Gives developers ready-made, tested workflows for code review, debugging, architecture decision records, SQL tuning and dependency audits, so routine checks run through a prompt instead of by hand.
Claude Code
> /plugin marketplace add Mathews-Tom/armory> /plugin install armory@armory
Codex
$ npx skills add Mathews-Tom/armory -a codex
Cursor
$ npx skills add Mathews-Tom/armory -a cursor
Gemini CLI
$ npx skills add Mathews-Tom/armory -a gemini-cli
GitHub Copilot
$ copilot plugin marketplace add Mathews-Tom/armory$ copilot plugin install armory@armory
OpenCode
$ npx skills add Mathews-Tom/armory -a opencode
Windsurf
$ npx skills add Mathews-Tom/armory -a windsurf
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.
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. An optional eighth type, native extensions, packages executable OMP and Claude Code modules that install only through an explicit per-host lifecycle.
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.
Use the planning packages in sequence, not as interchangeable formats:
unknown destination → decision-map → known design → plan-prompts → task-decomposer / plan-review → milestone-runner → stacked-prs
| Situation | Package | Output |
|---|---|---|
| Destination or design is unresolved | decision-map | Tracker-backed decision map, frontier, recorded decisions, and prototype evidence (built by prototype) when uncertainty needs executable evidence |
| Design is known and needs milestones | plan-prompts | Size-matched DEVELOPMENT_PLAN.md and EXECUTION_PROMPTS.md (or one prompt for one-PR work) with user-observable acceptance |
| Feature is specified and needs implementation slices | task-decomposer | Phased task board with dependencies and test matrix |
| Existing plan needs a pre-implementation challenge | plan-review | Severity-ranked plan review |
| Approved plan needs execution across sessions | milestone-runner | Goal-bound execution: one design check, observed outcome, CI, review and merge per milestone |
decision-map stops at resolved decisions. It recommends plan-prompts after a complete map and never creates implementation milestones or tasks.
Disposable prototypes are built by prototype and recorded as ticket evidence by decision-map; neither is a substitute for production implementation.
Start with the shortest complete user workflow, not a tooling-only milestone. plan-prompts sizes the plan to the work, writes acceptance as Done when <user> can <interaction> and sees <result>, and moves unsourced checks to suggested follow-ups. milestone-runner restates each milestone's GOAL, NON-GOALS and DONE WHEN, does only the work acceptance requires, and treats a reviewed, green, merged milestone with an observed interaction as done.
Both packages use the 3.0.0 contract; 2.x prompts still run. Missing release metadata blocks release preparation only. Static conformance and package/adapter checks do not establish live skill effectiveness.
158 package definitions across seven prompt/automation types and the optional native extension type. This is an unversioned rolling catalog; individual packages carry their own versions.
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 | Explicit release preparation — authorized version and changelog edits, quality gates, secret scan, release PR; stops at the PR |
| 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 | Explicit reflective capture — queues privacy-safe lessons, reports status, and reviews approved package changes |
| 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
opusrequest Claude Code'sopusalias withxhigheffort by default. The declared target matrix records the alias and an expected fully disclosed model-id prefix; a dated snapshot is an observed receipt field, never inferred from this declaration. Usemaxeffort only for genuinely hard novel problems (diminishing returns, overthinking risk); usehighfor concurrent sessions or cost-sensitive work.
model_fit.yaml declares the client/model/effort/profile/telemetry matrix. Its current target requests the Claude Code opus alias, requires a disclosed model ID beginning claude-opus-, and requires observed input-token and output-token fields.
Validate a profile without creating a model request:
uv run python scripts/model_fit.py --target claude-code-opus-xhigh --profile core --dry-run
uv run python scripts/model_fit.py --target claude-code-opus-xhigh --profile full --dry-run
Dry runs read claude --version and resolve repository package definitions only. They report discovery metadata and explicitly state that package content was not loaded. They do not install a profile or make a paid call.
Use --live --output /path/to/receipt.json only when a model request is authorized. The probe disables built-in tools, suppresses ambient MCP configuration, and does not persist sessions. Its receipt stores derived fields only: declared target/profile/tool surface, client version, an observed model that must match the target's declared prefix, required token fields, wall time, and terminal metadata. Raw prompts, assistant text, and stream events are never retained. A missing or changed required field exits non-zero; the probe never estimates a token value.
| 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 |
| domain-modeling | Challenge ambiguous domain terms, cardinalities, and code/model contradictions; capture agreed vocabulary without generic context scaffolding or ADR writing |
| walkthrough | Deliver sourced, repeatable procedures for unavoidable human-only operations; keep agent-doable setup and repository onboarding with the agent |
Stars on GitHub
A development methodology for coding agents that makes the agent brainstorm before building, follow plans, run parallel work and finish branches in a disciplined way. You get fewer skipped steps and more consistent results from your coding assistant.
@obraHelps you take a change from a rough idea or bug report through to a tested, reviewed result, with each step handled by a focused skill. Its skills cover pinning down domain terms, designing module interfaces, diagnosing hard bugs, and reviewing changes against your repo's coding standards.
@mattpocockGives your coding agent a planning, testing and review routine to follow on every task. It plans before building, checks changes with tests, has a fresh reviewer look for regressions, and saves repeated wins as reusable skills.
@affaan-mDeveloper tools for working on the React codebase itself. Provides skills for extracting error messages, managing and debugging feature flags across release channels, running Flow type checks, and fixing lint and formatting issues before committing.
@facebookGives developers ready-made, tested workflows for code review, debugging, architecture decision records, SQL tuning and dependency audits, so routine checks run through a prompt instead of by hand. Install it with the command on its page. It includes doc-condenser, opus-4-7-migration, regex-builder. Its skills do not fire on their own yet. Free and open source.
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