AgentMemory is a persistent memory system designed for AI coding agents (Claude Code, Cursor, Codex, etc.) that captures agent observations via hooks, compresses and stores them, and enables semantic/recall search across sessions. Built on the iii-engine framework, it provides a modular architecture with CLI, REST API, MCP server, and web viewer interfaces. The system eliminates repetitive context re-explanation by automatically injecting relevant memories into agent sessions, implements memory consolidation to extract stable facts, and includes features like working memory management, audit trails, and health monitoring. Agent integrations occur through platform-specific hooks and MCP tools, allowing agents to store observations and retrieve context without manual intervention.
This repository is a curated collection of 177+ domain-specific scientific 'skills' for AI agents, following the Agent Skills standard. Each skill is a self-contained module providing a CLI tool, documentation, and tests for a specific scientific or technical task—ranging from single-cell RNA-seq analysis (scanpy) and molecular docking (diffdock) to clinical report validation and ISO standards readiness. The architecture is a monorepo of independent skills, each with its own scripts/, references/, assets/, and tests/. Skills are designed to be safe, bounded, and reproducible: they validate inputs before execution, enforce resource limits, avoid network access unless explicitly required, and produce structured JSON reports. The repository also includes a shared test infrastructure (skill_contract) and a meta-skill (autoskill) that can automatically propose new skills from screen activity. While primarily an application library for AI agents, it has strong data/research characteristics due to its heavy use of scientific data formats and validation of research artifacts.
This is a Claude Code plugin and resource library, not a deployable application or data pipeline: 27 slash commands, 9+ autonomous skills, 3 auditor subagents, and extensive prompt-engineering documentation that extend the Claude Code CLI. In production, the system functions as configuration-as-code content consumed by the Claude Code runtime — teaching the assistant how to create executable plans (PLAN.md is the prompt itself), chain meta-prompts, build MCP servers, manage subagent orchestration, run autonomous coding loops (the 'Ralph' technique), and apply iOS/macOS development expertise. The only real executable code is a handful of self-contained scripts: a TypeScript CLI for The Pirate Bay API, Python/TypeScript MCP server templates, project scaffolding shell scripts, and autonomous loop iteration scripts. The dominant architectural pattern is the 'router skill': a SKILL.md entry point with essential principles, an intake gate, a routing table, and progressive disclosure into workflows, references, templates, and scripts — implemented consistently across all skills with pure XML prompt structure and checkpoint-based plan execution. Despite being classified as an application (there is no data-research character — no notebooks, datasets, or pipelines), its substance is largely authored content rather than software. The craft is strong: consistent XML schemas across files, a genuine QA layer (skill-auditor agents), secret hygiene practices, and a well-developed checkpoint/auth-gate protocol for safe automation. However, the repo carries real drift risks: duplicated reference files (research-pitfalls.md, cli-observability.md are copied across skills), dangling references to not-yet-created files, tight coupling to Claude Code's evolving plugin API, version-specific Apple platform guidance that will age, and the included The Pirate Bay skill which depends on a fragile third-party API and presents a content-policy concern. An engineer seeking to reuse this should extract the self-contained templates and scripts (MCP server scaffolding, loop automation) and treat the reference material as opinionated prompt guidance rather than canonical documentation.
This repository is a curated 'awesome list' aggregating external resources about Agent Skills — an open specification for packaging workflows and knowledge into folders consumable by AI agents. It does not contain runnable software, data pipelines, or ML models. The codebase consists of a multilingual README index, three translated adaptation guides (design patterns, practical experience, and a complete build guide), and an Excalidraw MCP integration guide. These documents are high-quality translations of official material from Anthropic and Google, providing actionable design patterns (Tool Wrapper, Generator, Reviewer, Inversion, Pipeline) and best practices for building Agent Skills. The primary function of this repository is as a reference index and a gateway to the broader Skill ecosystem; there is no executable code to analyze for architecture or data flows. Classified as 'application' due to lack of data research characteristics, but it is accurately described as a curated documentation and resource hub.