vasilyu1983/AI-Agents-public

agents-hooks

Configures Claude Code hooks and Codex hooks.json/notify callbacks. Use when adding guardrails, preflight, audit trails, worktree automation, or budget enforcement.

agents-mcp

Configures and hardens MCP servers for Claude Code and Codex agents. Use when connecting databases, APIs, files, or SaaS via MCP, or building custom servers.

agents-memory

Manages AGENTS.md, CLAUDE.md, and scoped repo rules for Claude Code and Codex. Use when fixing stale memory, ignored instructions, memory audits, or model-upgrade migration.

agents-skills-feedback-loop

Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.

agents-skills

Creates and audits agent skills with SKILL.md, references, scripts, and platform-scoped metadata. Use when creating, updating, or validating shared skills.

agents-swarm-orchestration

Coordinates multi-agent execution across subagents, teams, and workflows. Use when planning dependency-aware fan-out, verifier passes, runtime selection, or Loop Engineering.

ai-agents

AI agent architecture, graph and loop composition, protocol choice, evaluation, and observability. Use when scoping or reviewing systems before implementation.

ai-coding-agents-execution-sandbox

Designs execution sandboxes for coding agents. Use when modeling process isolation, filesystem policy, network controls, workspace mounts, or destructive-command boundaries.

ai-coding-agents-observability-evals

Designs coding-agent observability and evals. Use when measuring traces, replay, checkpoint lineage, quality trajectories, tool grading, regression, or cost.

ai-coding-agents-permissions

Designs approval and permission systems for coding-agent runtimes. Use when modeling tool approvals, plan-mode transitions, sandbox prompts, or worker permission handoffs.

ai-coding-agents

Creates coding agents on Claude Code, Codex, and Agent SDK. Use when defining review, test, refactor, or team agents — not building a runtime.

ai-coding-agents-tasks

Designs task runtimes for Loop Engineering, Graph Engineering, and background work. Use when work needs task lists, cyclic/workflow graphs, cancellation, or teammate coordination.

ai-evals

Designs trustworthy LLM/agent evals and optimization loops. Use when building graders, calibrating judges, choosing eval/fine-tune methods, thresholds, or fixing noisy scores.

ai-llm

Guides the LLM lifecycle from strategy to deployment. Use when planning, comparing, fine-tuning, distilling, compressing, migrating, or operating LLM systems.

ai-prompt-engineering

Prompt engineering for production LLMs — structured outputs, evals, RAG, tool workflows, multimodal prompting, and safety. Use when designing, debugging, or shipping prompts.

data-sql-optimization

Diagnoses and tunes SQL for OLTP workloads on PostgreSQL, MySQL, and SQL Server. Use when tuning queries, reading plans, indexing, or fixing lock contention.

dev-ai-coding-metrics

Measures AI coding impact and extension robustness. Use when tracking delivery, quality trajectories, cost, experience, pilots, scorecards, or leadership reporting.

dev-api-design

Designs durable API contracts across REST, GraphQL, gRPC, tRPC, and AsyncAPI. Use when specifying interfaces, auth, versioning, errors, rate limits, or agent APIs.

dev-contribution-quality-analysis

Analyzes commit and PR history to score contribution quality objectively. Use when building engineering scorecards, calibrating promotions, or measuring AI-assist impact.

dev-git-workflow

Designs team Git workflows for branching, PRs, and releases. Use when choosing branching models, stacked PRs, merge queues, worktree isolation for agents, or collaboration rules.

docs-ai-prd

Writes PRDs and specs optimized for coding assistants. Use when authoring requirements or project context for Claude Code, Cursor, Copilot, or Codex.

document-docx

Create/edit .docx files with styles, templates, comments, and extraction workflows. Use when asked to generate Word reports, contracts, proposals, or convert Word content.

foundations-ai-planning-search

Applies planning and search theory (A*, CSP, MCTS, STRIPS/PDDL, HTN) to agent design. Use when an LLM agent hallucinates action sequences or needs precondition/effect validity.

foundations-behavioral-economics

16 behavioral-economics primitives for ethical pricing, choice design, and retention. Use when framing, defaults, habits, dark patterns, AI-agent nudging, or nudge ethics apply.

foundations-causal-inference

Causal-inference primitives: DAGs, IV, RDD, DiD, synthetic control, propensity, CATE, interference. Use when attributing confounded impact or rollout and LLM-eval confounding.

foundations-consumer-neuroscience

Consumer-neuroscience primitives for attention, arousal, bonding, narrative, memory, and reward. Use when shaping ethical UX, neuro study design, or DMCC/AI Act gates.

foundations-control-theory

Control-theory primitives for PID, MPC, Kalman, stability, anti-windup, dead-time, breakers, and limits. Use when tuning autoscaling, retries, or agent loops.

foundations-cybernetics-vsm

Applies Beer's VSM and Ashby's Law to diagnose org or agent-system viability. Use when a team or agent hierarchy has coordination, escalation, or requisite-variety problems.

foundations-decision-theory

Decision-theory primitives for uncertain choices, utility, Bayesian decisions, regret, value of information, MCDA, options, and bandits. Use when choosing under uncertainty.

foundations-distributed-systems

Distributed-systems primitives for CAP/PACELC, FLP, Paxos, Raft, clocks, CRDTs, leases, quorums, and broadcast protocols. Use when designing coordination.

foundations-game-theory

Game-theory primitives for strategic decision systems, auctions, mechanism design, incentives, attribution, negotiation, debate, and trust. Use when modeling strategic play.

foundations-grounding-communication

Grounding-theory primitives for human-AI and agent handoffs, common ground, acceptance evidence, repair, and ambiguity. Use when coordinating meaning.

foundations-information-theory

Information-theory primitives for AI systems, entropy, mutual information, KL, compression, channel limits, MDL, bottlenecks, and signal quality. Use when quantifying information.

foundations-network-science

Network-science primitives for graph systems, centrality, PageRank, communities, contagion, link prediction, and temporal networks. Use when analyzing graph structure.

foundations-queueing-theory

Applies queueing theory (Little's Law, M/M/c, Erlang, Kingman, USL) to capacity and latency decisions. Use when load causes non-linear latency growth or queue overrun risk.

foundations-reliability-theory

Reliability-theory primitives for MTBF/MTTR, availability, hazards, FMEA, redundancy, error budgets, Weibull analysis, and SLOs. Use when modeling failure.

foundations-team-theory

Team-theory primitives for cooperative multi-agent decisions, subagent allocation, communication value, Dec-POMDPs, and decentralized control. Use when organizing agents.

foundations-theory-of-constraints

Theory of Constraints primitives for focusing steps, drum-buffer-rope, throughput accounting, critical chain, and policy constraints. Use when sequencing by bottleneck.

ops-cost-optimization

Audits SaaS/PaaS, cloud commitment, and AI/LLM costs across Vercel, Supabase, AWS, and Cloudflare. Use when analyzing bills, right-sizing plans, or buying commitments.

product-management

Founder-PM toolkit for discovery, roadmaps, prioritization, and PMF measurement. Use when planning product strategy, metrics, or roadmaps.