AI Workflow Kit

Summary

AI Workflow Kit is a set of ready-to-use, markdown-defined skills that let an AI coding assistant run the recurring, non-coding parts of an engineer’s day: morning kickoffs, midday check-ins, end-of-day reviews, async standups, meeting prep, journaling, and code review. The skills are tool-agnostic (the same definitions work in Claude Code, Cursor, and Codex) and route through an integration layer, so no single service is hard-coded.

The problem

The biggest gap in most engineers’ productivity is everything around writing code: context gathering before work, notes during meetings, the status update the team needs, the end-of-day review that sets up tomorrow. Each takes ~15 minutes and gets perpetually deferred.

Why it mattered

These tasks compound. Skipping them quietly erodes focus, team awareness, and continuity across days. Automating them where the engineer already works (the terminal or editor) is the only place it sticks.

Constraints

• Must work across multiple AI assistants without rewriting each skill.
• Must not lock a user into one notes/tasks/calendar/chat vendor.
• Setup had to be low-friction enough that people actually adopt it.
• Skills are prompts; reliability has to come from structure, since there’s no runtime to enforce it.

Role

Creator and sole maintainer. Designed the skill format, the integration-abstraction approach, the setup wizard, and authored the skill library.

Architecture

Each skill is a markdown file following the Agent Skills specification, an emerging standard supported by Claude Code, Codex CLI, and VS Code Copilot. Skills reference integrations (“fetch my calendar events”), and an integration layer resolves those to Google/Apple Calendar, Notion/Obsidian, Jira/Linear, Slack/Discord, etc. The recommended auth path is the Zapier MCP server: one connection to thousands of apps instead of per-service API keys.

Key decisions

• Markdown skills over code plugins: portable across tools, diffable, and editable by the user without a build step.
• Integration indirection: skills describe intent; the binding to a specific vendor happens at setup, so the same skill survives a tool switch.
• A setup wizard that asks which tools you use and wires everything up, so there’s no config file to hand-edit.

Tradeoffs

• Prompts-as-skills are less deterministic than compiled tools; the design leans on explicit structure and trigger phrases to compensate.
• Standardizing on the Agent Skills spec bets on an emerging standard, chosen deliberately for portability over a proprietary format.

Failure modes considered

• A skill firing against the wrong integration → mitigated by explicit setup-time binding.
• Assistant-specific quirks → mitigated by keeping skills declarative and testing across tools.

Outcome

A working, public kit (the author’s most-starred original repository) that covers workflow, dev, and personal skills, with a one-command setup. The companion article, “I Built 50 AI Skills for the Parts of My Day I Never Got Around To,” documents the broader system it grew from.

What I learned

Reliability for prompt-based automation lives in the surrounding structure (trigger phrases, clear step sequencing, and integration boundaries) far more than in clever wording.

What I’d change next

Extract the integration-abstraction layer into a documented, standalone spec others can build skills against, and formalize the cross-tool compatibility matrix.