Skip to content

Example: Content Calendar Planning

This page walks through the complete output of running a real workflow through all three steps of the Business-First AI Framework. The workflow is Content Calendar Planning — a weekly process for planning and sequencing content across LinkedIn, Substack, X, and YouTube.

The framework produced three deliverables. Each one is a detailed markdown document. This page summarizes what's in each and why it matters — then links to the full file on GitHub where you can read every table, every decision point, and every failure mode at full width.

The Three Deliverables

1. Workflow Definition (Step 2 — Deconstruct)

View full Workflow Definition on GitHub

The Workflow Definition is what Deconstruct produces. What started as "I plan content on Sundays" became 10 refined steps across four phases after the five-question deep dive.

What's inside:

  • Scenario metadata — workflow name, trigger, outcome, business objective
  • 10 refined steps organized into four phases: Input Gathering (4 steps), Planning (4 steps), Approval (1 step), Execution (1 step)
  • Each step decomposed with: Action, Sub-steps, Decision Points, Data In, Data Out, Context Needed, and Failure Modes
  • Dependency map showing which steps can run in parallel and which must be sequential
  • Context shopping list — 9 artifacts the workflow needs, with status (Exists / Needs Creation)
  • Related workflows — upstream and downstream dependencies

Key insight: Notice how each step captures not just what to do, but what to do when things go wrong. Step 1 has a fallback for when no metrics data exists. Step 2 detects an empty backlog and escalates the importance of Step 3. Step 9 limits refinement to 3 rounds to prevent endless iteration. This failure-mode thinking is what makes the workflow robust enough for AI to execute.

2. AI Building Block Spec (Step 3.1 — Design)

View full Building Block Spec on GitHub

The AI Building Block Spec is what Design produces from the Workflow Definition. It classifies each step on the autonomy spectrum, identifies which steps should become reusable skills, and recommends an implementation order.

What's inside:

  • Execution pattern selection — why "Skill-Powered Prompt" was chosen over a full agent (the creative middle steps benefit from human judgment)
  • Step-by-step decomposition table — each step classified by Phase, Autonomy level, Building Blocks needed, Skill candidacy, and whether it's a Human Gate
  • Autonomy spectrum summary — AI-Deterministic (4 steps), AI-Semi-Autonomous (4 steps), Human + AI-Assist (2 steps)
  • 4 skill candidates with full specifications: purpose, inputs, outputs, decision logic, and failure modes
  • Dependency map and critical path analysis
  • Context inventory — what data sources and reference materials each step needs
  • Recommended implementation order — Quick Wins first (prompt + 2 simple skills), then Semi-Autonomous (2 complex skills), then Future Enhancements

Key insight: The spec doesn't say "build everything at once." It recommends starting with a prompt — pure conversation, no infrastructure — so you get value immediately. Database skills layer in incrementally. This build order means you're running the workflow on day one.

3. Workflow Prompt (Step 3.2 — Construct)

View full Workflow Prompt on GitHub

The workflow prompt is one of the platform artifacts that Construct produces. This is the ready-to-run prompt you paste into any AI tool to execute the workflow.

What's inside:

  • Full 10-step orchestration — the AI knows exactly what to do at each step, what to present, and when to pause for human input
  • Phase-by-phase instructions — Input Gathering runs skills automatically, Planning proposes and waits for approval, Approval iterates collaboratively, Execution commits to the database
  • Embedded context — channel cadence targets, channel-format fit rules, and content pillar definitions are baked into the prompt so no external documents are needed
  • Input and output specifications — what the user provides (fresh ideas, calendar context, theme selection, approvals) and what the workflow produces (input summary, draft plan, final plan, database confirmation)

Key insight: The prompt reads like a conversation script. At each step, the AI knows whether to act autonomously, propose and wait, or ask for input. The human-in-the-loop gates (Steps 3, 5, 6, 9) are explicit — the AI won't proceed without approval at those points.

What to Notice

A few things to take away from this example:

  • The expansion. "I plan content on Sundays" became 10 steps across 4 phases, with decision logic, failure modes, and a dependency map. That expansion is what makes the workflow executable by AI.
  • The autonomy spectrum. Not every step needs AI autonomy. Steps 1, 2, 4, and 10 are fully automated (deterministic data operations). Steps 5-8 are collaborative (AI proposes, human decides). Steps 3 and 9 are human-led. The framework helps you see this clearly.
  • The build order. The Building Block Spec doesn't just say "build everything." It recommends starting with a prompt (pure conversation, no infrastructure needed), then layering in skills incrementally. You get value from the first run.
  • Platform-agnostic. The Workflow Definition and Building Block Spec work with any AI tool. The skills and MCP connections are implementation details that vary by platform — but the underlying logic is the same everywhere.

Try It on Your Own Workflow

Ready to run your own workflow through the framework?

Or start from the Business-First AI Framework overview for the full methodology.