AI & ML

Classifies ECC components (skills, commands, rules, hooks) into DAILY and LIBRARY buckets based on evidence from the actual codebase, producing an install plan and verification report.

Build persistent multi-agent operating systems on Claude Code with kernel architecture, specialist agents, slash commands, file-based memory, and scheduled automation.

Search the web, code, companies, and people using the Exa MCP server for current information and technical research.

Write test-driven Laravel applications using PHPUnit, Pest, model factories, HTTP tests, Sanctum authentication testing, mocking, and coverage.

Provides a formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles to define pass/fail criteria, measure reliability with pass@k metrics, and create regression test suites.

Generate images, videos, and audio using fal.ai models via MCP tools, with support for text-to-image, text/image-to-video, text-to-speech, and video-to-audio.

Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

Uses two independent review agents to verify output quality, requiring both to pass before shipping.

Automatically measures whether coding agents follow skills, rules, or agent definitions by generating scenarios at multiple prompt strictness levels, running agents, classifying tool calls, and reporting compliance rates with full timelines.

Browse available agent personas and compose ad-hoc teams of up to 5 agents to work in parallel on a task, then synthesize their results.

Analyze Claude Code token usage, spending, and budgets from the local ECC cost-tracker metrics log. Provides summaries, breakdowns by model, session, or date, and CSV export.

Manage a multi-layered knowledge system for ingesting, organizing, syncing, and retrieving knowledge across local files, MCP memory, vector stores, and Git repos.

Audit Claude Code configuration files for security vulnerabilities, misconfigurations, and injection risks using AgentShield.

Provides real-time structural code health scores via CodeScene MCP to review code quality before edits, verify score deltas after changes, and gate commits and PRs.

Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.

Orchestrate parallel AI agent sessions using dmux, a tmux pane manager for agent harnesses like Claude Code and Codex.

Orchestrate multi-agent coding tasks by dispatching parallel agents in isolated worktrees, monitoring progress, and reading structured reports.

Reproduce a bug as a failing regression test, fix until green, review, and commit via a delegated pipeline.

Run Claude Code autonomously in loops, from simple sequential pipelines to multi-agent DAG orchestration.

Retrieve, analyze, and update Jira tickets directly from your AI coding workflow using MCP or REST API.

Orchestrates building a new feature end-to-end by delegating research, planning, TDD implementation, review, and gated commit to specialized agents.

Reads a plan document, decomposes it into steps, designs a per-step agent chain from the ECC catalogue, and emits ready-to-paste /orchestrate custom prompts without executing them.

Analyze draft prompts to identify intent, scope, and missing context, then generate an optimized prompt with ECC component recommendations. Advisory only — never executes the task.

Provides idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications, covering model architecture, training loops, data pipelines, and checkpointing.

Schedule and publish social media posts across 13 platforms using SocialClaw, with support for media uploads, campaign validation, and delivery monitoring.

Create reproducible, cross-platform development environments with Flox, a declarative Nix-based environment manager for macOS and Linux.

Uses a multi-agent generator-evaluator feedback loop to build high-quality applications from a single prompt, inspired by GANs and Anthropic's harness design.

Enables AI agents to make policy-gated payments using the x402 HTTP payment protocol with per-task budgets, spending controls, and non-custodial wallets.

Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions.

Guides AI agents through engineering workflows with eval-first execution, task decomposition, cost-aware model routing, and review focus for generated code.

Provides evidence-first revenue, pricing, refunds, team-billing, and billing-model truth workflows for sales snapshots, pricing comparisons, duplicate-charge diagnosis, or code-backed billing reality.

Progressively refines context retrieval in multi-agent workflows to solve the subagent context problem.

Build MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API.

Quickly query genomic reference databases for Ensembl IDs, gene metadata, sequences, BLAST searches, and enrichment analysis using the gget CLI or Python package.

Guides teams in adapting engineering processes, architecture, code review, and testing for high-volume AI-assisted code generation.

Produces an evidence-backed inventory of live automations (jobs, hooks, connectors, MCP servers, wrappers) and recommends keep/merge/cut/fix-next actions before rewriting anything.