This repository is the source of specify-cli, the flagship Python CLI of Spec Kit. In production it is a Typer-based developer tool that bootstraps projects for Spec-Driven Development: `specify init` scaffolds a `.specify/` structure, installs shared bash/PowerShell/Python helper scripts, and generates agent command/skill files into 37+ AI coding-tool directories (Claude, Copilot, Codex, Gemini, Cursor, Grok, Qwen, Tabnine, Goose, and more). Beyond scaffolding, the tool manages a layered ecosystem of extensions, presets, bundles, and YAML-defined workflows, plus a self-upgrade path that detects uv/pipx/uvx installations and pulls releases from the GitHub API.
Packmind is a developer productivity platform that centralizes engineering standards and distributes them to AI coding agents. It solves the problem of scattered standards (in Slack, Notion, PR comments) that AI agents can't access and the manual copy-pasting of standards across repositories. The platform provides organization/space-based collaboration, Git provider integrations, LLM/AI service abstraction, marketplace for sharing components, deployment/target management, and change proposal workflows. It features a hexagonal architecture with clear separation of concerns across domains (accounts, organizations, spaces, standards, skills, commands, deployments), using NestJS for the backend, React for the frontend, and a separate CLI tool for local artifact management.
This repository is a comprehensive operational playbook for 'Ralph', Geoff Huntley's methodology for running Claude Code in an autonomous loop to build software. The core deliverable is a detailed guide (README.md plus a styled index.html mirror) explaining the three-phase workflow: define requirements as JTBD-aligned specs, generate an implementation plan via a planning loop, then run a building loop where Claude picks tasks from the plan, implements them, validates against tests, and commits — with a fresh context window per iteration. The guide is unusually well-structured, covering context management, backpressure via tests, sandbox security, and tuning strategies. Beyond documentation, the repo ships functional artifacts: two bash loop scripts (files/loop.sh, files/loop_streamed.sh) that orchestrate the Claude CLI, a dependency-free Node.js stream parser (files/parse_stream.js) for readable terminal output, and five prompt templates (PROMPT_build.md, PROMPT_plan.md, PROMPT_specs.md, PROMPT_reverse_engineer_specs.md) encoding the methodology's guardrails. It also documents sandbox environment options (E2B, Fly Sprites, Modal, Cloudflare, Docker) for safely running autonomous agents with --dangerously-skip-permissions. The prompt templates are the real product — they encode hard-won operational knowledge about context efficiency, subagent fan-out, and steering LLM behavior. Notable patterns include escalating 9s-numbered guardrails, explicit subagent limits (500 for search, 1 for build/tests), the 'one sentence without and' topic scoping test, and a two-phase reverse-engineering prompt that produces implementation-free behavioral specs. This is a documentation-first repo with a small but functional code footprint.
This repository is a comprehensive, tool-agnostic skills and steering pack that teaches AI coding agents to perform AWS Well-Architected Framework reviews. It contains a curated reference corpus of over 2,000 markdown files (6 pillars, 307 best practices, 27 industry lenses) derived from official AWS documentation, plus per-tool adapters for 15 AI coding assistants (Claude Code, Cursor, Codex, GitHub Copilot, etc.). The repo also ships production-grade supporting tooling: a CI gating tool (wa-ci) that diffs structured review JSON against a baseline and blocks PRs on regressed gaps; an evaluation harness with blind multi-reviewer adversarial adjudication; a crawler to regenerate the reference corpus from AWS docs; and one-command installers. The core skill dispatches six parallel subagents (one per pillar) to keep the 2.2 MB corpus out of any single context window. Despite the extensive content, the repository has notable maintenance challenges: the reference snapshot will drift as AWS documentation evolves (users are responsible for regeneration); lens files are duplicated across `powers/` and `skills/` directories creating a sync hazard; and the flagship skill is explicitly deprecated in favor of a successor in Agent Toolkit for AWS. The evaluation harnesses and CI tooling are well-crafted and reusable, but the sheer scale of markdown files and platform-specific adapters makes ongoing synchronization complex.