Standards and tools we publish

Standardized way for AI agents to discover and execute intents against web services. Defines a unified vocabulary for intents, metadata, and execution methods, plus an ODRL-based policy layer for permissions and rate limits. The missing layer between agents and APIs.

An open specification for composable AI agent capabilities. 36 atomic capabilities across 9 cognitive layers, a type-safe workflow DSL, and grounded world modeling with trust-aware conflict resolution. Built on the Grounded Agency philosophy: agent reliability becomes structural, not optional.

Open standard for detecting and scoring manipulation in online content. 20-category framework across 5 composite factors. The substrate Decipon runs on. Anyone can implement, contest findings, or extend the framework.

A four-layer governance architecture for multi-agent AI systems. Authority boundaries, escalation, and constitutional review applied to ensembles where naive agent autonomy produces drift, conflict, or runaway behavior.

Curated collection of Claude Code plugins for AI-augmented development plus advanced analytical and research tasks. Each plugin ships specialized agents, skills, and commands for a specific domain. The umbrella distribution channel for our Claude Code work.

A standardized framework for enhancing how LLMs process and respond to prompts through composable decorators. Ships an open standard, a Python reference implementation, and an MCP server that brings decorator behavior into Claude Desktop and other MCP-aware clients.

Behavior-Driven Specification Kit. Specification-first governance for AI-assisted code generation. Eight-phase validator, lifecycle skills, and scope enforcement. Test-before-code, uncertainty documentation, multi-pass verification.

Open-source epistemic audit for Claude conversation history. Applies eight published AI-safety research frameworks (Spiral-Bench, Sharma sycophancy, SycEval, BeliefShift, ITP, MedTrust-RAG) and produces citation-validated HTML reports. For users who want to know whether their assistant is drifting toward sycophancy.

Self-Improving Agentic RAG Engine. Evolves multi-agent RAG pipelines using quality-diversity optimization (Map-Elites) and topology search. Lets the pipeline find its own better shape rather than requiring humans to enumerate candidate architectures.