Uncontrolled diffs
A request can turn into more change than the team intended to review.
AI Workflow is an npm CLI that installs agents, commands, skills, policies and templates into your repository. It guides coding agents from request to implementation, validation and evidence.
For developers and engineering teams, AIWK adds scoped delivery, branch safety, observed validation, and release conformance around the coding runtime you already use.
Fast output is not the same as a controlled delivery. Without an explicit path, teams have to reconstruct scope, branch state, validation, and evidence after the fact.
A request can turn into more change than the team intended to review.
Without a branch gate, write-mode work can begin on a protected branch.
A completion message can arrive without observed results from the required checks.
Decisions, ownership, and task constraints get reconstructed across sessions.
A failed check can trigger changes that quietly expand the approved scope.
AI Workflow Kit adds a workflow layer around coding agents. The value is in the boundaries it makes visible and the evidence it expects—not in the size of its internal inventory.
Request classification and proportional policies make the intended work easier to define and review.
The Branch Gate blocks write-mode work on protected branches and establishes a safe path when recovery is allowed.
A workflow owner routes delivery work while capability specialists contribute bounded analysis.
A successful delivery needs real command results; an unobserved check cannot become a success claim.
Concrete validation findings can be repaired within a configured attempt limit instead of opening an uncontrolled loop.
Receipt-aware updates reconcile untouched AIWK assets while preserving consumer-owned or modified files.
AIWK does not replace your coding agent. It installs managed workflow assets into the repository and keeps runtime-specific integration boundaries explicit.
AIWK is not a single prompt. It combines explicit workflow ownership, bounded specialist analysis, reusable domain skills, and repeatable commands.
Verified against the installed AIWK package. These counts describe the capability surface, not product quality or behavioral parity.
Primary agents own workflow stages and handoffs. They coordinate delivery responsibility rather than acting as interchangeable personalities.
Specialists provide focused analysis when needed and return their findings to the active workflow owner. They do not receive workspace ownership.
Skills load focused technical guidance only when relevant to the current task, keeping domain context proportional to the work.
frontend-developmentqa-workflowrelease-workflowdocumentationperformancearchitecturebackend-developmentcyber-securitydatabasedeploymentdesign-principlesdevopsdocumentationfrontend-design-systemfrontend-developmentfull-stack-developmentlocalizationoptimize-tokensperformancepr-workflowproduct-discoveryproduct-planningproject-memoryprompt-engineerqa-workflowrefactoringrelease-workflowspec-driven-developmenttechnical-leadershipui-ux-designCommands provide repeatable entry points for planning, implementation, validation, evidence handoff, and workflow execution.
/plan/implement/validate/release/run/atlas/audit/deploy/discover/implement/optimize-tokens/plan/release/run/spec-create/spec-implement/spec-review/update-memory/validateAI Workflow Kit 2.13 adds executable release conformance and a strict release attestation. The qualified source, exact consumer tarball, observed validation, runtime result, and release identity are linked before publication.
The attestation binds the source commit, version, tag, package identity and SHA-256, observed commands, conformance results, and real OpenCode qualification. Missing or skipped required evidence blocks publication.
This is the release contract, not a live release record. No fabricated hash, tag, or publication result is shown on this page.
AIWK is OpenCode-first and provides documented integration paths for Antigravity, Codex, and Claude Code. The level describes the evidence and boundary—not behavioral parity.
This terminal is illustrative, not a live run. It shows the shape of a delivery that records the request, safe branch, implementation, observed validation, and evidence.
State the change and the constraints that shape it.
Pass the Branch Gate before write-mode work begins.
Deliver the scoped change under explicit ownership.
Use observed command results, not an assumed pass.
Return the outcome, artifacts, and limitations for review.
$ npx aw execute "Add contact form validation"
Example branch: feat/contact-form-validation
Request ✓ classified
Branch Gate ✓ safe branch established
Implementation ✓ scoped change
Validation ✓ observed commands
Evidence ✓ delivery summary
Handoff ✓ reviewable result
Example only — results depend on the repository, runtime, and policy.AI Workflow Kit 2.13.0 requires Node.js 20.11 or newer and npm 10 or newer. Add the package, project the integration for the runtime you use, run the doctor check, and then start through that runtime's documented path.
npm install -D @williambeto/ai-workflownpx aw init --yesnpx aw doctornpx aw execute "your task"
Use opencode.jsonc and the generated OpenCode assets. ai-workflow execute is the CLI-enforced path for branch, delivery, validation, and evidence gates.
npx aw init --yes --codexnpx aw doctornpx aw execute --runtime=codex "your task"
Trust the repository in Codex first. The App Server path is opt-in and adapter-controlled; doctor fails closed when the trust gate is absent.
npx aw init --yes --claudenpx aw doctorclaude --agent <name>
Use the generated CLAUDE.md, native agents, and skills. Authenticated execution is opt-in and full workspace-write evidence is not release-gated.
npx aw init --yes --antigravitynpx aw doctorSelect Atlas in the Agent selector
Use the generated ANTIGRAVITY.md and .agents/ projection. In the IDE, select Atlas or begin a prompt with /atlas.
AIWK makes workflow controls and evidence more explicit. It does not remove the need for engineering judgment.
Tests, review and production safeguards are still required.
AIWK complements existing engineering controls; it does not certify the software itself.
Runtime and model capabilities still affect direct-agent behavior and available enforcement.
Direct model interaction may not have the same enforcement as the AIWK control plane, especially where integrations are limited.