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Development Lifecycle Router Maintainer

Community
Undertone0809
development-lifecycle-router-maintainer

Use when a Rudder development request has an unclear lifecycle stage or owner: requirements, advisor analysis, UI design, implementation, verification, review, release, recovery, runtime contracts, performance, component lab, handoff, or named-skill optimization.

Overview

PublisherUndertone0809
Repositoryrudder
Skill namedevelopment-lifecycle-router-maintainer
Stars
290
Forks
31
Bundled files
9
LicenseApache-2.0
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.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Undertone0809 on GitHub. Read the source before you install it.

Installation

Install the Development Lifecycle Router Maintainer 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.
  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/Undertone0809/rudder.git /tmp/rudder
mkdir -p .claude/skills
cp -r /tmp/rudder/agent-skills-bak/maintainer/development-lifecycle-router-maintainer .claude/skills/development-lifecycle-router-maintainer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Development Lifecycle Router Maintainer 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 Development Lifecycle Router Maintainer 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 Development Lifecycle Router Maintainer 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.

Development Lifecycle Router Maintainer

Use this skill as a routing layer: choose the current lifecycle stage, name the exit bar, and hand off to the smallest capable maintainer skill or normal coding workflow.

The reason this skill exists is to prevent two expensive mistakes:

  • jumping from a complaint into implementation before the correct stage is known
  • calling a stage complete without the product proof or reviewer evidence that the user's request actually requires

Keep this file thin. Load the reference files below only when the current route needs them.

Reference Map

  • references/route-selection.md: stage classifier, narrow-skill routing, meta-request precedence, and skill-optimization boundaries.
  • references/verification-review.md: verifier gates, terminal product proof, spawned reviewer policy, reviewer lenses, agent prompt templates, and evidence ledger.
  • references/special-routes.md: recovery, component lab, performance benchmark, runtime/provider contracts, and hard real-local validation.
  • references/handoff-git.md: git safety, final handoff shape, acceptance blockers, and common route templates.

Fast Start

Before editing files, running long checks, spawning reviewers, or committing:

  1. Inspect git status --short --branch.
  2. Classify the current stage.
  3. State the selected route and downstream owner in one concise update.
  4. Name the artifact or proof required to leave the current stage.
  5. For development work, state the full loop before editing: implementation, writer checks, verifier, final reviewers, reconciliation, and handoff. Development work enters spawn-required mode by default; the user does not need to separately ask for subagents, review, or verification.
  6. Decide which references are needed for this route and read only those.

For obvious narrow requests, do not expand a lifecycle plan. Say the route, load the narrow skill, and execute it.

Non-Use Gate

Use the narrow maintainer directly when all of these are true:

  • the prompt names a concrete surface, run, PR, release, screenshot, data path, or local runtime problem
  • the next useful artifact is obvious for that surface
  • the task does not need cross-stage sequencing, reviewer orchestration, or destructive recovery judgment before the narrow work can begin

Keep ownership in this router only when it adds value by choosing a stage, resolving ambiguity, sequencing multiple stages, or protecting a high-risk handoff.

Core Rule

Route first, then execute.

State the lifecycle stage and acceptance bar before implementation. Any routed development task enters spawn-required mode by default. This applies when the task changes user-visible, agent-visible, Desktop, release, runtime, CLI, workflow, or product behavior, even if the user only says "fix", "optimize", or "推进". The normal implementation sandwich is:

text
writer implementation
-> writer basic checks
-> optional lightweight pre-review
-> spawn availability probe if needed
-> spawned verifier black-box acceptance
-> spawned final reviewer gate
-> handoff / commit / push

Pre-review is only for catching obvious diff, startup, safety, scope, or test readiness problems before verifier time is spent. It is not the final reviewer gate. Final review follows verifier PASS so reviewers can inspect the diff, tests, handoff, and verifier evidence together.

Separate verification from review:

  • verification asks whether the product path meets the requirement from the user's side
  • review asks whether the diff, architecture, scope, tests, proof, and handoff are trustworthy

For development routes, do not treat the loop as optional just because the user said "fix", "优化", "推进", or omitted the word review. User-visible, agent-visible, Desktop, release, runtime, CLI, workflow, and Rudder changes need the verifier plus final reviewer loop before complete handoff. If a route truly does not need that loop, say why it is not applicable rather than silently skipping it.

spawn-required means:

  • after writer checks, probe the available spawn/subagent mechanism before declaring verifier or reviewers unavailable
  • if spawning works, create the verifier and final reviewer agents and wait for their verdicts before complete handoff
  • if spawning is unavailable, record the failed probe as blocked: spawned verifier/reviewer unavailable
  • do not claim complete handoff, commit/push readiness, or review pass from author-run tests, CI, screenshots, self-review, or serial personas alone

Stage Classifier

Choose one primary stage. If multiple stages are present, choose the earliest blocking stage.

  • intake: user intent, target artifact, or mode is unclear
  • requirements: problem framing, scenarios, acceptance criteria, or "do you understand?"
  • advisor: first-principles diagnosis of a build, UI, workflow, trace, or proposal that feels wrong
  • ui_design: interface direction, wireframe, visual hierarchy, or screenshot-based design judgment before code
  • implementation: approved direction or direct fix/build request
  • verification: tests, CI, E2E, screenshots, Desktop smoke, actor-run-chain, release checks, or black-box acceptance proof
  • review: review, PM judgment, first-principles critique, or Codex/session/PR/commit verdict
  • debug: failed run, UI path, data path, CI job, Desktop app, or local process
  • release: canary/stable release, npm, Desktop assets, tags, or GitHub Release state
  • handoff: final summary, validation, commit, push, residual risk, or PR
  • recovery: dirty worktree, stash, interrupted run, rollback, delete/restore, or suspected old Codex work
  • component_lab: UI Lab, component inventory, fixtures, or design-system coverage
  • performance_benchmark: benchmark or optimize before the exact bottleneck is known
  • runtime_contract: provider/runtime/tool-call/transcript/parser/CLI or agent-visible contract parity
  • skill_optimization: optimize, harden, refactor, validate, benchmark, package, or improve a named skill or workflow skill

For full routing detail, read references/route-selection.md.

Narrow Routes

Prefer the smallest matching owner:

  • screenshot-driven UI polish: rudder-ui-polish-maintainer
  • missing, stale, suspicious, or slow page data: rudder-data-path-diagnostician-maintainer
  • run transcript, stdout/stderr, recent run batch, or run-quality issue: debug-run-transcript-maintainer
  • Desktop startup, packaged app, Electron shell, update, profile, local instance recovery: rudder-desktop-dev-recovery-maintainer
  • release, npm, GitHub Release, Desktop assets, tags, dist-tags, install smoke: release-maintainer
  • review-only session/PR/commit/proposal/UI/release outcome: agent-work-reviewer-maintainer or codex-session-product-reviewer-maintainer
  • local branch preview: rudder-worktree-preview-maintainer
  • GitHub PR checkout/preview/review: pr-local-preview-maintainer
  • mock/demo/seed data: mock-data-maintainer
  • landing screenshots: landing-proof-shots-maintainer
  • stop/restart/clean local dev runtime: stop-rudder-dev-maintainer
  • new or existing reusable workflow skill creation, optimization, eval, benchmark, packaging, or description tuning: skill-creator

If this router itself is the target artifact for optimization, route to skill-creator or the user-requested skill-engineering workflow. Do not run this router's normal product lifecycle except for git safety around the patch.

Skill Optimization Boundary

The newest user instruction is the routing source of truth.

If the user says a skill "needs optimization", "should be hardened", "always does the wrong thing", "I have to ask this every time", or asks to use skill-creator on a named skill, classify the turn as skill_optimization.

Treat screenshots, prior requirements, session ids, quoted logs, and prior assistant recommendations as evidence for the skill failure. They are not the active product task unless the newest user instruction says to continue that product task.

For this route:

  • name the target skill and path
  • extract the failed decision point
  • choose the smallest durable owner: target skill body, frontmatter description, eval case, memory update, or no-op
  • add or update a validation case when the behavior should change next time

Verification And Review Defaults

For user-visible, agent-visible, Desktop, release, runtime, CLI, workflow, or product changes, identify the terminal product surface before calling verification complete. Use product-acceptance-verifier-maintainer for a distinct black-box acceptance pass. If the product path is too expensive or unsafe to exercise, record verifier blocked or substituted with the exact reason; do not silently skip acceptance.

Spawned verifier and reviewer gates are the default for routed development artifacts when this router owns the lifecycle. If spawning is available, the verifier should be a distinct child agent using agents/product-verifier.md or equivalent product-acceptance-verifier-maintainer instructions. Final reviewers should be child agents using the templates under agents/ or equivalent reviewer prompts, with these lenses:

  • functional trust
  • adversarial
  • heuristic/product-systems

Do not replace spawned verifier or reviewer verdicts with self-review, serial personas, or author-claimed screenshots. If the active runtime cannot spawn agents after a real availability probe, record blocked: spawned verifier/reviewer unavailable with the probe evidence instead of claiming the loop passed.

For the full verifier/reviewer contract, read references/verification-review.md.

Execution Outline

  1. Build a compact routing packet:
    • latest user request and corrections
    • git status --short --branch
    • named files, screenshots, sessions, runs, PRs, commits, or plans
    • relevant AGENTS.md route docs and nearby skill contracts
    • changed-file ownership if cleanup or recovery is involved
  2. Declare route and stage exits.
  3. Execute the current stage using the narrow owner or normal repo workflow.
  4. Run the required verification and review gates for that route.
  5. Hand off with evidence, blockers, git state, and commit/push status.

Minimal Handoff Shape

markdown
Route: ...
Stage exits: ...
Used: ...
Review: spawned reviewers / blocked / not applicable
Validation: passed / not run / not proven
Evidence: required / scenario / proven / missing or substituted
Git: commit / push
Residual risk: ...

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Development Lifecycle Router Maintainer AI skill do?

Use when a Rudder development request has an unclear lifecycle stage or owner: requirements, advisor analysis, UI design, implementation, verification, review, release, recovery, runtime contracts, performance, component lab, handoff, or named-skill optimization.

Why use Development Lifecycle Router Maintainer on TypingMind?

Because you install it once and use it with any model. Development Lifecycle Router Maintainer 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 Development Lifecycle Router Maintainer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Undertone0809/rudder/tree/main/agent-skills-bak/maintainer/development-lifecycle-router-maintainer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Development Lifecycle Router Maintainer?

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 Development Lifecycle Router Maintainer?

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

Is the Development Lifecycle Router Maintainer AI skill free?

Yes. It is published on GitHub by Undertone0809 under the Apache-2.0 license. 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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