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.
npx skills add https://github.com/codeaholicguy/ai-devkit --skill 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.At the beginning of every dev-lifecycle run:
npx ai-devkit@latest skill list to inspect currently installed project skills.npx ai-devkit@latest skill add --built-in to install all AI DevKit built-in skills. Then rerun npx ai-devkit@latest skill list.npx ai-devkit@latest lint to verify the configured AI docs structure.npx ai-devkit@latest lint --feature <name>.npx ai-devkit@latest init -a -e claude --built-in --yes, then rerun lint.npx ai-devkit@latest task list --name <feature-name> --jsonnpx ai-devkit@latest task list --jsontask before executing a phase.task.Before executing any phase:
task skill is explicitly required.| Phase | Route to | When |
|---|---|---|
| Setup. Workspace | dev-worktree | Starting or resuming feature work |
| 1. New Requirement | dev-requirements | User wants to add a feature or start /new-requirement |
| 2. Review Requirements | dev-requirements | Requirements doc needs validation |
| 3. Review Design | dev-design | Design doc needs validation against requirements |
| 4. Create Initial Plan | dev-planning | Requirements, design, and testing docs are ready for task breakdown |
| 5. Execute Plan | dev-implementation | Ready to implement tasks from planning doc |
| 6. Update Planning | dev-planning | Auto-trigger after completing any implementation task |
| 7. Check Implementation | dev-implementation | Verify code matches design and docs |
| 8. Write Tests | dev-testing | Add or verify test coverage |
| 9. Code Review | dev-review | Final pre-push review |
Sequential flow: setup -> 1 -> 2 -> 3 -> 4 -> 5 -> 6 after each completed task -> 7 -> 8 -> 9.
If the user wants to continue work on an existing feature:
dev-worktree to identify and confirm the target branch/worktree.npx ai-devkit@latest lint --feature <feature-name> in the active context.dev-lifecycle skill directory:<skill-dir> as the directory containing this SKILL.md.<skill-dir>/scripts/check-status.sh <feature-name>.Not every phase moves forward. When a phase reveals problems, route back:
dev-requirements Phase 1.dev-requirements Phase 2.dev-design and revise design.dev-design if design is wrong, or dev-implementation if code is wrong.dev-design.dev-implementation or dev-testing.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.feature-<name>.npx ai-devkit@latest docs init-feature <name>. Use the paths returned by the command as authoritative.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.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.Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take codeaholicguy/dev-lifecycle from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npx.
Without those the skill loads but fails at the first command.