AIWG
Reusable project context and specialist workflows for the AI coding tools you already use, so decisions persist across sessions.
- No source files were provided for this review; the result is neither a positive nor a negative judgment on this repository.
- Provide revision-pinned README, LICENSE, package., workflows, and representative agent/workflow files to enable a substantive assessment.
What does this agent do, and when should you use it?
AIWG is a cognitive-architecture framework for AI-augmented software development that packages agents, skills, rules, templates, and workflow utilities into a single source of truth deployable across 15 named AI platforms. Its deployment layer copies Markdown/YAML source files into the provider-native paths each tool reads via `aiwg use`, while project outputs persist in the `.aiwg/` artifact directory for reuse across sessions. Six core components cover semantic memory, multi-agent deliberation with synthesis, the Ralph closed-loop self-correction cycle, bidirectional traceability, Cooper Stage-Gate phase planning, and controllable voice generation. Nine domain frameworks (SDLC Complete, Forensics Complete, Research Complete, and others) ship alongside dozens of opt-in addons such as Agent Loop, Compound Memory, and Dataset Intelligence. It is not a model and does not replace a provider subscription: the deployment core writes plain-text files the platform reads natively, and optional orchestration components must be explicitly enabled.
After installing, aiwg use all --provider <provider> copies kernel skills, specialist agents (Security Auditor, Test Architect, etc.), rules, and templates into provider-native directories (.claude/skills/, .codex/agents/, and so on), builds an artifact index, emits AIWG.md / AGENTS.md at the project root, self-verifies the deployment, and reports whether a provider reload is required. Day to day, you describe outcomes in natural language; the assistant searches AIWG's capability graphs, loads matching skills, and follows workflows shaped as Primary Author → Parallel Reviewers → Synthesizer → Human Gate → Archive. Work products — requirements, architecture decisions, test strategies, deployment plans — are saved in .aiwg/, cross-referenced with @-mentions, and linked from code via @implements annotations for doc-to-code-to-test traceability. The optional Ralph addon runs bounded execute-verify-learn-retry iterations with checkpoints, while aiwg doctor diagnoses deployment health.
- An engineering team starting a multi-month feature project that needs architecture, security, and testability reviews with explicit phase-gate approvals.
- A legacy system migration requiring phased rollback strategies and architecture decision records that survive across sessions.
- Compliance-heavy domains (healthcare, finance, aerospace) that need audit trails and a traceable requirements-code-tests chain.
- An incident-response team using the forensics framework for evidence acquisition, log analysis, persistence hunting, timeline building, and IOC extraction.
- Marketing or research teams that need reusable briefs, source records, citation checking, and content that carries forward between sessions.
- An individual developer who wants structured reviews inside an existing Claude Code, Codex, or Cursor session without switching tools.
What are this agent's strengths and limitations?
- One source of truth deploys to 15 named platforms plus a generic adapter, eliminating per-tool duplication of instructions.
- The
.aiwg/artifact memory plus bidirectional traceability (Doc↔Code↔Tests) makes decisions and review findings inspectable and auditable across sessions. - Unusual breadth: SDLC Complete alone ships 100 agents, 116 skills, 217 templates, and 39 rules, with eight more domain frameworks including forensics, marketing, research, ops, and security engineering.
- Project-local extensions are byte-identical to their upstream form, enabling zero-rewrite, hash-verified promotion.
- Requires Node.js >= 20 and an existing AI platform subscription; it replaces neither, and most workflows operate on project artifacts rather than an independent runtime.
- Installation has many documented failure paths: three deployment scopes, optional native features, macOS EACCES, and PATH issues.
- The README states plainly that a role definition is not a separate model and a written rule is not proof of enforcement; multiple reviewers can share an error, and benefits depend on keeping artifacts current.
- A full lifecycle is overkill for small tasks; extra context, reviewers, and verification steps add model calls and human effort.
How do you install or deploy this agent?
Prerequisites: Node.js >= 20.0.0 (prefer Node 24 for new installs) and a supported AI platform. Standard install: npm i -g aiwg, then cd /path/to/your/project && aiwg use all --provider claude (replace claude with your provider: codex, copilot, cursor, warp, factory, opencode, devin, and others). For a lighter install resolving signed, versioned resources, use npm i -g @aiwg/cli. Optional: install AIWG Cockpit with the self-hosted Agentic Sandbox executor (Docker or KVM/libvirt) via the guided prompt at https://aiwg.io/agentic-sandbox/setup.aiwg.yaml. On macOS, if npm fails with EACCES under /usr/local/lib/node_modules, follow the macOS install guide and avoid sudo global installs.
How do you use this agent?
After deployment, issue natural-language requests in your AI tool, such as "Set up the SDLC framework for this project and my AI tool" or "Review this design for security risks" — the assistant handles discovery, artifact lookup, and workflow tools. Deploy individual frameworks on demand: aiwg use sdlc --provider claude, aiwg use forensics --provider claude, aiwg use research --provider claude, etc. Maintenance commands: aiwg refresh regenerates context, aiwg doctor checks deployment health, and npx aiwg doctor works without PATH changes. Author project-local extensions directly under .aiwg/{extensions,addons,frameworks}/<name>/ for automatic discovery by aiwg use, no fork required.