armory

Curated, production-grade skills, agents, hooks, rules, commands, utilities, and presets for AI coding agents. No magic, no demos — battle-tested workflows built for developers who use AI seriously.

Overview

armory is a collection of packages for Claude Code and Claude.ai. Each package is a self-contained prompt or automation unit that extends Claude with a repeatable, opinionated workflow for a specific task domain. Packages span seven types: skills, agents, hooks, rules, commands, utilities, and presets.

Philosophy: Packages in this collection are practical and context-free. They define the how, not just the what — covering inputs, outputs, edge cases, and failure modes. They are tested in real workloads, not constructed as examples.

Intended for developers who treat AI coding agents as a serious part of their workflow.

Package Catalog

Agents — Orchestrators

Orchestrator agents compose skills and other agents into multi-phase workflows. Each can run solo or be spawned by another agent via the Agent tool.

| Agent | Model | Description | | ------------------------------------------------ | ------ | --------------------------------------------------------------------------------------------------------------- | | team-lead | opus | Meta-orchestrator — decomposes multi-domain requests, delegates to specialized agents, synthesizes results | | codebase-auditor | sonnet | Unified quality assessment — spawns code-reviewer, security-reviewer, secret-scanner in parallel, merges report | | project-architect | opus | Phased requirements discovery producing architecture documents with diagrams and tech stack justification | | project-planner | sonnet | Task decomposition with dependency mapping, three-point estimates, milestone timelines, and risk logs | | research-analyst | opus | Multi-source investigation with parallel agents across web, academic, video, and competitive sources | | idea-scout | opus | Business idea validation — Lean Canvas, parallel market/competitive/feasibility research, weighted scorecard | | full-stack-builder | opus | End-to-end implementation from spec — scaffolding, sprints, quality passes, documentation, pre-delivery review | | release-captain | sonnet | Ship lifecycle with quality gates — pre-flight, secret scan, changelog, version bump, PR creation | | proposal-writer | opus | Technical proposals with ROI calculations, three-tier pricing, and Problem-Agitate-Solve framing | | content-strategist | sonnet | Multi-channel content creation with per-channel adaptation and automated quality passes | | media-producer | sonnet | Visual and video format router — selects the right skill based on concept type and output needs |

Agents — Analyzers

| Agent | Model | Description | | ---------------------------------------------- | ------ | ----------------------------------------------------- | | code-reviewer | sonnet | Multi-phase code review with severity-ranked findings | | security-reviewer | sonnet | OWASP Top 10 vulnerability scanning | | secret-scanner | haiku | Pre-commit detection of hardcoded credentials | | test-engineer | sonnet | Co-evolutionary skill evolution with generate-verify-refine loops |

Model routing: Agents marked opus run on Claude Opus 4.7 with xhigh effort by default in Claude Code. Use max effort only for genuinely hard novel problems (diminishing returns, overthinking risk); high when running concurrent sessions or for cost-sensitive work. Opus 4.7 uses adaptive thinking — there is no fixed thinking budget to tune.

Skills — Development & Tooling

| Skill | Description | | ------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | | agent-builder | Build AI agents using the Claude Agent SDK and headless CLI mode — covers tool definitions, MCP servers, and programmatic orchestration | | github | GitHub CLI operations via gh — issues, PRs, CI/Actions, releases, search, REST/GraphQL API, with error handling and automation workflows | | filesystem | File and directory operations via Claude Code built-in tools — replaces the Filesystem MCP server with native Read, Write, Edit, Glob, Grep | | mcp-to-skill | Convert MCP servers into on-demand skills to reduce active context window token usage | | gpu-optimizer | GPU optimization for consumer GPUs (8-24GB VRAM) — PyTorch, XGBoost, CuPy/RAPIDS, memory management, and CUDA tuning | | tavily | AI-optimized web search and content extraction via Tavily API with structured output parsing | | test-harness | Comprehensive pytest suite generation — happy path, edge cases, error conditions, fixtures, mocks, async, parametrized tests | | debug-investigator | Systematic debugging framework — hypothesis-driven investigation with bisection, log analysis, instrumentation, and minimal reproduction | | to-markdown | Convert any file or URL to clean Markdown via MarkItDown — PDF, DOCX, XLSX, PPTX, HTML, images, audio, CSV, JSON, XML, YouTube, EPub | | web-fetch | Web content fetching via curl and WebFetch — replaces the Fetch MCP server with native HTTP operations and jq parsing | | lightpanda-browser | Lightweight headless browser automation via Lightpanda + agent-browser CDP — 9x lower memory, 11x faster, for scraping, DOM extraction, and form automation | | skill-library | Agent-native catalog for browsing, installing, updating, syncing, and removing armory skills from within a Claude Code session | | env-validator | Validate .env files against project requirements — missing vars, type mismatches, insecure defaults, .env.example drift |

Skills — Research & Analysis

| Skill | Description | | ---------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | literature-review | Systematic literature review — search, screen, extract, and synthesize academic research with gap analysis and structured citations | | youtube-search | Search YouTube by keyword via yt-dlp — returns structured metadata (title, URL, channel, views, duration, date) for discovery and source curation | | youtube-analysis | YouTube video transcript extraction and structured concept analysis — multi-level summaries, key concepts, takeaways, no API keys required | | notebooklm | Google NotebookLM automation via notebooklm-py — create notebooks, add sources, chat, generate podcasts, videos, infographics, quizzes, flashcards, and more | | research-critique | Critical analysis of research papers — methodology evaluation, claims-evidence alignment, contribution assessment with collegial analytical posture | | immune | Hybrid adaptive memory with Cheatsheet (positive patterns) and Immune (negative patterns) — Hot/Cold tiered memory, multi-domain antibody scanning, auto-learning |

Skills — Review & Quality

| Skill