AIF skillsworkflow map

Skill	What it is and why	When to use	Result and next step
`$aif`	Set up agent context for a project. Analyzes tech stack, installs relevant skills from skills.sh, generates custom skills, and configures MCP servers.	Use when starting a new project, setting up AI context, or asking "set up project", "configure AI", "what skills do I need".	Agent-ready project context: path settings, artifact language, project map, skills, and MCP configuration.
`$aif-architecture`	Generate architecture guidelines for the project. Analyzes tech stack from DESCRIPTION.md, recommends an architecture pattern, and creates .ai-factory/ARCHITECTURE.md.	Use when setting up project architecture, asking "which architecture", or after `$aif` setup.	An ARCHITECTURE.md with practical constraints for future plans, reviews, and implementation.
`$aif-archive`	Archive completed plans and roadmap milestones. Moves finished plans to the archive directory and optionally trims closed milestones from ROADMAP.md.	Use when user says "archive plans", "clean up plans", "archive completed", or "trim roadmap".	Closed plans and roadmap snapshots are moved out of the active workspace while history remains available.
`$aif-best-practices`	Code quality guidelines and best practices for writing clean, maintainable code. Covers naming, structure, error handling, testing, and code review standards.	Use when writing code, reviewing, refactoring, or asking "how should I name this", "best practice for", "clean code".	Clear quality defaults that can be turned into project rules and used during planning, reviews, and implementation.
`$aif-build-automation`	Analyze project and generate or enhance build automation file (`Makefile`, `Taskfile.yml`, `Justfile`, `Magefile.go`). If a build file already exists, improves it by adding missing targets and best practices.	Use when user says "generate makefile", "create taskfile", "add justfile", "setup mage", or "build automation".	One repeatable command interface for people, CI, and agents.
`$aif-ci`	Generate or enhance CI/CD pipeline (GitHub Actions / GitLab CI) with linting, static analysis, tests, and security checks; supports `--enhance`.	Use when user says "ci", "setup ci", "github actions", "gitlab ci", "pipeline".	CI configuration that repeats key checks and reduces the chance of breaking main.
`$aif-commit`	Create conventional commit messages by analyzing staged changes. Generates semantic commit messages following the Conventional Commits specification.	Use when user says "commit", "save changes", or "create commit".	A clear Conventional Commit message that matches the staged changes.
`$aif-distillation`	Distill books, documents, folders, or URLs into compact, practical Agent Skills.	Use when source material should become either one reusable skill package or a split set of focused skills, each with a concise SKILL.md plus detailed references and examples.	A reusable skill package or split set of focused skills instead of repeatedly rereading the full source material.
`$aif-dockerize`	Analyze project and generate Docker configuration: Dockerfile (multi-stage dev/prod), compose.yml, compose.override.yml (dev), compose.production.yml (hardened), and .dockerignore. Includes production security audit.	Use when user says "dockerize", "add docker", "docker compose", "containerize", or "setup docker".	Repeatable container startup and a more predictable dev/CI environment.
`$aif-docs`	Generate and maintain project documentation. Creates a lean README as a landing page with detailed docs pages split by topic in the configured docs directory.	Use when user says "create docs", "write documentation", "update docs", "generate readme", or "document project".	Current documentation tied to real files, commands, architecture, integrations, QA, or operations.
`$aif-evolve`	Self-improve AI Factory skills based on project context, accumulated patches, and codebase patterns. Analyzes what went wrong, what works, and enhances skills to prevent future issues.	Use when you want to make AI smarter for your project.	Project-specific skill context improves so agents repeat fewer old mistakes.
`$aif-explore`	Enter explore mode: a thinking partner for exploring ideas, investigating problems, and clarifying requirements.	Use when the user wants to think through something before or during a change.	Clarified scope, options, risks, and open questions; if saved to `.ai-factory/RESEARCH.md`, it can feed `$aif-plan` from a fresh chat.
`$aif-fix`	Fix a specific bug or problem in the codebase. Supports two modes - immediate fix or plan-first. Without arguments executes existing FIX_PLAN.md. Always suggests test coverage and adds logging.	Use when user says "fix bug", "debug this", "something is broken", or pastes an error message.	A targeted fix with checks and notes for future learning when useful.
`$aif-grounded`	Reliability gate for answers. Forces evidence-based reasoning, explicit uncertainty, and “insufficient information” instead of guesses.	Use when user says “be 100% sure”, “no hallucinations”, “only if verified”, “grounded answer”, or when stakes are high.	A verified answer with evidence, explicit uncertainty, and honest “insufficient information” when proof is missing.
`$aif-implement`	Execute implementation tasks from the current plan. Works through tasks sequentially, marks completion, and preserves progress for continuation across sessions; supports `--list`, `@plan-file`, `status`, and inline `--without-plan`.	Use when user says "implement", "start coding", "execute plan", or "continue implementation".	Plan tasks executed with progress preserved for continuation, then ready for `$aif-verify`.
`$aif-improve`	Refine an existing implementation plan with a second iteration. Re-analyzes the codebase for gaps, missing tasks, and wrong dependencies. Use after `$aif-plan` or to improve an `$aif-fix` plan. Optional +check flag validates refinements via a fresh-context subagent.	Use after `$aif-plan`, to improve an `$aif-fix` plan, or when a plan needs an auto-review / targeted improvement prompt before implementation.	A more realistic implementation-ready plan with gaps, dependencies, and risks addressed.
`$aif-loop`	Run a strict multi-iteration Reflex Loop with phases `PLAN`, `PRODUCE/PREPARE`, `EVALUATE`, `CRITIQUE`, `REFINE` to improve an artifact until quality gates pass or iteration limits are reached.	Use when user asks for iterative refinement, quality-gated generation, or "generate -> critique -> refine" loops.	An improved artifact with iteration history, gate results, and rationale; loop state is on disk, so continuation from a fresh chat uses `$aif-loop resume`.
`$aif-plan`	Plan implementation for a feature or task. Two modes — fast (single quick plan) or full (richer plan with optional git branch/worktree flow).	Use when user says "plan", "new feature", "start feature", "create tasks".	A markdown implementation plan that can be refined with `$aif-improve` and executed with `$aif-implement`.
`$aif-qa`	QA workflow for testing a feature or task implementation. Analyzes changes, produces test plans, and describes concrete test scenarios.	Use when user says "test this", "write test plan", "what should I test", or "QA this branch".	QA artifacts: change summary, test plan, concrete scenarios, and edge cases.
`$aif-reference`	Create knowledge references from URLs, documents, or files for use by AI agents. Fetch, process, and store structured references in the configured references directory (default: .ai-factory/references/).	Use when a URL, document, file, or handoff material must become a reusable project reference.	A structured reference in .ai-factory/references or the configured references directory.
`$aif-review`	Perform code review on staged changes or a pull request. Checks for bugs, security issues, performance problems, and best practices.	Use when user says "review code", "check my code", "review PR", or "is this code okay". Optional +check flag validates findings via a fresh-context subagent.	Prioritized findings with concrete locations, ready for fix or follow-up.
`$aif-roadmap`	Create or update a project roadmap with major milestones. Generates the configured roadmap artifact (default .ai-factory/ROADMAP.md) — a strategic checklist of high-level goals.	Use when user says "roadmap", "project plan", "milestones", or "what to build next".	A high-level ROADMAP.md that connects milestones, order of work, progress, and next steps.
`$aif-rules`	Add project-specific rules and conventions to the configured RULES.md artifact. Each invocation appends new rules. These rules are automatically loaded by `$aif-implement` before execution.	Use when user says "add rule", "remember this", "convention", or "always do X".	Updated RULES.md or rules/*.md loaded by later skills before planning, implementation, and checks.
`$aif-rules-check`	Run a standalone read-only rules compliance gate against changed files or a git ref.	Use when you need a dedicated project-rules check without a full review or verify pass.	A rules-gate result: violations to fix or confirmation that changes comply.
`$aif-security-checklist`	Security audit checklist based on OWASP Top 10 and best practices. Covers authentication, injection, XSS, CSRF, secrets management, and more.	Use when reviewing security, before deploy, asking "is this secure", "security check", "vulnerability".	A security checklist with risks and required actions before work proceeds.
`$aif-skill-generator`	Generate professional Agent Skills for AI agents. Creates complete skill packages with SKILL.md, references, scripts, and templates.	Use when creating new skills, generating custom slash commands, or building reusable AI capabilities; can start from a skill name, `search <query>`, or URL(s), and validates against the Agent Skills specification.	A complete skill package with SKILL.md, references, scripts, templates, and validation.
`$aif-verify`	Verify completed implementation against the plan. Checks that all tasks were fully implemented, nothing was forgotten, code compiles, tests pass, and quality standards are met; supports `--strict`.	Use after `$aif-implement` completes, or when user says "verify", "check work", or "did we miss anything".	A pass/fail result against the plan, build/test/lint, and quality standards.