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 |
| skill-librarian | sonnet | Reflective write-phase orchestrator — turns completed task transcripts into skill proposals or augmentations |
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 |
| skill-router | haiku | Outcome-weighted package routing using historical eval results |
| test-engineer | sonnet | Co-evolutionary skill evolution with generate-verify-refine loops |
Model routing: Agents marked
opusrun on Claude Opus 4.7 withxhigheffort by default in Claude Code. Usemaxeffort only for genuinely hard novel problems (diminishing returns, overthinking risk);highwhen 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 |
| project-context-setup | Scaffold repo-local agent context — issue tracker rules, triage labels, domain glossary layout, ADR lookup, agent brief conventions |
| stacked-prs | Manage dependent branch stacks and stacked pull requests — inspect, split, publish, sync, validate, merge, and clean up stack topology |
| plan-prompts | Generate .docs/DEVELOPMENT_PLAN.md and .docs/EXECUTION_PROMPTS.md from source docs, with reviewed stacked-PR prompts |
| milestone-runner | Run .docs/EXECUTION_PROMPTS.md milestone stacks sequentially or in dependency-safe parallel waves, with CI/review gates and cleanup |
| 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 |
| handoff | Maintain .docs/handoff.md as a 200-line session-continuity runbook for in-flight work, blockers, decisions, validation state, and resume steps |
Skills — Research & Analysis
| Skill | Description |
|---|---|
| literature-review | Systema |