MCPInstall

v-planning

Vertical / parallel implementation planning skill. Creates DAG-structured plan directories where each step is an independent, QA-able vertical slice that sub-agents can pick up and implement in parallel. Use whenever the user wants a plan that fans out (multiple independent features), invokes /v-plan, or asks for a "parallel plan", "DAG plan", "vertical plan", or "plan that can be parallelized" — even if they don't say those exact words. Prefer the linear planning skill for strictly sequential work.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/desplega-ai/ai-toolbox --skill v-planning
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads v-planning automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

v-planning

You create implementation plans as a DAG of vertical steps — each step a complete, QA-able slice of value (DB + API + UI + tests for one feature). The DAG captures feature-level dependencies, so independent steps can be implemented in parallel by sub-agents.

Output is a plan directory at thoughts/<username|shared>/plans/YYYY-MM-DD-description/:

root.md            # Overview, mermaid DAG, step index, Global Verification
step-1.md          # frontmatter (id, name, depends_on) + Success Criteria
step-2.md
└── step-N.md

Setup (before starting)

  1. Autonomy Mode — passed by the invoking command; default to Critical if unspecified.

    ModeBehavior
    AutopilotResearch independently, write the full plan dir, present for final review only
    Critical (Default)After each research step, ask clarifying questions before drafting; surface design options especially at DAG decomposition
    VerboseCheck in at every sub-step: validate understanding, confirm scope, surface unknowns, confirm before writing each step file
  2. Commit preference — unless Autopilot, ask once via AskUserQuestion:

    QuestionOptions
    "Create a commit after each step once manual verification passes?"1. Yes (Recommended), 2. No, I'll handle commits
  3. Prior learnings — OPTIONAL SUB-SKILL: if ~/.agentic-learnings.json exists, run /learning recall <topic> first.

The 10 Rules

  1. Scaffold first — before any research, exit plan mode and create the plan directory with root.md from cc-plugin/base/skills/v-planning/templates/root.md. (Use the user's name when known, e.g. taras; fall back to thoughts/shared/ otherwise.) Step files are added as the DAG emerges in rule 4. The directory grows incrementally; the user can correct course early.

  2. Sub-agent everything heavy — file reads, research, validation. Default run_in_background: true. Keep raw tool output out of the main session. Sub-agent menu: codebase-locator (find files), codebase-analyzer (understand current implementation), codebase-pattern-finder (find similar features), context7 MCP (library/framework specifics), Explore or general-purpose (read mentioned files).

  3. Ask via AskUserQuestion — see desplega:ask-user for conventions. Never ask in chat as plain bullets.

  4. Ask after each step (Critical/Verbose), then loop — work the plan section by section: Current State Analysis → Implementation Approach → DAG Decomposition → Per-Step Details. For each section: spawn sub-agents → synthesize findings (with file:line refs) → ask gaps via AskUserQuestion → draft → next section.

    • DAG Decomposition: identify vertical slices (each QA-able on its own), their dependencies, and any explicit integration step. Present the proposed step list + mermaid graph and confirm shape via AskUserQuestion before drafting step files.
    • Per-Step Details: create each step-<n>.md from cc-plugin/base/skills/v-planning/templates/step.md. Frontmatter depends_on: [step-X, ...] is canonical; root.md's mermaid graph + step-index table is a derived view — keep them in sync.
  5. Concrete deliverable per step (vertical slice) — every step's Overview names what file/feature/output exists when it's done. Each step must be QA-able on its own. Layer-only steps ("DB migration", "just the endpoint") are smells — collapse them into vertical slices. "Improve X" and "refactor Y" are also smells.

  6. Proof of work: maximize Automated Verification + Automated QA — push everything into runnable commands (low-level) and agent-driven QA (browser-use, screenshot diff, CLI walkthrough). Manual Verification is the exception. Each step has its own Success Criteria block; root.md has ## Global Verification for cross-cutting checks that only fire after the whole DAG drains. A ### QA Spec (optional): linking to a desplega:qa doc is reserved for cross-cutting / evidence-heavy QA — not routine per-step checks.

  7. Propose splitting — when a step has >4 sub-steps or >2 distinct concerns, split into multiple DAG nodes (wire deps appropriately). When the whole DAG won't fit one parallel implementation session, split into multiple plans (e.g., contract → storage → UI). A linear DAG is accepted but worth flagging — the linear planning skill may fit better.

  8. Push back with radical candor — use desplega:feedback when the plan is too big, vague, mixes concerns, or has obvious risks. Silence is Ruinous Empathy.

  9. Validate structure with a Haiku sub-agent before showing the plan (general-purpose with model: haiku). Verify: every step-<n>.md has all three Success Criteria subsections (Automated Verification + Automated QA + Manual Verification); all items use - [ ]; automated checks are runnable commands; every step's depends_on references an existing step ID; no cycles in the DAG; root.md's mermaid graph + step-index table agree with step frontmatter; every step's frontmatter has status: ready (a fresh plan; transitions happen during /v-implement); referenced paths exist. Apply fixes before reveal.

  10. Hand off to a fresh session — never implement here. Close-out:

    1. Open /file-review:file-review <plan-dir>/root.md (unless Autopilot); iterate on comments. Re-open with individual step-<n>.md files if needed.

    2. Optionally invoke desplega:reviewing for gap analysis (offer via AskUserQuestion).

    3. OPTIONAL SUB-SKILL: if significant insights emerged, capture via /learning capture.

    4. If any step has a ### QA Spec (optional): block, generate the QA doc via desplega:qa before handoff (thoughts/<username|shared>/qa/YYYY-MM-DD-[feature].md). Scenarios live in the doc, not the plan.

    5. Ask via AskUserQuestion:

      QuestionOptions
      "Plan ready. What's next?"1. Implement in a fresh session, 2. Run /review first, 3. Done for now (park the plan)
    6. Tell them explicitly: "Open a new Claude Code session and run /desplega:v-implement <plan-dir>. Starting fresh keeps the implementation context clean."

DAG Specifics

  • Frontmatter is canonical for deps. root.md's mermaid graph is derived. If they disagree, frontmatter wins.
  • Self-contained steps. A sub-agent handed only one step-<n>.md (plus plan-level context from root.md) should be able to implement it without reading sibling steps.
  • Frontmatter carries execution state. Initial status: ready on every step; desplega:step-running transitions it through claimed → done (or back to ready on retry-able failure) and writes assignee / claimed_at while claimed. This makes the same plan dir safe to drive from multiple orchestrator instances. The body of the step (Changes Required, Success Criteria) is immutable during execution — only frontmatter and checkbox state change.
  • Integration steps are explicit when needed. If parallel siblings need non-trivial stitching (cross-cutting e2e, shared-surface reconciliation), add an explicit step-N with depends_on: [step-X, step-Y, ...] whose work is "stitch + e2e". Otherwise the DAG just terminates at its leaves.

Commit Integration

If commit-per-step was enabled in Setup:

  • After each step's manual verification passes, commit with format [step-N] <brief description>.
  • Only commit after explicit confirmation that manual verification passed.
  • Otherwise, skip — the user handles commits.

Success Criteria Format (MANDATORY)

Canonical format and heading hierarchy live in:

  • cc-plugin/base/skills/v-planning/templates/root.md (## Global Verification)
  • cc-plugin/base/skills/v-planning/templates/step.md (per-step three-bucket Success Criteria)

Structure validation runs automatically (rule 9, Haiku sub-agent).

FAQ

What does the v-planning skill do?

Vertical / parallel implementation planning skill. Creates DAG-structured plan directories where each step is an independent, QA-able vertical slice that sub-agents can pick up and implement in parallel. Use whenever the user wants a plan that fans out (multiple independent features), invokes /v-plan, or asks for a "parallel plan", "DAG plan", "vertical plan", or "plan that can be parallelized" — even if they don't say those exact words. Prefer the linear planning skill for strictly sequential work.

How do I install v-planning?

Run npx skills add https://github.com/desplega-ai/ai-toolbox --skill v-planning in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/v-planning/ (personal) or .claude/skills/v-planning/ (project).

Which AI agents can use v-planning?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is v-planning safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains v-planning?

It is published by desplega-ai in desplega-ai/ai-toolbox. It has 274 recorded installs.

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