SKILL_DIR}/references/agentic-workflows.md for:
- Complete ReAct loop implementation
- Tree of Thoughts exploration
- Multi-agent coordinator patterns
- Agent memory management
- Error recovery and safety guards
Multi-Agent Orchestration (Opus 4.5)
Advanced multi-agent patterns leveraging Opus 4.5's extended thinking capabilities.
When to Use Extended Thinking:
- Coordinating 3+ specialized agents
- Complex dependency resolution between agent outputs
- Dynamic task allocation based on agent capabilities
- Conflict resolution when agents produce contradictory results
Key Patterns:
- Orchestrator with dependency graph execution
- Task planning with extended thinking budget
- Conflict detection and resolution
- Adaptive agent selection based on task complexity
- Agent communication bus for coordination
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- checklists/ai-implementation-checklist.md 9.8 KB
- examples/chatbot-with-rag-example.ts 1.8 KB runs code
- references/agentic-workflows.md 15 KB
- references/function-calling.md 14 KB
- references/ml-experiment-loops.md 8.0 KB
- references/multi-agent-opus.md 7.3 KB
- references/observability.md 13 KB
- references/rag-patterns.md 13 KB
- references/vector-databases.md 10 KB
- templates/agent-workflow-template.ts 11 KB runs code
- templates/rag-pipeline-template.ts 8.8 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 148 lines · 108 tokens per session scan A b29a7648b9cb
ai-native-development is a skill published in the GitHub repository ArieGoldkin/claude-forge (7 stars, last pushed 2mo ago), licensed MIT. It adds 108 tokens to every session and 1,213 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
llm-app-development
LLM app development with RAG, prompt engineering, vector databases, and AI agents.
rag-prompt-context-placeholder-templates
Guidelines and instructions for RAG prompt context placeholder templates.
rag-query-expansion-prompt-configurations
Guidelines and instructions for RAG query expansion prompt configurations.
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
llm-ops
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.