The ant CLI exposes every Claude API resource as a shell subcommand. Compared to curl: request bodies are built from typed flags or piped YAML instead of hand-written JSON, @path inlines file contents into any string field, --transform extracts fields with a GJSON path (n. Use it to ground design choices in named patterns, trade-offs and examples.
This file covers decision heuristics for building agents on the Claude API: which primitives to reach for, how to design your tool surface, and how to manage context and cost over long runs. For per-tool mechanics and code examples, see tool-use-concepts.md and the language-spe. Use it as a repeatable review, validation or hardening pass.
List files the agent wrote to /mnt/session/outputs/ during a session, then. Use it to ground design choices in named patterns, trade-offs and examples.
For conceptual overview (tool definitions, tool choice, tips), see shared/tool-use-concepts.md. Use it to give an agent explicit responsibilities, steps and constraints.
Use with options() to override client settings for a single call without mutating the client:. Use it as a repeatable review, validation or hardening pass.
This skill helps you build LLM-powered applications with Claude. Choose the right surface based on your needs, detect the project language, then read the relevant language-specific documentation. Use it to make implementation decisions and avoid common dead ends.
A compilation of language agents using the Cognitive Architectures for Language Agents (🐨CoALA) framework. - CoALA Paper (16 pages of main content): https://arxiv.org/abs/2309.02427 - CoALA Tweet (6 threads): https://twitter.com/ShunyuYao12/status/1699396834983362690 - CoALA. Use it to navigate the topic and choose relevant methods, papers or tools.
A practical guide to Claude Code — from your first prompt to multi-agent automation, hooks, MCP, and team workflows. Built around clear mental models and real examples, not marketing. Use it as a repeatable review, validation or hardening pass.
This repository centralizes and summarizes practical and proposed defenses against prompt injection. Use it to navigate the topic and choose relevant methods, papers or tools.
At the core of every agent is a neural network -- a Transformer, an RNN, a trained function -- shaped by billions of gradient updates on sequences of perception, reasoning, and action. Agency was never bestowed by the surrounding code. It was learned during training. Use it to ground design choices in named patterns, trade-offs and examples.
Policy enforcement, identity, sandboxing, and SRE for autonomous AI agents. One pip install, any framework. Use it as a repeatable review, validation or hardening pass.
Pick a template, see what it does, get a Dockerfile + docker-compose + bot + README you can run anywhere. Use it as a repeatable review, validation or hardening pass.
Pattern Control - Define your patterns once, AI uses them forever ✋ Approval Gates - Review and approve before execution 🔁 Repeatable Results - Same patterns = Same quality code 📝 Editable Agents - Full control over AI behavior 👥 Team-Ready - Everyone uses t. Use it to build a structured path from fundamentals to hands-on practice.
The most comprehensive list of AI agents, frameworks, and tools in 2026. 340+ resources across 20+ categories. Updated monthly. Use it to navigate the topic and choose relevant methods, papers or tools.
Indexes any codebase into a knowledge graph — every dependency, call chain, cluster, and execution flow — then exposes it through smart MCP tools so AI agents never miss code. Use it to ground design choices in named patterns, trade-offs and examples.
A curated reference on agent design & orchestration centered on AgentBench. Use it to navigate the topic and choose relevant methods, papers or tools.
This repository uses a Core/Library split to ensure efficiency and high-signal discovery for LLMs:. Use it to build a structured path from fundamentals to hands-on practice.
This repo contains the earliest version of the Integuru agent we released publicly. It shows the original approach: using browser network requests to generate runnable integration code for platforms without official APIs.
A collection of security and utility hooks for Claude Code. Hooks allow you to extend Claude Code's behavior by running custom scripts at key points during execution.
MARVIS-Agent Tell MARVIS what risk decision you need. It turns local data into governed analysis, models, strategies, and audit-ready deliverables.
The ant CLI exposes every Claude API resource as a shell subcommand. Compared to curl: request bodies are built from typed flags or piped YAML instead of hand-written JSON, @path inlines file contents into any string field, --transform extracts fields with a GJSON path (n. Use it to ground design choices in named patterns, trade-offs and examples.
This file covers decision heuristics for building agents on the Claude API: which primitives to reach for, how to design your tool surface, and how to manage context and cost over long runs. For per-tool mechanics and code examples, see tool-use-concepts.md and the language-spe. Use it as a repeatable review, validation or hardening pass.
List files the agent wrote to /mnt/session/outputs/ during a session, then. Use it to ground design choices in named patterns, trade-offs and examples.
For conceptual overview (tool definitions, tool choice, tips), see shared/tool-use-concepts.md. Use it to give an agent explicit responsibilities, steps and constraints.
Use with options() to override client settings for a single call without mutating the client:. Use it as a repeatable review, validation or hardening pass.
This skill helps you build LLM-powered applications with Claude. Choose the right surface based on your needs, detect the project language, then read the relevant language-specific documentation. Use it to make implementation decisions and avoid common dead ends.
A compilation of language agents using the Cognitive Architectures for Language Agents (🐨CoALA) framework. - CoALA Paper (16 pages of main content): https://arxiv.org/abs/2309.02427 - CoALA Tweet (6 threads): https://twitter.com/ShunyuYao12/status/1699396834983362690 - CoALA. Use it to navigate the topic and choose relevant methods, papers or tools.
A practical guide to Claude Code — from your first prompt to multi-agent automation, hooks, MCP, and team workflows. Built around clear mental models and real examples, not marketing. Use it as a repeatable review, validation or hardening pass.
This repository centralizes and summarizes practical and proposed defenses against prompt injection. Use it to navigate the topic and choose relevant methods, papers or tools.
At the core of every agent is a neural network -- a Transformer, an RNN, a trained function -- shaped by billions of gradient updates on sequences of perception, reasoning, and action. Agency was never bestowed by the surrounding code. It was learned during training. Use it to ground design choices in named patterns, trade-offs and examples.
Policy enforcement, identity, sandboxing, and SRE for autonomous AI agents. One pip install, any framework. Use it as a repeatable review, validation or hardening pass.
Pick a template, see what it does, get a Dockerfile + docker-compose + bot + README you can run anywhere. Use it as a repeatable review, validation or hardening pass.
Pattern Control - Define your patterns once, AI uses them forever ✋ Approval Gates - Review and approve before execution 🔁 Repeatable Results - Same patterns = Same quality code 📝 Editable Agents - Full control over AI behavior 👥 Team-Ready - Everyone uses t. Use it to build a structured path from fundamentals to hands-on practice.
The most comprehensive list of AI agents, frameworks, and tools in 2026. 340+ resources across 20+ categories. Updated monthly. Use it to navigate the topic and choose relevant methods, papers or tools.
Indexes any codebase into a knowledge graph — every dependency, call chain, cluster, and execution flow — then exposes it through smart MCP tools so AI agents never miss code. Use it to ground design choices in named patterns, trade-offs and examples.
A curated reference on agent design & orchestration centered on AgentBench. Use it to navigate the topic and choose relevant methods, papers or tools.
This repository uses a Core/Library split to ensure efficiency and high-signal discovery for LLMs:. Use it to build a structured path from fundamentals to hands-on practice.
This repo contains the earliest version of the Integuru agent we released publicly. It shows the original approach: using browser network requests to generate runnable integration code for platforms without official APIs.
A collection of security and utility hooks for Claude Code. Hooks allow you to extend Claude Code's behavior by running custom scripts at key points during execution.
MARVIS-Agent Tell MARVIS what risk decision you need. It turns local data into governed analysis, models, strategies, and audit-ready deliverables.