Feature Workflow logo

Feature Workflow

Organization
cubeplexai
feature-workflow

Use at the START of a large, unclear, or multi-subsystem cubeplex feature — before writing code — to isolate work, resolve the design, and write a spec/plan under docs/dev. Clearly specified, small, single-concern tasks skip spec, plan, clarification, and review and execute directly. Covers worktree provisioning (./scripts/new-worktree), reading .worktree.env for allocated ports/DBs, brainstorming requirements, and one-concern-per-PR planning. Triggers on phrases like "做个新功能", "开个 worktree", "先写 spec/plan", "怎么开始这个 feature", "plan before code", "brainstorm 一下需求".

Overview

Publishercubeplexai
Repositorycubeplex
Skill namefeature-workflow
Stars
533
Forks
46
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cubeplexai on GitHub. Read the source before you install it.

Installation

Install the Feature Workflow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cubeplexai/cubeplex/tree/main/.agents/skills/feature-workflow
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cubeplexai/cubeplex.git /tmp/cubeplex
mkdir -p .claude/skills
cp -r /tmp/cubeplex/.agents/skills/feature-workflow .claude/skills/feature-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feature Workflow in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Feature Workflow on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Feature Workflow is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Feature Workflow (cubeplex)

The front half of cubeplex's workflow for changes that need design work: worktree → clarify → spec/plan → execute. This replaces the generic brainstorm/plan/execute skills with the repo's actual commands and paths. Read docs/worktrees.md and AGENTS.md → "Workflow Discipline" for the authoritative rules; this skill is the operating loop.

Choose the workflow

Use the full workflow when the task is a large feature, spans independent subsystems, has unresolved requirements, or requires a meaningful design decision. Use it when the user explicitly requests a spec, plan, brainstorming, or review.

Directly execute a clearly specified, small, single-concern task. Do not create a spec or plan, ask for requirement clarification, or wait for design review just because the change is user-facing or touches documentation, a route, or UI. Inspect the affected files, make the surgical change, and verify it.

1. Worktree before spec/code for the full workflow

Always from the main repo root (not inside another worktree):

bash
./scripts/new-worktree feat/YYYY-MM-DD-<name>

Branches from origin/main, allocates ports/DBs, runs migrations, pushes the branch (details: docs/worktrees.md).

First thing on entry — read the allocated values; never assume ports 8000/3000. Use CUBEPLEX_API__PORT etc. from it for every dev-server / curl / Playwright command:

bash
cat .worktree.env

2. Clarify intent, then write the spec

Explore first — the relevant files, docs/, and recent commits — so questions are informed, not generic. Then resolve ambiguity one question at a time (purpose, constraints, success criteria); don't batch five questions or invent requirements to fill gaps. Genuine forks worth asking:

  • What business invariant or user-visible contract does this protect?
  • Which scope — workspace (/api/v1/ws/{ws}/...), org-admin (/api/v1/admin/...), or both? They are separate handlers (AGENTS.md "Scope-isolated APIs"). Decide now; it shapes the whole design.
  • What's explicitly out of scope?

Scope check. If the request spans multiple independent subsystems, stop and decompose — one spec → plan → PR cycle per subsystem. A spec that tries to cover everything produces a plan nobody can review.

Propose 2-3 approaches with trade-offs and your recommendation before committing to one. Then write the spec to docs/dev/specs/YYYY-MM-DD-<slug>-design.md:

  • Goal — one sentence: what this builds and why.
  • Context — current behavior and why it's changing.
  • Approaches considered — the options + why you chose this one.
  • Design — what literally happens, in sections scaled to complexity: data model / migrations, API surface (routes, request/response shapes), scope & RBAC, UI flow, edge cases.
  • Out of scope — what this deliberately does not do.
  • Success criteria — the observable invariants that prove it works.

Plain language (AGENTS.md rule 12): say what literally happens + why, in concrete words — no invented jargon.

Spec self-review (a checklist you run yourself — not a review gate): skim for placeholders (TBD, "handle edge cases"), internal contradictions, ambiguity, and scope creep. Fix inline. Then have the user glance at the spec before you turn it into a plan.

3. Turn the spec into a plan

Write the plan to docs/dev/plans/YYYY-MM-DD-<slug>.md before code. Investigations / decisions go to docs/dev/notes/. docs/dev files are frozen snapshots — rebase the content, don't rewrite history.

The plan's job is to be reviewable for soundness — not to pre-write the code. You can't judge whether unwritten, non-TDD'd code is "right", so don't fill the plan with literal implementation; it just makes the plan un-reviewable and it'll change anyway. Capture the decisions a reviewer actually needs to sanity-check. The red→green→commit detail belongs to execution (/cubeplex-tdd), not here.

Header: Goal (one sentence) · Architecture (2-3 sentences: how the pieces fit, the key data flow, the load-bearing decision) · Tech stack.

For each unit of work, write down:

  • Files — which to create/modify (path) and each one's responsibility / what changes in it. Files that change together live together; follow existing patterns, don't unilaterally restructure.
  • Interfaces — the contracts that lock the decomposition: API routes with request/response shapes, function/type signatures, DB schema / migration changes, event shapes. Concrete enough that the next unit can call into it.
  • Core logic — the non-obvious control flow, algorithm, or invariant, in prose or short pseudocode. Say what happens and why, not the final Python/TS line-for-line.
  • Tests — which invariant each unit must protect and where it lands (unit / integration / e2e per /cubeplex-tdd) — the intent, not the test body.

Right altitude: a teammate can read it and tell you "that interface is wrong" or "this misses a case" without the code existing yet. If you're pasting what will become the actual implementation, you've gone too fine.

Concrete, not vague — the fix for "too detailed" is not hand-waving. "Handle edge cases" / "add validation" / "TBD" are non-answers: name the actual edge cases and the actual rule, then stop there.

Plan self-review (a self-checklist, not a per-step review gate):

  1. Spec coverage — point each spec requirement to a unit of work; add for gaps.
  2. Interface consistency — a type / route / signature named one way in one unit and differently in another is a bug. Reconcile.
  3. Vagueness scan — any "handle edge cases"-class hand-wave → make it concrete.

Fix inline and move on.

4. Push the spec/plan PR first — before any code

Spec + plan are the first reviewable artifact. Commit them and push a PR before writing implementation code — get the design reviewed while it's still cheap to change. Implementation lands as follow-up commits / PR(s) (one concern per PR, rule 6).

bash
git add docs/dev/specs/<slug>-design.md docs/dev/plans/<slug>.md
git commit -m "spec+plan: <feature>"
git push --no-verify        # pre-push checks are too slow to run every push
gh pr create --title "<feature> — spec & plan" --body "..."

--no-verify rule: OK only when the push has no code and no test changes (spec/plan/notes, config) — the hooks exist to protect code/tests, so there's nothing to catch. Any push with code or tests keeps the hooks: the pre-push hook is the make check-ci gate, so don't run check-ci by hand. (Skipping here also matches new-worktree, which pushes with --no-verify.)

5. Execute

For the full workflow, work the plan's units of work top to bottom. For a direct task, make the scoped change immediately. In both cases, stay on the feature branch, never auto-switch to main or start a merge mid-execution (rule 8), and verify before claiming completion. No review gate between steps or units — the per-step verifications plus the pre-push hook are the checkpoints.

  • Simplest thing that works. Write the minimum code for the requirement — no speculative abstractions, config, or flexibility nobody asked for, no handling for impossible cases. If 200 lines could be 50, rewrite. The test: "would a senior call this overcomplicated?" → if yes, simplify. (Keep changes surgical too — AGENTS.md "Authoring Conventions".)
  • Test-first loop → /cubeplex-tdd. Stuck on a bug → /debug-cubeplex.
  • Per task, run only the changed-module tests (rule 11); the pre-push hook runs check-ci on the code push.
  • Before the PR: verify the feature with a real command, paste the evidence, then push and open the PR.
  • After the PR is open, ask the user whether to start /pr-codex-review-loop — don't kick it off automatically.

Stop and ask only on a real blocker (missing dependency, unclear instruction, a verification that keeps failing) — don't guess through it.

Guardrails

All AGENTS.md hard rules apply. The one most missed mid-feature: docs ship with the code in the same PR (rule 13).

Frequently asked questions

What does the Feature Workflow AI skill do?

Use at the START of a large, unclear, or multi-subsystem cubeplex feature — before writing code — to isolate work, resolve the design, and write a spec/plan under docs/dev. Clearly specified, small, single-concern tasks skip spec, plan, clarification, and review and execute directly. Covers worktree provisioning (./scripts/new-worktree), reading .worktree.env for allocated ports/DBs, brainstorming requirements, and one-concern-per-PR planning. Triggers on phrases like "做个新功能", "开个 worktree", "先写 spec/plan", "怎么开始这个 feature", "plan before code", "brainstorm 一下需求".

Why use Feature Workflow on TypingMind?

Because you install it once and use it with any model. Feature Workflow is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Feature Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cubeplexai/cubeplex/tree/main/.agents/skills/feature-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Feature Workflow?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Feature Workflow?

As many as you like. As long as a model supports skills, you can use Feature Workflow with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Feature Workflow AI skill free?

It is published on GitHub by cubeplexai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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