AIWG

Multi-agent AI framework for Claude Code, Copilot, Cursor, Warp, and 6 more platforms

200+ agents, 67+ CLI commands, 400+ deployable agent/skill/command/rule artifacts, 8 core frameworks + training marketplace plugin, 27 addons. SDLC workflows, digital forensics, research management, marketing operations, media curation, ops infrastructure, knowledge base, and fine-tuning dataset curation — all deployable with one command.

npm i -g aiwg        # install globally
aiwg use sdlc        # deploy SDLC framework

Get Started · Features · Agents · CLI Reference · Documentation · Community

Installation Troubleshooting

If aiwg is not found after npm i -g aiwg, the npm global bin directory is not on your PATH. Confirm and fix:

which aiwg                              # empty? PATH is the issue
npm config get prefix                   # find npm's global prefix (bin lives under here)
echo 'export PATH="$(npm config get prefix)/bin:$PATH"' >> ~/.zshrc   # or ~/.bashrc
source ~/.zshrc                         # or restart your shell

You can also invoke AIWG without adjusting PATH by using npx aiwg <command>. For a broader health check — version, deployed providers, missing dependencies, kernel-skill probes — run aiwg doctor, which surfaces the same PATH guidance on every invocation if the binary isn't reachable.

What AIWG Is

AIWG is a deployment tool and support utility for AI context. At its core, aiwg use copies markdown and YAML source files into the specific paths each AI platform looks in — .claude/agents/, ~/.codex/skills/, .cursor/rules/, .github/prompts/, and six more — so one source of truth works across 10 platforms.

Around that core, AIWG ships utilities for things the base platforms do not handle on their own: persistent artifact memory (.aiwg/), background orchestration (aiwg mc), autonomous loops (aiwg ralph), artifact indexing (aiwg index), cost telemetry, health diagnostics, and more. Most are opt-in. The deployment layer works standalone as plain text files the platform reads natively.

Project scope (recommended) vs user scope (global)

aiwg use writes artifacts at one of two scopes. Both are first-class supported (see ADR-NUA-001 in .aiwg/studies/novice-user-adoption/):

• Project scope — default. Run aiwg use sdlc from a project root and the artifacts land in ./.claude/agents/, ./.claude/skills/, etc. One project's agent set never bleeds into another's session. This is the recommended default for most use cases.
• User scope (global install) — aiwg use sdlc --scope user writes to ~/.claude/agents/, ~/.claude/skills/, etc. Same artifact set loads into every session, regardless of project. Fits "AIWG in every conversation" workflows and is the canonical mode for OpenClaw and Hermes (whose primary discovery is user-scope).

The trade-off is real: when the same agent set loads into every session, context from one project can bleed into reasoning about another. Research (REF-720, Lost in Multi-Turn Conversation, MSR/Salesforce 2025) measured a 39% capability drop when this happens. The non-blocking project-isolation warning surfaces the trade-off at deploy time so the scope choice is informed. Neither scope is wrong; pick the one that fits the workflow.

See docs/cli-reference.md (under aiwg use → "Scope models") for the per-provider details and the global-install rough-edge inventory.

Simple Building Blocks

AIWG ships five primitive artifact types. All are plain text:

• Agents — specialized personas (Security Auditor, Test Architect) with a scoped toolset
• Skills — natural-language workflows the platform auto-invokes on trigger phrases
• Commands — explicit slash invocations (/flow-security-review-cycle)
• Rules — enforcement directives the platform loads into every session
• Behaviors — lifecycle hooks that fire on events (pre-write, post-session)

Each is a single .md file with YAML frontmatter. Nothing executes until an AI platform reads it.

Why It Compounds

Because the primitives are text, they compose without runtime coordination:

• One agent file becomes one member of a 180-agent SDLC team that reviews architecture, tests, security, and compliance in parallel.
• One skill becomes a natural-language entry point — "run security review" routes to the right multi-agent flow on every platform that supports skills.
• One framework (SDLC, forensics, marketing) bundles dozens of agents + skills + rules + templates that cross-reference each other. Deploying a framework deploys a working multi-agent ecosystem.
• The .aiwg/ directory gives those agents a shared memory — artifacts from Monday's requirements session are read by Thursday's test design.
• Flows orchestrate Primary Author → Parallel Reviewers → Synthesizer → Archive patterns that no single-prompt workflow can match.

The leverage is not in any one file. It is that hundreds of small files — each independently readable and editable — snap together into workflows that would otherwise take a bespoke agent platform to build.

This is also where the research background lives. AIWG implements patterns from cognitive science (Miller 1956, Sweller 1988), multi-agent systems (Jacobs et al. 1991, MetaGPT, AutoGen), and software engineering (Cooper's stage-gate, FAIR Principles, W3C PROV) — applied as file conventions and deployment rules, not as a runtime you depend on.

What's Optional

These are CLI tools and services on top of the text-file substrate. The substrate works without them:

• aiwg ralph — autonomous iterate-until-done loops
• aiwg mc — background mission-control for parallel tasks
• aiwg daemon — persistent session manager
• aiwg discover — capability search across AIWG's 400+ skills/agents/commands/rules
• aiwg show — fetch the full body of a specific skill / agent / command / rule
• aiwg index — searchable artifact index (project + codebase + framework graphs)
• aiwg mcp — MCP server for runtime tool access

Turn any of these on when you want persistence, parallelism, or automation. Turn them off and your deployed agents, skills, and rules still work — they are still text files the platform reads natively.

What AIWG Is Not

• Not a prompt library. Prompts are the artifacts, not the product. The product is placing the right prompts where the platform finds them.
• Not an LLM runtime. AIWG never calls a model. The AI platform you already use does that; AIWG configures what it sees.
• Not a framework you import into your app. Nothing is imported at build time. Your project gets a .aiwg/ directory (artifacts) and a few provider-specific context dirs (deployed copies). Delete them and your app is unchanged.

Who It's For

If you have used AI coding assistants and thought "this is amazing for small tasks but falls apart on anything complex," AIWG is the missing infrastructure layer that scales AI assistance to multi-week projects.

What Problems Does AIWG Solve?

Base AI assistants (Claude, GPT-4, Copilot without frameworks) have three fundamental limitations:

1. No Memory Across Sessions

Each conversation starts fresh. The assistant has no idea what happened yesterday, what requirements you documented, or what decisions you made last week. You re-explain context every morning.

Without AIWG: Projects stall as context rebuilding eats time. A three-month project requires continuity, not fresh starts every session.

With AIWG: The .aiwg/ directory maintains 50-100+ interconnected artifacts across days, weeks, and months. Later phases build on earlier ones automatically because memory persists. Agents read prior work via @-mentions instead of regenerating from scratch.

The segmented structure also makes large projects tractable. As code files grow, the project doesn't become harder to reason about — agents load only the slice of memory relevant to the current task (@requirements/UC-001.md, @architecture/sad.md, @testing/test-plan.md) rather than the entire codebase. Each subdirectory is a focused knowledge domain that fits comfortably in context, while cross-references keep everything connected.

The artifact index (aiwg index) takes this further. Without any tooling, agents often need to browse 3-6 documents before finding what they need. AIWG's structured artifacts reduce this to 2-3. With the index enabled, agents resolve artifact lookups in one query more often than not — a direct hit on the right requirement, architecture decision, or test case without browsing.

2. No Recovery Patterns

When AI generates broken code or flawed designs, you manually intervene, explain the problem, and hope the next attempt works. There is no systematic learning from failures, no structured retry, no checkpoint-and-resume.

Without AIWG: Research shows 47% of AI workflows produce inconsistent outputs without reproducibility constraints (R-LAM, Sureshkumar et al. 2026). Debugging is trial-and-error.

With AIWG: The agent loop implements closed-loop self-correction — execute, verify, learn from failure, adapt strategy, retry. External Ralph survives crashes and runs for 6-8+ hours autonomously. Debug memory accumulates failure patterns so the agent doesn't repeat mistakes.

3. No Quality Gates

Base assistants optimize for "sounds plausible" not "actually works." A general assistant critiques security, performance, and maintainability simultaneously — poorly. No domain specialization, no multi-perspective review, no human approval checkpoints.

Without AIWG: Production code ships without architectural review, security validation, or operational feasibility assessment.

With AIWG: 162 specialized agents provide domain expertise — Security Auditor reviews security, Test Architect reviews testability, Performance Engineer reviews scalability. Multi-agent review panels with synthesis. Human-in-the-loop gates at every phase transition. Research shows 84% cost reduction keeping humans on high-stakes decisions versus fully autonomous systems (Agent Laboratory, Schmidgall et al. 2025).

The Six Core Components

1. Memory — Structured Semantic Memory

The .aiwg/ directory is a persistent artifact repository storing requirements, architecture decisions, test strategies, risk registers, and deployment plans across sessions. This implements Retrieval-Augmented Generation patterns (Lewis et al., 2020) — agents retrieve from an evolving knowledge base rather than regenerating from scratch.

Each artifact is discoverable via @-mentions (e.g., @.aiwg/requirements/UC-001-login.md). Context sharing between agents happens through artifacts: the requirements analyst writes use cases, the architecture designer reads them.

2. Reasoning — Multi-Agent Deliberation with Synthesis

Instead of a single general-purpose assistant, AIWG provides 162 specialized agents organized by domain. Complex artifacts go through multi-agent review panels:

Architecture Document Creation:
  1. Architecture Designer drafts SAD
  2. Review Panel (3-5 agents run in parallel):
     - Security Auditor    → threat perspective
     - Performance Engineer → scalability perspective
     - Test Architect       → testability perspective
     - Technical Writer     → clarity and consistency
  3. Documentation Synthesizer merges all feedback
  4. Human approval gate → accept, iterate, or escalate

Research shows 17.9% accuracy improvement with multi-path review on complex tasks (Wang et al., GSM8K benchmarks, 2023). Agent specialization means security review is done by a security specialist, not a generalist.

3. Learning — Closed-Loop Self-Correction (Ralph)

Ralph executes tasks iteratively, learns from failures, and adapts strategy based on error patterns. Research from Roig (2025) shows recovery capability — not initial correctness — predicts agentic task success.

Ralph Iteration:
  1. Execute task with current strategy
  2. Verify results (tests pass, lint clean, types check)
  3. If failure: analyze root cause → extract structured learning → adapt strategy
  4. Log iteration state (checkpoint for resume)
  5. Repeat until success or escalate to human after 3 failed attempts

External Ralph adds crash resilience: PID file tracking, automatic restart, cross-session persistence. Tasks run for 6-8+ hours surviving terminal disconnects and system reboots.

4. Verification — Bidirectional Traceability

AIWG maintains links between documentation and code to ensure artifacts stay synchronized:

// src/auth/login.ts
/**
 * @implements @.aiwg/requirements/UC-001-login.md
 * @architecture @.aiwg/architecture/SAD.md#section-4.2
 * @tests @test/unit/auth/login.test.ts
 */
export function authenticateUser(credentials: Credentials): Promise<AuthResult> {

Verification types: Doc → Code, Code → Doc, Code → Tests, Citations → Sources. The retrieval-first citation architecture reduces citation hallucination from 56% to 0% (LitLLM benchmarks, ServiceNow 2025).

5. Planning — Phase Gates with Cognitive Load Management

AIWG structures work using Cooper's Stage-Gate methodology (1990), breaking multi-month projects into bounded phases with explicit quality criteria and human approval:

Inception → Elaboration → Construction → Transition → Production
   LOM          ABM            IOC            PR

Cognitive load optimization follows Miller's 7±2 limits (1956) and Sweller's worked examples approach (1988):

• 4 phases (not 12)
• 3-5 artifacts per phase (not 20)
• 5-7 section headings per template (not 15)
• 3-5 reviewers per panel (not 10)

6. Style — Controllable Voice Generation

Voice profiles provide continuous control over AI writing style using 12 parameters (formality, technical depth, sentence variety, jargon density, personal tone, humor, directness, examples ratio, uncertainty acknowledgment, opinion strength, transition style, authenticity markers).

Built-in voices: technical-authority (docs, RFCs), friendly-explainer (tutorials), executive-brief (summaries), casual-conversational (blogs, social). Create custom voices from your existing content with /voice-create.

A Real Project Walkthrough

Here is how the six components work together across a project lifecycle. How long each phase takes depends entirely on the project — AIWG is a force multiplier, not a clock. Most projects arrive at a complete, reviewed document set in hours to a day. What takes time is the human work that matters: reviewing, editing, and making decisions. The more input your team provides, the better the output. AIWG memory lets operators participate through the tools they already use — industry-standard documents and templates, issues, and knowledge bases.

Inception

/intake-wizard "Build customer portal with real-time chat" --interactive

Memory: Intake forms capture goals, constraints, stakeholders in .aiwg/intake/ Planning: Executive Orchestrator guides through structured questionnaire Reasoning: Requirements Analyst drafts initial use cases, Product Designer reviews UX Verification: Requirements reference intake forms, ensuring alignment Human Gate: Stakeholder reviews intake → approves transition to Elaboration

Elaboration

/flow-inception-to-elaboration

Memory: Architecture doc, ADRs, threat model, test strategy accumulate in .aiwg/ Reasoning: Multi-agent review panel — Architecture Designer drafts, Security Auditor + Performance Engineer + Test Architect critique in parallel, Documentation Synthesizer merges Learning: Ralph iterates on ADRs (generate options, evaluate against constraints, refine) Style: Technical documents use technical-authority, stakeholder summaries use executive-brief Human Gate: Architect reviews SAD, security team approves threat model

Construction

/flow-elaboration-to-construction
/ralph "Implement authentication module" --completion "npm test passes"

Learning: Ralph handles implementation iterations — execute, verify (run tests), learn ("async race condition in token refresh"), adapt (add synchronization), retry Verification: Code references requirements (@implements UC-001), tests reference code Memory: Test plans, implementation, deployment scripts accumulate across iterations Human Gate: Code review approves merges, QA approves test results

Transition

/flow-deploy-to-production
/flow-hypercare-monitoring 14

Planning: Deployment checklist — monitoring, rollback plan, incident response Learning: Ralph retries deployment steps if validation fails Verification: Deployment scripts reference architecture (which services, what order) Human Gate: Operations team reviews deployment plan → approves production release

Quantified Claims and Evidence

AIWG makes specific, falsifiable claims backed by peer-reviewed research:

| Claim | Evidence | Source | |-------|----------|--------| | 84% cost reduction with human-in-the-loop vs fully autonomous | Agent Laboratory study | Schmidgall et al. (2025) | | 47% workflow failure rate without reproducibility constraints | R-LAM evaluation | Sureshkumar et al. (2026) | | 0% citation hallucination with retrieval-first vs 56% generation-only | LitLLM benchmarks | ServiceNow (2025) | | 17.9% accuracy improvement with multi-path review | GSM8K benchmarks | Wang et al. (2023) | | 18.5x improvement with tree search on planning tasks | Game of 24 results | Yao et al. (2023) |

Full references: docs/research/

When to Use AIWG (and When Not To)

Good Fit

Multi-week or multi-month projects where requirements evolve, multiple stakeholders have different concerns, quality gates are required, auditability matters, or context exceeds conversation limits.

Examples: New product features with architecture/security/operational implications, legacy system migrations requiring phased rollback strategies, research projects needing literature review and reproducibility, compliance-heavy domains (healthcare, finance, aerospace) needing audit trails.

Not the Best Fit

Single-session tasks where no memory is needed, quality gates are overkill, and overhead exceeds value.

Examples: "Write a Python script to parse this CSV," "Fix this typo," "Explain how this code works."

The Trade-off

AIWG adds structure (templates, phases, gates) that slows trivial tasks but scales to complex multi-week workflows. If your project fits in a single conversation, use a base assistant. If it spans days, weeks, or months, AIWG provides the infrastructure to maintain quality and context.

User intent → AIWG CLI → Deploy agents + rules + templates → AI platform
                │                                                │
                ▼                                                ▼
         "aiwg use sdlc"                              Claude Code / Copilot /
                │                                     Cursor / Warp / Factory /
                ▼                                     OpenCode / Codex / Windsurf
         ┌──────────────┐
         │ 188 Agents   │  Specialized AI personas with domain expertise
         │ 50 Commands  │  CLI + slash commands for workflow automation
         │ 128 Skills   │  Natural language workflow triggers
         │ 35 Rules     │  Enforcement patterns (security, quality, anti-laziness)
         │ 334 Templates│  SDLC artifact templates with progressive disclosure
         └──────────────┘
                │
                ▼
         .aiwg/ artifacts ← Persistent project memory across sessions

How It Works

For visual diagrams of AIWG's architecture, deploy flow, and discovery model, see docs/architecture-overview.md. The prose walkthrough lives in docs/how-it-works.md.

At a glance — AIWG is a deploy-time tool. aiwg use copies plain-text files into your AI platform's native directories and exits. Nothing runs in the background; the AI platform's own loader handles everything from there.

flowchart LR
  subgraph Source["AIWG framework source"]
    direction TB
    KERN[16 kernel skills<br/>~15-25k tokens]
    STD[~385 standard skills<br/>read from $AIWG_ROOT]
    AGENT[200+ agents]
    RULES[60+ rules]
    TPL[100+ templates]
  end

  CLI([aiwg use sdlc<br/>--provider X]) --> DEPLOY

  subgraph DEPLOY["Deploy step (one-shot)"]
    direction TB
    COPY[Copy kernel skills, agents,<br/>rules to provider-native dirs]
    INDEX[Build artifact index<br/>~/.local/share/aiwg/index/]
    CTX[Emit AIWG.md + AGENTS.md<br/>at project root]
  end

  Source --> CLI
  DEPLOY --> Project

  subgraph Project["Your project (after deploy)"]
    direction TB
    PLAT[.claude/skills/<br/>.codex/agents/<br/>.warp/agents/ ...]
    AIWGMD[AIWG.md / .hermes.md /<br/>WARP.md / AGENTS.md]
    ART[.aiwg/<br/>requirements/<br/>architecture/<br/>...]
  end

  Project --> SESS

  subgraph SESS["AI session (Claude / Codex / Hermes / etc.)"]
    direction TB
    NATIVE[Platform-native loader<br/>reads provider dir]
    DISC([Optional: aiwg discover<br/>+ aiwg show])
  end

  classDef optional stroke-dasharray: 5 5,fill:#fef9e7
  class DISC optional
  class INDEX optional

Multi-agent orchestration — once deployed, AIWG coordinates specialized agents through phase-gated workflows:

You: "transition to elaboration phase"

AIWG: [Step 1] Requirements Analyst   → Analyze vision document, generate use case briefs
      [Step 2] Architecture Designer  → Baseline architecture, identify technical risks     } parallel
      [Step 3] Security Architect     → Threat model, security requirements                 }
      [Step 4] Documentation Synth.   → Merge reviews into Architecture Baseline Milestone
      [Step 5] Human Gate             → GO / CONDITIONAL_GO / NO_GO decision
      [Step 6] → Next phase or iterate

The orchestration pattern: Primary Author → Parallel Reviewers → Synthesizer → Human Gate → Archive. Agents run in parallel where possible, with human-in-the-loop checkpoints at phase transitions.

Features

• 188 specialized agents — domain experts across testing, security, architecture, DevOps, cloud, frontend, backend, data engineering, documentation, and more
• 50 CLI commands — framework deployment, project scaffolding, iterative execution, metrics, reproducibility validation
• 128 workflow skills — natural language triggers for regression testing, forensics, voice profiles, quality gates, and CI/CD integration
• 35 enforcement rules — anti-laziness detection, token security, citation integrity, executable feedback, failure mitigation across 6 LLM archetypes
• 334 artifact templates — progressive disclosure templates for requirements, architecture, testing, security, deployment, and more
• 8 platform support — deploy to Claude Code, Copilot, Cursor, Warp, Factory AI, OpenCode, Codex, and Windsurf
• 8 core frameworks + training marketplace plugin — SDLC, Digital Forensics, Marketing Operations, Research Management, Media Curation, Ops Infrastructure, Knowledge Base, Security Engineering, plus aiwg-training for fine-tuning dataset curation (corpus-to-dataset pipeline with DPO/KTO/ORPO/SimPO export)
• 27 addons — semantic-memory kernel, llm-wiki (Obsidian-native knowledge base), RLM recursive decomposition, voice profiles, testing quality, mutation testing, UAT automation, and more
• Agent Loop — iterative task execution with automatic error recovery and crash resilience (6-8 hour sessions)
• RLM addon — recursive context decomposition for processing 10M+ tokens via sub-agent delegation
• YAML metalanguage — declarative schema-validated workflow definitions (JSON Schema 2020-12)
• MCP server — Model Context Protocol integration for tool-based AI workflows
• Bidirectional traceability — @-mention system linking requirements → architecture → code → tests
• FAIR-aligned artifacts — W3C PROV provenance, GRADE quality assessment, persistent REF-XXX identifiers
• Reproducibility validation — deterministic execution modes, checkpoints, configuration snapshots

Quick Start

Prerequisites: Node.js >=20.0.0 and an AI platform (Claude Code, GitHub Copilot, Cursor, Warp Terminal, or others). New installs should prefer Node 24. See Prerequisites Guide for details.

Verifying releases (v2026.5.3+): Every AIWG release ships with Sigstore-anchored npm provenance, a signed git tag, a cosign keyless tarball signature, and a signed CycloneDX SBOM. Verification is optional but recommended:

npm view [email protected] --json | jq .dist.attestations

Full walkthrough at docs/releases/verifying.md. Adopt the same pattern for your own packages: docs/security/supply-chain-hardening.md.

Install & Deploy

# Install globally
npm install -g aiwg

# Deploy to your project
cd your-project
aiwg use sdlc              # Full SDLC framework (98 agents, 38 rules, 200+ templates)
aiwg use forensics         # Digital forensics & incident response (13 agents, 10 skills)
aiwg use marketing         # Marketing operations (37 agents, 87+ templates)
aiwg use media-curator     # Media archive management (6 agents, 9 commands)
aiwg use research          # Research workflow automation (8 agents, 8-stage pipeline)
aiwg use rlm               # RLM addon (recursive context decomposition)
aiwg use all               # Everything

# Or scaffold a new project
aiwg new my-project

# Check installation health
aiwg doctor

Customize Without Forking

Author project-specific rules, skills, agents, addons, or frameworks directly under .aiwg/{extensions,addons,frameworks,plugins}/<name>/. No fork, no rebuild. Discovered automatically by aiwg use.

aiwg new-bundle my-team-rules --type extension --starter rule
# edit the rule, then:
aiwg use my-team-rules
aiwg doctor --project-local      # health check (counts, validation, drift)
aiwg promote my-team-rules        # graduate to upstream when proven

The bundle is byte-identical in shape to its upstream form, so aiwg promote is a hash-verified copy with zero rewrite. See the customization guide for the three paths (project-local, fork, corpus).

Claude Code Plugin (Alternative)

/plugin marketplace add jmagly/ai-writing-guide
/plugin install sdlc@aiwg

Multi-Platform Deployment

aiwg use sdlc                          # Claude Code (default)
aiwg use sdlc --provider copilot       # GitHub Copilot
aiwg use sdlc --provider cursor        # Cursor
aiwg use sdlc --provider warp          # Warp Terminal
aiwg use sdlc --provider factory       # Factory AI
aiwg use sdlc --provider opencode      # OpenCode
aiwg use sdlc --provider openai        # OpenAI/Codex
aiwg use sdlc --provider windsurf      # Windsurf

What You Get

Frameworks (6)

| Framework | Agents | Templates | What It Does | |-----------|--------|-----------|--------------| | SDLC Complete | 98 | 200+ | Full software development lifecycle — Inception through Production with multi-agent orchestration, quality gates, and DORA metrics | | Forensics Complete | 13 | 8 | Digital forensics and incident response — evidence acquisition, timeline reconstruction, IOC extraction, Sigma rule hunting. NIST SP 800-86, MITRE ATT&CK, STIX 2.1 | | Media/Marketing Kit | 37 | 87+ | End-to-end marketing operations — strategy, content creation, campaign management, brand compliance, analytics, and reporting | | Media Curator | 6 | — | Intelligent media archive management — discography analysis, source discovery, quality filtering, metadata curation, multi-platform export (Plex, Jellyfin, MPD) | | Research Complete | 8 | 6 | Academic research automation — paper discovery, citation management, RAG-based summarization, GRADE quality scoring, FAIR compliance, W3C PROV provenance | | Ops Complete | 12 | 3 | Operational infrastructure — incident management, runbooks, troubleshooting workflows | | Security Engineering | 2 | 5 | Applied security beyond STRIDE/OWASP — cryptographic primitive selection, chain-of-trust integrity, authentication-factor architecture, degraded-mode design, runtime secret hygiene, supply-chain trust, physical-access threats. Pattern-based, product-agnostic |

Addons (27)

| Addon | What It Does | |-------|--------------| | RLM | Recursive context decomposition — process 10M+ tokens via sub-agent delegation with parallel fan-out | | Writing Quality | Content validation, AI pattern detection, authentic voice enforcement | | Testing Quality | TDD enforcement, mutation testing, flaky test detection and repair | | Voice Framework | 4 built-in voice profiles (technical-authority, friendly-explainer, executive-brief, casual-conversational) with create/blend/apply skills | | UAT-MCP Toolkit | User acceptance testing with MCP-powered test execution, coverage tracking, and regression detection | | AIWG Evals | Agent evaluation framework — archetype resistance testing (Roig 2025), performance benchmarks, quality scoring | | Agent Loop | Iterative task execution engine (aiwg ralph / aiwg agent-loop-ext) — automatic error recovery, crash resilience, completion tracking | | Agentic Installer | setup.aiwg.io/v1 SetupManifest installer — cross-platform install workflows with recovery | | AIWG Dev | AIWG development tooling — extension scaffolding, local-source dev mode | | Daemon | Persistent daemon mode — background sessions, task queue, health monitoring | | LLM Wiki | Obsidian-native knowledge base for LLM agents — semantic linking, vault integration | | NLP Prod | Production NLP pipelines — entity extraction, classification, summarization | | Prose Integration | OpenProse contract grammar integration — declarative service contracts | | Semantic Memory | Semantic memory kernel — query, capture, lifecycle management for agent memory | | Context Curator | Context pre-filtering to remove distractors — production-grade agent reliability | | Verbalized Sampling | Probability distribution prompting — 1.6-2.1x output diversity improvement | | Guided Implementation | Bounded iteration control for issue-to-code automation | | Skill Factory | Dynamic skill generation and packaging at runtime | | Doc Intelligence | Document analysis, PDF extraction, documentation site scraping | | Color Palette | WCAG-compliant color palette generation with trend research | | Auto Memory | Automatic memory seed templates for new project context initialization | | Agent Persistence | Agent state management for session continuity | | AIWG Hooks | Lifecycle event handlers — pre-session, post-write, workflow tracing | | AIWG Utils | Core meta-utilities (auto-installed with any framework) | | Droid Bridge | Factory Droid orchestration — multi-platform agent bridge | | Star Prompt | Repository star prompt for success celebration |

Agents (188)

Specialized AI personas deployed to your platform with defined tools, responsibilities, and operating rhythms.

SDLC Agents (90)

| Domain | Agents | Examples | |--------|--------|---------| | Testing & Quality | 11 | Test Engineer, Test Architect, Mutation Analyst, Regression Analyst, Laziness Detector, Reliability Engineer | | Security & Compliance | 9 | Security Auditor, Security Architect, Compliance Checker, Privacy Officer, Citation Verifier | | Architecture & Design | 12 | Architecture Designer, API Designer, Cloud Architect, System Analyst, Product Designer, Decision Matrix Expert | | DevOps & Cloud | 8 | AWS Specialist, Azure Specialist, GCP Specialist, Kubernetes Expert, DevOps Engineer, Multi-Cloud Strategist | | Backend & Data | 10 | Django Expert, Spring Boot Expert, Data Engineer, Database Optimizer, Software Implementer, Incident Responder | | Frontend & Mobile | 6 | React Expert, Frontend Specialist, Mobile Developer, Accessibility Specialist, UX Lead | | AI/ML & Performance | 5 | AI/ML Engineer, Performance Engineer, Cost Optimizer, Metrics Analyst | | Code Quality | 11 | Code Reviewer, Debugger, Dead Code Analyzer, Technical Debt Analyst, Legacy Modernizer | | Documentation | 7 | Technical Writer, Documentation Synthesizer, Documentation Archivist, Context Librarian | | Requirements & Planning | 7 | Requirements Analyst, Requirements Reviewer, Intake Coordinator, RACI Expert | | Agent/Tool Smiths | 9 | AgentSmith, CommandSmith, MCPSmith, SkillSmith, ToolSmith | | Governance & Meta | 3 | Executive Orchestrator, Recovery Orchestrator, Migration Planner |

Forensics Agents (13)

| Agent | What It Does | |-------|-------------| | Forensics Orchestrator | Coordinates full investigation lifecycle from scoping through reporting | | Triage Agent | Quick volatile data capture following RFC 3227 volatility order | | Acquisition Agent | Evidence collection with chain of custody and SHA-256 hash verification | | Log Analyst | Auth.log, syslog, journal, and application log analysis for brute force, privilege escalation, lateral movement | | Persistence Hunter | Sweeps cron, systemd, SSH keys, LD_PRELOAD, PAM modules, kernel modules — maps to MITRE ATT&CK | | Container Analyst | Docker, containerd, Kubernetes forensics — privilege escalation, container escapes, eBPF monitoring | | Network Analyst | Connection state, DNS, traffic patterns — beaconing, C2, data exfiltration detection | | Memory Analyst | Volatility 3 memory forensics — process analysis, rootkit detection, credential extraction | | Cloud Analyst | AWS/Azure/GCP audit logs, IAM review, network flows, API activity anomaly detection | | Timeline Builder | Multi-source event correlation — chronological incident timelines with attribution | | IOC Analyst | IOC extraction, enrichment, STIX 2.1 formatting — actionable IOC register | | Recon Agent | Target reconnaissance — system topology, services, users, network baselines | | Reporting Agent | Structured forensic reports — executive summary, technical findings, timeline, remediation |

Marketing Agents (37)

| Domain | Agents | |--------|--------| | Strategy | Campaign Strategist, Brand Guardian, Positioning Specialist, Market Researcher, Content Strategist, Channel Strategist | | Creation | Copywriter, Content Writer, Email Marketer, Social Media Specialist, SEO Specialist, Graphic Designer, Art Director | | Management | Campaign Orchestrator, Production Coordinator, Traffic Manager, Asset Manager, Workflow Coordinator | | Analytics | Marketing Analyst, Data Analyst, Attribution Specialist, Reporting Specialist, Budget Planner | | Communications | PR Specialist, Crisis Communications, Corporate Communications, Internal Communications, Media Relations |

Research Agents (8)

Discovery Agent, Acquisition Agent, Documentation Agent, Citation Agent, Quality Agent, Archival Agent, Provenance Agent, Workflow Agent

Media Curator Agents (6)

Discography Analyst, Source Discoverer, Acquisition Manager, Quality Assessor, Metadata Curator, Completeness Tracker

Rules (35)

Enforcement patterns that prevent common AI failure modes. Rules deploy automatically with their framework.

Core Rules (10) — Always Active

| Rule | Severity | What It Enforces | |------|----------|-----------------| | no-attribution | CRITICAL | AI tools are tools — never add attribution to commits, PRs, docs, or code | | token-security | CRITICAL | Never hard-code tokens; use heredoc pattern for scoped lifetime; file permissions 600 | | versioning | CRITICAL | CalVer YYYY.M.PATCH with NO leading zeros; npm rejects leading zeros | | citation-policy | CRITICAL | Never fabricate citations, DOIs, or URLs; only cite verified sources; GRADE-appropriate hedging | | anti-laziness | HIGH | Never delete tests to pass, skip tests, remove features, or weaken assertions; escalate after 3 failures | | executable-feedback | HIGH | Execute tests before returning code; track execution history; max 3 retries with root cause analysis | | failure-mitigation | HIGH | Detect and recover from 6 LLM failure archetypes: hallucination, context loss, instruction drift, safety, technical, consistency | | research-before-decision | HIGH | Research codebase before acting: IDENTIFY → SEARCH → EXTRACT → REASON → ACT → VERIFY | | instruction-comprehension | HIGH | Fully parse all instructions before acting; track multi-part requests to completion | | subagent-scoping | HIGH | One focused task per subagent; <20% context budget; no delegation chains deeper than 2 levels |

SDLC Rules (34) — Active with Framework

Actionable feedback, mention wiring, HITL gates, agent fallback, provenance tracking, TAO loop, reproducibility validation, SDLC orchestration, agent-friendly code, agent generation guardrails, artifact discovery, HITL patterns, human gate display, thought protocol, reasoning sections, few-shot examples, best output selection, reproducibility, progressive disclosure, conversable agent interface, auto-reply chains, criticality panel sizing, qualified references.

Research Rules (2) — Active with Research

Research metadata (FAIR-compliant YAML frontmatter), index generation (auto-generated INDEX.md per FAIR F4).

Skills (128)

Natural language workflow triggers. Say "what's the project status?" and the project-awareness skill activates.

| Category | Skills | Examples | |----------|--------|---------| | Regression Testing | 12 | regression-check, regression-baseline, regression-bisect, regression-performance, regression-api-contract, regression-cicd-hooks, regression-learning | | Voice & Writing | 6 | voice-create, voice-analyze, voice-apply, voice-blend, ai-pattern-detection, brand-compliance | | Testing & Quality | 8 | auto-test-execution, test-coverage, test-sync, mutation-test, flaky-detect, flaky-fix, tdd-enforce, qa-protocol | | Forensics & Security | 8 | linux-forensics, memory-forensics, cloud-forensics, container-forensics, sigma-hunting, log-analysis, ioc-extraction, supply-chain-forensics | | SDLC & Workflow | 10 | sdlc-accelerate, sdlc-reports, gate-evaluation, approval-workflow, iteration-control, risk-cycle, parallel-dispatch, decision-support | | Documentation | 6 | doc-sync, doc-scraper, doc-splitter, llms-txt-support, pdf-extractor, source-unifier | | Artifacts & Traceability | 6 | artifact-orchestration, artifact-metadata, artifact-lookup, traceability-check, claims-validator, citation-guard | | Research | 2 | grade-on-ingest, auto-provenance | | Infrastructure | 5 | config-validator, template-engine, code-chunker, decompose-file, workspace-health | | Iteration | 4 | agent-loop, issue-driven-ralph, cross-task-learner, reflection-injection | | Other | 19 | performance-digest, competitive-intel, audience-synthesis, skill-builder, skill-enhancer, skill-packager, quality-checker, nl-router, tot-exploration, and more |

Framework Deep Dives

SDLC Complete — Full Software Development Lifecycle

The SDLC framework implements a phase-gated development lifecycle with 90 specialized agents, 34 enforcement rules, and 170+ artifact templates. Natural language commands drive phase transitions with automated quality gates.

 ┌──────────┐    ┌─────────────┐    ┌──────────────┐    ┌────────────┐    ┌────────────┐
 │ CONCEPT  │───▶│  INCEPTION  │───▶│ ELABORATION  │───▶│CONSTRUCTION│───▶│ TRANSITION │
 │          │    │             │    │              │    │            │    │            │
 │ Intake   │    │ Vision      │    │ Architecture │    │ Code       │    │ Deploy     │
 │ Wizard   │    │ Requirements│    │ Risk Retire  │    │ Test       │    │ Hypercare  │
 │ Solution │    │ Stakeholder │    │ Prototype    │    │ Review     │    │ Handoff    │
 │ Profile  │    │ Analysis    │    │ API Design   │    │ Iterate    │    │ Knowledge  │
 └──────────┘    └──────┬──────┘    └──────┬───────┘    └─────┬──────┘    └────────────┘
                        │                  │                   │
                     ┌──▼──┐            ┌──▼──┐            ┌──▼──┐
                     │ LOM │            │ ABM │            │ IOC │    ← Quality Gates
                     │Gate │            │Gate │            │Gate │
                     └─────┘            └─────┘            └─────┘

 LOM = Lifecycle Objectives Milestone    ABM = Architecture Baseline Milestone
 IOC = Initial Operational Capability

SDLC Flow Commands (24):

| Command | Phase | What It Does | |---------|-------|-------------| | /intake-wizard | Concept | Generate project intake form from natural language description | | /intake-start | Concept→Inception | Validate intake, kick off with agent assignments | | /intake-from-codebase | Concept | Scan existing codebase, generate intake from analysis | | /flow-concept-to-inception | Concept→Inception | Phase transition with intake validation and vision alignment | | /flow-inception-to-elaboration | Inception→Elaboration | Architecture baselining and risk retirement | | /flow-elaboration-to-construction | Elaboration→Construction | Iteration planning, team scaling, full-scale development | | /flow-construction-to-transition | Construction→Transition | IOC validation, production deployment, operational handover | | /flow-discovery-track | Any | Prepare validated requirements one iteration ahead of delivery | | /flow-delivery-track | Any | Test-driven development, quality gates, iteration assessment | | /flow-iteration-dual-track | Any | Synchronized Discovery + Delivery workflows | | /flow-deploy-to-production | Transition | Strategy selection, validation, automated rollback, regression gates | | /flow-incident-response | Operations | Triage, escalation, resolution, post-incident review (ITIL) | | /flow-security-review-cycle | Any | Continuous security validation, threat modeling, vulnerability management | | /flow-performance-optimization | Any | Baseline, bottleneck ID, optimization, load testing, SLO validation | | /flow-retrospective-cycle | Any | Structured feedback, improvement tracking, action items | | /flow-change-control | Any | Baseline management, impact assessment, CCB review, communication | | /flow-risk-management-cycle | Any | Continuous risk identification, assessment, tracking, retirement | | /flow-compliance-validation | Any | Requirements mapping, audit evidence, gap analysis, attestation | | /flow-knowledge-transfer | Transition | Assessment, documentation, shadowing, validation, handover | | /flow-team-onboarding | Any | Pre-boarding, training, buddy assignment, 30/60/90 day check-ins | | /flow-hypercare-monitoring | Transition | 24/7 support, SLO tracking, rapid issue response | | /flow-gate-check | Any | Multi-agent phase gate validation with comprehensive reporting | | /flow-handoff-checklist | Any | Handoff validation between phases and tracks | | /flow-guided-implementation | Construction | Bounded iteration with issue-to-code automation |

SDLC Accelerate — Idea to Construction-Ready in One Command:

# From a description
aiwg sdlc-accelerate "AI-powered code review tool with GitHub integration"

# From existing codebase
aiwg sdlc-accelerate --from-codebase .

# Resume interrupted pipeline
aiwg sdlc-accelerate --resume

Generates intake form, vision document, use cases, architecture baseline, risk register, test strategy, and deployment plan — all with human approval gates between phases.

Dual-Track Iteration Model:

        ┌─────────────────────────────────────────────────┐
        │                ITERATION N                       │
        │                                                  │
        │  Discovery Track          Delivery Track         │
        │  (Next iteration)         (Current iteration)    │
        │                                                  │
        │  ┌─────────────┐          ┌──────────────┐      │
        │  │ Requirements│          │ Implement    │      │
        │  │ Research    │          │ Test         │      │
        │  │ Design      │          │ Review       │      │
        │  │ Validate    │          │ Deploy       │      │
        │  └─────┬───────┘          └──────┬───────┘      │
        │        │                         │               │
        │        └────────────┬────────────┘               │
        │                     │                            │
        │              ┌──────▼──────┐                     │
        │              │ Iteration   │                     │
        │              │ Assessment  │                     │
        │              └─────────────┘                     │
        └─────────────────────────────────────────────────┘

Metrics & Quality Tracking:

| Metric Category | Metrics Tracked | |-----------------|-----------------| | DORA (4) | Deployment Frequency, Lead Time, Change Failure Rate, MTTR | | Velocity (3) | Story Points, Cycle Time, Throughput | | Flow (3) | WIP Limits, Flow Efficiency, Blocked Items | | Quality (13) | Test Coverage (4), Defect Metrics (4), Code Quality (3), Technical Debt (2) | | Operational (16) | SLO/SLI (5), Infrastructure (4), Incidents (4), Cost (3) |

Forensics Complete — Digital Forensics & Incident Response

Full DFIR investigation workflow following NIST SP 800-86, with MITRE ATT&CK mapping and Sigma rule hunting.

 ┌──────────┐    ┌──────────┐    ┌────────────┐    ┌──────────┐    ┌──────────┐
 │  SCOPE   │───▶│  TRIAGE  │───▶│  ACQUIRE   │───▶│ ANALYZE  │───▶│  REPORT  │
 │          │    │          │    │            │    │          │    │          │
 │ Profile  │    │ Volatile │    │ Evidence   │    │ Log      │    │ Executive│
 │ target   │    │ data     │    │ collection │    │ Timeline │    │ summary  │
 │ system   │    │ capture  │    │ Chain of   │    │ IOC      │    │ Findings │
 │          │    │ RFC 3227 │    │ custody    │    │ Sigma    │    │ Timeline │
 └──────────┘    └──────────┘    └────────────┘    └──────────┘    └──────────┘
                                       │
                                  SHA-256 hash
                                  verification

Investigation Commands:

/forensics-profile          # Build target system profile via SSH
/forensics-triage           # Quick triage following RFC 3227 volatility order
/forensics-acquire          # Evidence acquisition with chain of custody
/forensics-investigate      # Full multi-agent investigation workflow
/forensics-timeline         # Build correlated event timeline
/forensics-hunt             # Threat hunt using Sigma rules
/forensics-ioc              # Extract and enrich IOCs
/forensics-report           # Generate forensic investigation report
/forensics-status           # Show investigation dashboard

Bundled Sigma Rules (8):

| Rule | What It Detects | |------|----------------| | SSH Brute Force | Repeated failed SSH authentication attempts | | Unauthorized SUID | Unexpected SUID/SGID binaries | | LD_PRELOAD Rootkit | Library injection via LD_PRELOAD | | Cron Persistence | Unauthorized crontab modifications | | Kernel Module Load | Suspicious kernel module insertion | | PAM Backdoor | PAM configuration tampering | | SSH Key Injection | Unauthorized authorized_keys modifications | | Systemd Persistence | Suspicious systemd unit creation |

Supported Evidence Sources:

| Source | Agent | Analysis | |--------|-------|----------| | Auth logs | Log Analyst | Brute force, privilege escalation, lateral movement | | Syslog / journal | Log Analyst | System events, service anomalies | | Network connections | Network Analyst | C2 beaconing, data exfiltration, DNS tunneling | | Docker/containerd | Container Analyst | Container escapes, image tampering, eBPF monitoring | | Memory dumps | Memory Analyst | Process injection, rootkits, credential extraction | | AWS/Azure/GCP | Cloud Analyst | API anomalies, IAM abuse, network flow analysis | | File system | Persistence Hunter | Cron, systemd, SSH keys, PAM, kernel modules |

Media/Marketing Kit — Campaign Lifecycle

 ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐
 │ STRATEGY │───▶│ CREATION │───▶│  REVIEW  │───▶│ PUBLISH  │───▶│ ANALYZE  │
 │          │    │          │    │          │    │          │    │          │
 │ Research │    │ Copy     │    │ Brand    │    │ Schedule │    │ KPIs     │
 │ Audience │    │ Design   │    │ Legal    │    │ Channels │    │ Reports  │
 │ Strategy │    │ Content  │    │ Quality  │    │ Launch   │    │ ROI      │
 └──────────┘    └──────────┘    └──────────┘    └──────────┘    └──────────┘

37 agents across strategy, creation, management, analytics, and communications. 87+ templates covering campaign intake, brand guidelines, content briefs, social playbooks, email sequences, PR kits, and analytics dashboards.

Media Curator — Intelligent Archive Management

# Full curation pipeline
/curate "Pink Floyd"

# Step by step
/analyze-artist "Pink Floyd"           # Identify eras, catalog structure
/find-sources "Pink Floyd" "DSOTM"     # Discover across YouTube, Archive.org, Bandcamp
/acquire                               # Download with format selection
/tag-collection                        # Apply metadata, embed artwork, rename
/check-completeness                    # Gap analysis against canonical discography
/assemble "Pink Floyd live 1973"       # Build thematic compilations
/export --format plex                  # Export to Plex, Jellyfin, MPD, or archival
/verify-archive                        # SHA-256 integrity verification

Quality tiers: Tier 1 (Official/Lossless) → Tier 2 (High Quality) → Tier 3 (Acceptable) → Tier 4 (Avoid). Standards: ID3v2.4, Vorbis Comments, MusicBrainz, PREMIS 3.0, W3C PROV-O.

Research Complete — Academic Research Pipeline

 ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐
 │ DISCOVER │───▶│ ACQUIRE  │───▶│ DOCUMENT │───▶│ ARCHIVE  │
 │          │    │          │    │          │    │          │
 │ Search   │    │ Download │    │ RAG      │    │ OAIS     │
 │ databases│    │ PDF      │    │ summaries│    │ lifecycle│
 │ Rank     │    │ Metadata │    │ Citations│    │ FAIR     │
 │ results  │    │ extract  │    │ GRADE    │    │ W3C PROV │
 └──────────┘    └──────────┘    └──────────┘    └──────────┘

8-stage pipeline: Discovery → Acquisition → Documentation → Citation → Quality Assessment → Synthesis → Gap Analysis → Archival. Persistent REF-XXX identifiers. GRADE scoring (HIGH/MODERATE/LOW/VERY LOW). Unpaywall integration for open access papers.

Voice Framework — Content Voice Consistency

4 built-in voice profiles with create, analyze, blend, and apply skills:

| Profile | When to Use | Characteristics | |---------|-------------|----------------| | technical-authority | API docs, architecture guides | Precise terminology, confident assertions, specific metrics | | friendly-explainer | Tutorials, onboarding | Accessible language, analogies, encouragement | | executive-brief | Status reports, proposals | Bottom-line-first, quantified impact, action-oriented | | casual-conversational | Blog posts, social media | Natural rhythm, opinions, varied structure |

# Apply a voice to content
"Apply technical-authority voice to docs/architecture.md"

# Create a custom voice from existing content
/voice-create --source "blog-posts/*.md" --name "company-voice"

# Analyze writing patterns in a voice sample
/voice-analyze docs/existing-content.md

# Blend two voices
/voice-blend technical-authority casual-conversational --ratio 70:30

# Detect AI patterns and suggest authentic alternatives
/ai-pattern-detection docs/generated-content.md

MCP Server — Model Context Protocol Integration

AIWG includes a built-in MCP server for tool-based AI workflow integration:

# Start MCP server
aiwg mcp serve

# Install into Claude Desktop
aiwg mcp install claude

# Show capabilities
aiwg mcp info

The MCP server exposes AIWG's artifact management, workflow execution, and project health capabilities as tools that any MCP-compatible AI platform can invoke programmatically.

Agent Evaluation Framework

Test agent quality with archetype resistance testing based on Roig (2025) failure patterns:

# Evaluate a specific agent
/eval-agent security-auditor

# Test archetype resistance
/eval-agent test-engineer --category archetype

# Run performance benchmarks
/eval-agent code-reviewer --category performance

Test Categories:

| Category | Tests | What It Validates | |----------|-------|-------------------| | Archetype | 4 | Grounding (hallucination resistance), Substitution (scope adherence), Distractor (context noise), Recovery (failure handling) | | Performance | 3 | Latency, token efficiency, parallel execution capability | | Quality | 3 | Output format compliance, correct tool usage, scope adherence |

Target score: >=85% per agent. Results include passed/failed breakdown with evidence.

Bidirectional Traceability — @-Mention System

Link requirements to architecture to code to tests with semantic @-mentions:

<!-- In a use case document -->
This use case @implements(UC-001) the authentication flow
described in @architecture(SAD-section-3.2).

<!-- In a test file -->
// @tests(UC-001) @depends(auth-service)
describe('authentication flow', () => { ... });

Mention Commands:

/mention-wire         # Analyze codebase and inject @-mentions for traceability
/mention-validate     # Validate all @-mentions resolve to existing files
/mention-report       # Generate traceability report
/mention-lint         # Lint @-mentions for style consistency
/mention-conventions  # Display naming conventions and placement rules

Relationship qualifiers: @implements, @tests, @depends, @derives-from, @blocked-by, @supersedes. Enables queries like "what implements UC-001?" and "what tests cover the auth module?"

Configuration & Customization

Workspace Structure

your-project/
├── .aiwg/                    # SDLC artifacts (persistent project memory)
│   ├── intake/               # Project intake forms
│   ├── requirements/         # Use cases, user stories, NFRs
│   ├── architecture/         # SAD, ADRs, system diagrams
│   ├── planning/             # Phase plans, iteration plans
│   ├── risks/                # Risk register, mitigations
│   ├── testing/              # Test strategy, plans, results
│   ├── security/             # Threat models, security gates
│   ├── deployment/           # Deployment plans, runbooks
│   ├── reports/              # Generated status reports
│   ├── ralph/                # In-session agent loop state
│   ├── ralph-external/       # External Ralph crash-resilient state
│   ├── research/             # Research corpus and findings
│   ├── forensics/            # Investigation artifacts
│   ├── working/              # Temporary files (safe to delete)
│   └── frameworks/           # Installed framework registry
│       └── registry.json
├── .claude/                  # Claude Code deployment
│   ├── agents/               # 162 agent definitions
│   ├── commands/             # Slash commands
│   ├── skills/               # 86 skill definitions
│   └── rules/                # RULES-INDEX.md + on-demand full rules
├── .github/                  # GitHub Copilot deployment
├── .cursor/                  # Cursor deployment
├── .warp/                    # Warp Terminal deployment
└── CLAUDE.md                 # Project instructions (auto-generated)

Creating Custom Extensions

# Add a custom agent
aiwg add-agent my-domain-expert

# Add a custom command
aiwg add-command my-workflow

# Add a custom skill
aiwg add-skill my-capability

# Scaffold a complete addon
aiwg scaffold-addon my-addon

# Scaffold a complete framework
aiwg scaffold-framework my-framework

# Validate all extension metadata
aiwg validate-metadata

Capability Discovery — aiwg discover + aiwg show

The headline operator surface for finding and reading AIWG capabilities. Most AIWG skills (~385 of 400) are not loaded into your platform's flat skill listing — they stay at $AIWG_ROOT and are reached on demand through aiwg discover (find) and aiwg show (fetch). The kernel set on disk is small on purpose: 9 framework quickrefs + 6 self-maintenance ops = 15 skills, well under every supported platform's skill-listing budget.

# Find a skill by capability
aiwg discover "deploy production"           # → flow-deploy-to-production
aiwg discover "create intake"               # → intake-* family
aiwg discover "audit security" --type skill --limit 5
aiwg discover "<phrase>" --json             # stable schema for sub-agents

# Fetch the full body of a specific artifact (companion to discover)
aiwg show skill flow-deploy-to-production
aiwg show agent aiwg-steward
aiwg show command discover
aiwg show rule no-attribution

The kernel quickrefs ship curated, validated discovery phrases per capability domain — phrases tested against the live scorer to surface the right top-3 candidates. The 6 self-maintenance ops (steward, aiwg-doctor, aiwg-refresh, aiwg-status, aiwg-help, use) stay loaded so the agent retains repair surfaces even when discovery itself is broken. See docs/discovery-and-kernel-skills.md for the full best-practices guide, ASCII flow diagrams, and verification steps.

Artifact Index — aiwg index

# Build searchable artifact index
aiwg index build
aiwg index build --force --verbose

# Search artifacts by keyword
aiwg index query "authentication" --json

# Show dependency graph for an artifact
aiwg index deps .aiwg/requirements/UC-001.md --json

# Index statistics
aiwg index stats --json

The index supports multiple graphs: project graph (.aiwg/ artifacts), codebase graph (src/ / test/ / tools/), and framework graph (agentic/code/ + docs/).

Doc Sync — Bidirectional Documentation

# Audit doc drift (dry run)
aiwg doc-sync code-to-docs --dry-run

# Sync docs to match code
aiwg doc-sync code-to-docs

# Bidirectional reconciliation
aiwg doc-sync full --interactive

Reproducibility Validation

# Show/set execution mode (strict = temperature 0, fixed seed)
aiwg execution-mode

# Create execution snapshot
aiwg snapshot

# Create workflow checkpoint
aiwg checkpoint

# Validate workflow reproducibility
aiwg reproducibility-validate

Thresholds: compliance audit (100%), security scan (100%), test generation (95%).

Issue-Driven Development

AIWG integrates with issue trackers for 2-way human-AI collaboration:

# Create issues from any backend (Gitea, GitHub, Jira, Linear, local files)
/issue-create "Implement OAuth2 flow" --labels "feature,auth"

# List and filter issues
/issue-list --state open --labels "priority:high"

# Drive an issue with agent loop — posts status to issue thread
/issue-driven-ralph 42

# Auto-sync issues from commits and artifacts
/issue-sync

# Close with comprehensive summary and verification
/issue-close 42

The /address-issues command orchestrates issue-thread-driven agent loops with automatic progress posting and human feedback incorporation at each cycle.

Daemon Mode & Messaging Integration

Daemon Mode

# Background file watching, cron scheduling, IPC
aiwg daemon start

See Daemon Guide for background agent orchestration.

Messaging Integration

Bidirectional Slack, Discord, and Telegram bots for remote agent control:

# Connect to messaging platforms
aiwg messaging connect slack
aiwg messaging connect discord
aiwg messaging connect telegram

See Messaging Guide for setup and configuration.

See It In Action

# Generate project intake from natural language
/intake-wizard "Build customer portal with real-time chat"

# Accelerate from idea to construction-ready
/sdlc-accelerate "AI-powered code review tool"

# Phase transition with automated gate check
/flow-inception-to-elaboration

# Iterative task execution — "iteration beats perfection"
/ralph "Fix all failing tests" --completion "npm test passes"

# Long-running tasks with crash recovery (6-8 hours)
/ralph-external "Migrate to TypeScript" --completion "npx tsc --noEmit exits 0"

# Process massive codebases with recursive context decomposition
/rlm-query "src/**/*.ts" "Extract all exported interfaces" --model haiku
/rlm-batch "src/components/*.tsx" "Add TypeScript types" --max-parallel 4

# Digital forensics investigation
/forensics-investigate
/forensics-triage
/forensics-timeline

# Scan codebase for agent-readiness
/codebase-health --format text

# Decompose large files into agent-friendly modules
/decompose-file src/large-file.ts --execute

# Deploy to production with rollback gates
/flow-deploy-to-production

# Security assessment
/security-audit

# Voice transformation
"Apply technical-authority voice to docs/architecture.md"
"Create a voice profile based on our existing blog posts"

Platform Support

All 8 platforms receive agents, commands, skills, and rules. Deployment adapts to each platform's conventions automatically.

| Platform | Status | Agents | Commands | Skills | Rules | Deploy Command | |----------|--------|--------|----------|--------|-------|---------------| | Claude Code | Tested | .claude/agents/ | .claude/commands/ | .claude/skills/ | .claude/rules/ | aiwg use sdlc | | GitHub Copilot | Tested | .github/agents/ | .github/agents/ | .github/skills/ | .github/copilot-rules/ | --provider copilot | | Warp Terminal | Tested | .warp/agents/ + WARP.md | .warp/commands/ | .warp/skills/ | .warp/rules/ | --provider warp | | Factory AI | Tested | .factory/droids/ | .factory/commands/ | .factory/skills/ | .factory/rules/ | --provider factory | | Cursor | Tested | .cursor/agents/ | .cursor/commands/ | .cursor/skills/ | .cursor/rules/ | --provider cursor | | OpenCode | Tested | .opencode/agent/ | .opencode/commands/ | .opencode/skill/ | .opencode/rule/ | --provider opencode | | OpenAI/Codex | Tested | `.cod