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Dev Lifecycle

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codeaholicguy
dev-lifecycle

AI DevKit · Orchestrator for structured SDLC phase skills. Use when the user wants to run the full lifecycle or choose the next phase across requirements, design, planning, implementation, testing, and review.

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill namedev-lifecycle
Stars
1.6K
Forks
252
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Dev Lifecycle 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/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/dev-lifecycle .claude/skills/dev-lifecycle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dev Lifecycle 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 Dev Lifecycle 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 Dev Lifecycle 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.

Dev Lifecycle

Coordinate the phase-specific AI DevKit skills instead of running phase details directly.

Required phase skills:

  • dev-worktree for feature workspace setup and resume.
  • dev-requirements for phases 1-2: new requirement and requirements review.
  • dev-design for phase 3: design review.
  • dev-planning for phases 4 and 6: initial task planning and updates after implementation tasks.
  • dev-implementation for phases 5 and 7: execute plan and check implementation.
  • dev-testing for phase 8: write tests and verify coverage.
  • dev-review for phase 9: final code review.

Supporting skills:

  • memory for reusable project knowledge during clarification.
  • tdd for implementation tasks.
  • verify before completing implementation, implementation checks, testing claims, and review readiness.
  • task for optional progress tracing when the task command is usable.

Startup Validation

At the beginning of every dev-lifecycle run:

  1. Run npx ai-devkit@latest skill list to inspect currently installed project skills.
  2. Confirm the listed skills include all required phase skills and supporting skills.
  3. If any required skill is missing, run npx ai-devkit@latest skill add --built-in to install all AI DevKit built-in skills. Then rerun npx ai-devkit@latest skill list.
  4. If installation fails or a required skill is still missing, stop and report the missing skill names and command output summary. Do not run a phase without its skill.
  5. Run npx ai-devkit@latest lint to verify the configured AI docs structure.
  6. If working on a specific feature, run npx ai-devkit@latest lint --feature <name>.
  7. If lint fails because project docs are not initialized, run npx ai-devkit@latest init -a -e claude --built-in --yes, then rerun lint.
  8. Probe optional task tracing availability:
    • With a feature: npx ai-devkit@latest task list --name <feature-name> --json
    • Without a feature: npx ai-devkit@latest task list --json
    • Treat task tracing as available only if the read probe exits 0. If it fails, record task tracing as unavailable with the failed command and reason, then continue without task logging.
    • Never block lifecycle work only because the task command is missing or unusable.
  9. When working on a specific feature and task tracing is available:
    • Load and follow task before executing a phase.
    • Initialize or show the task named after the feature, mark active work, and emit phase/progress/next/blocker/evidence events per task.
    • Sequence task mutations; do not batch or parallelize mutations for the same feature.

Plan Before Execution

Before executing any phase:

  1. Identify the target feature, current docs state, branch/worktree context, and likely next phase.
  2. Propose a concise plan that names the phase skill to use, the docs/files to read, commands to run, expected edits, task tracing status and planned task events if tracing is available, and verification evidence.
  3. Wait for user approval before executing the plan unless the user already gave explicit approval for that exact phase execution.
  4. After approval, load and follow only the selected phase skill plus any explicitly required supporting skills. If tracing is available, the task skill is explicitly required.

Phase Routing

PhaseRoute toWhen
Setup. Workspacedev-worktreeStarting or resuming feature work
1. New Requirementdev-requirementsUser wants to add a feature or start /new-requirement
2. Review Requirementsdev-requirementsRequirements doc needs validation
3. Review Designdev-designDesign doc needs validation against requirements
4. Create Initial Plandev-planningRequirements, design, and testing docs are ready for task breakdown
5. Execute Plandev-implementationReady to implement tasks from planning doc
6. Update Planningdev-planningAuto-trigger after completing any implementation task
7. Check Implementationdev-implementationVerify code matches design and docs
8. Write Testsdev-testingAdd or verify test coverage
9. Code Reviewdev-reviewFinal pre-push review

Sequential flow: setup -> 1 -> 2 -> 3 -> 4 -> 5 -> 6 after each completed task -> 7 -> 8 -> 9.

Resuming Work

If the user wants to continue work on an existing feature:

  1. Use dev-worktree to identify and confirm the target branch/worktree.
  2. Run npx ai-devkit@latest lint --feature <feature-name> in the active context.
  3. Run the phase detector from the installed dev-lifecycle skill directory:
    • Resolve <skill-dir> as the directory containing this SKILL.md.
    • Run <skill-dir>/scripts/check-status.sh <feature-name>.
    • Use the suggested phase when proposing the execution plan.

Backward Transitions

Not every phase moves forward. When a phase reveals problems, route back:

  • Requirements review finds fundamental gaps: return to dev-requirements Phase 1.
  • Design review finds requirements gaps: return to dev-requirements Phase 2.
  • Design review finds design flaws: stay in dev-design and revise design.
  • Implementation check finds major deviations: return to dev-design if design is wrong, or dev-implementation if code is wrong.
  • Testing reveals design flaws: return to dev-design.
  • Review finds blocking issues: return to dev-implementation or dev-testing.

Rules

  • Use npx ai-devkit@latest lint and npx ai-devkit@latest lint --feature <name> to discover and validate the configured docs directory. Do not assume docs/ai; it is only the default.
  • Read existing configured AI docs before changes. Keep diffs minimal.
  • Keep feature names aligned with branch/worktree feature-<name>.
  • New feature docs come from npx ai-devkit@latest docs init-feature <name>. Use the paths returned by the command as authoritative.
  • Existing feature docs are the paths reported or validated by npx ai-devkit@latest lint --feature <name>. If you must infer manually, first resolve the configured docs directory from .ai-devkit.json paths.docs, falling back to docs/ai.
  • After each phase, summarize output and suggest the next phase.
  • Do not claim completion without fresh verification evidence.
  • When task tracing is available, follow task: create once, assign actor when known, mark active/blocked, set phase, record progress/next/evidence, and close only after final verification/review. If tracing is unavailable, include failed probe commands in the phase summary without blocking the lifecycle.

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 Dev Lifecycle AI skill do?

AI DevKit · Orchestrator for structured SDLC phase skills. Use when the user wants to run the full lifecycle or choose the next phase across requirements, design, planning, implementation, testing, and review.

Why use Dev Lifecycle on TypingMind?

Because you install it once and use it with any model. Dev Lifecycle 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 Dev Lifecycle in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/dev-lifecycle. 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 Dev Lifecycle?

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 Dev Lifecycle?

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

Is the Dev Lifecycle AI skill free?

Yes. It is published on GitHub by codeaholicguy 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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