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Sprint Planning

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cubetribe
sprint-planning

Plan-first orchestration (ADR-004): comprehensive PLAN.md, sprint files with write-scope ownership, preflight checks, serialized integration, and the release sprint. Use for any non-trivial or multi-part request BEFORE dispatching agents.

Overview

Publishercubetribe
RepositoryClaudeCode_GodMode-On
Skill namesprint-planning
Stars
54
Forks
10
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 cubetribe on GitHub. Read the source before you install it.

Installation

Install the Sprint Planning 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/cubetribe/ClaudeCode_GodMode-On/tree/main/skills/sprint-planning
  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/cubetribe/ClaudeCode_GodMode-On.git /tmp/ClaudeCode_GodMode-On
mkdir -p .claude/skills
cp -r /tmp/ClaudeCode_GodMode-On/skills/sprint-planning .claude/skills/sprint-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sprint Planning 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 Sprint Planning 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 Sprint Planning 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.

Sprint Planning (Plan-First Orchestration)

Replaces the pre-v8.5 Version-First rule. Work is planned before it is dispatched; the version is decided at release, not at start. Release law: docs/orchestrator/VERSIONING.md.

When to plan

RequestPath
Trivial fix (one file, one concern)Implicit sprint-00 — no plan ceremony, but changelog-at-integration still applies
One coherent feature/bugSingle sprint file, minimal PLAN.md (or extend an existing plan)
Multi-part work, release overhauls, anything touching >1 domainFull plan: PLAN.md + sprint files

Artifacts

ROADMAP.md                          # living roadmap: initiative → target version → status
plans/vX.Y.Z/PLAN.md                # master plan: analysis, target architecture, sprint index, global ownership
plans/vX.Y.Z/sprint-NN-<slug>.md    # per sprint (template: docs/templates/SPRINT_TEMPLATE.md)
reports/vX.Y.Z/sprint-NN/           # TRACKED agent/subagent reports (committed at integration)

vX.Y.Z is the target version of the plan — a working label. The actual bump happens only in the release sprint and may differ if scope changed (re-label the plan folder then).

Writing the plan

  1. Analyze first — understand the repo state before decomposing (audit sprint if needed).
  2. Decompose into sprints with disjoint write scopes where possible. Every sprint file carries: Goal, Scope, Non-Goals, Files/Write-Scope table, Risks, Acceptance Criteria, Test/Validation Strategy, Changelog Note, Version Relevance, execution mode.
  3. Ownership matrix in PLAN.md — for hot files (VERSION, CHANGELOG.md, ROADMAP.md, README.md, plans/**) name the single writer and the sprint that owns them.
  4. Order by dependency — tooling before consumers, contracts before implementations, docs after behavior. Mark which sprints could run in parallel (disjoint scopes only).
  5. User review — present the plan before execution for scope-relevant work.

Executing sprints

Per sprint (see CLAUDE.md "Sprint Execution"):

  1. Preflight: git status clean; plan assumptions still valid; no in-progress sprint with overlapping write scope; sprint status → in-progress.
  2. Dispatch: agents get the sprint file path + their write scope in the prompt. Agents obey their Sprint Contract (stop on BLOCKED (scope|conflict) — never overwrite foreign work).
  3. Gates: dual quality gates; a BLOCKED (conflict) halts this and dependent sprints.
  4. Integration (serialized — one sprint at a time): @scribe adds the [Unreleased] changelog entry; orchestrator fills the sprint Result, ticks acceptance criteria, status → done. Commit per sprint with a Conventional Commit message — including the sprint's reports/ folder (reports are tracked; temp-dir output is a contract violation).

Parallel sprints — decision rule

Parallel execution is allowed ONLY when ALL are true:

  • write-scope tables are disjoint (verified, not assumed),
  • neither sprint touches hot files outside serialized integration,
  • no dependency between their acceptance criteria.

Otherwise: sequential. When parallel writes to overlapping paths are truly needed, isolate in git worktrees and serialize the merge order explicitly.

Release sprint (always last)

  1. All sprints done, [Unreleased] non-empty, working tree clean.
  2. Resolve inherited drift first (missing tags etc. — node scripts/release-check.js lists it).
  3. Bump type = max of all sprint Version Relevance fields.
  4. node scripts/version-bump.js <type> → bump + [Unreleased] promotion + full touchpoint sync.
  5. Verify: npm run version:check + npm run release:check green (ahead-of-tag is OK on release/*).
  6. Release PR → merge (merge commit) → .github/workflows/release-tag.yml tags + drafts the GitHub Release → maintainer publishes. RCs: vX.Y.Z-rc.N as GitHub pre-release.
  7. Flip ROADMAP entry to released.

Push/tag/publish always requires explicit user permission.


CC_GodMode — © 2025–2026 Dennis Westermann (dennis-westermann.de). Proprietary — not open source. Free for private, non-commercial use; redistribution or re-hosting outside GitHub is prohibited; attribution required. Official source: github.com/cubetribe/ClaudeCode_GodMode-On. See LICENSE.

Frequently asked questions

What does the Sprint Planning AI skill do?

Plan-first orchestration (ADR-004): comprehensive PLAN.md, sprint files with write-scope ownership, preflight checks, serialized integration, and the release sprint. Use for any non-trivial or multi-part request BEFORE dispatching agents.

Why use Sprint Planning on TypingMind?

Because you install it once and use it with any model. Sprint Planning 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 Sprint Planning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cubetribe/ClaudeCode_GodMode-On/tree/main/skills/sprint-planning. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sprint Planning?

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 Sprint Planning?

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

Is the Sprint Planning AI skill free?

It is published on GitHub by cubetribe. 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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