Spec-Driven Development with Claude Code in Action

I used to run big refactors the same way most teams do: open the code, start changing files, and keep a mental model of everything I touched.

That approach works until complexity spikes.

Once you cross a certain threshold, you lose thread integrity. You forget a dependency. You patch one issue and create two side effects. You restart the session and spend 20 minutes rebuilding context.

That’s why I shifted to spec driven development claude code workflows for larger implementation work. The rule is simple: research first, write the spec, refine assumptions, then delegate implementation against the spec as a contract.

This post is a practical version of that system. If you want adjacent context, read CLAUDE.md Progressive Disclosure for Fast Teams and AEO for B2B Websites.

Hook

The trigger for me was a migration that looked “straightforward” on paper.

It touched storage, synchronization, and integration points across multiple modules. I started directly in implementation mode, and within an hour I had partial progress with rising uncertainty.

No single change looked wrong, but overall coherence was fragile.

I stopped, rewound, and ran the same work with a spec-first approach. The difference was immediate: cleaner sequencing, less context thrash, better recovery when I had to restart, and much easier review.

The key insight: for complex work, the spec is not documentation. It is execution infrastructure.

Problem Framing

Without a spec-first workflow, AI-assisted development usually fails in predictable ways.

1. Context pollution. You keep too many moving pieces in one session, and the model drifts.

2. Task ambiguity. The agent starts coding before assumptions are explicit.

3. Weak recoverability. If a session degrades, you lose high-quality intent and spend time rebuilding it.

4. Non-atomic changes. Large mixed commits hide causality and make rollbacks painful.

5. Quality bottlenecks. Everything waits for one final manual review pass.

Spec-driven execution addresses all five.

Spec-Driven Development Framework

Here is the workflow I now use for medium-to-large refactors with Claude Code.

Phase 1: parallel research before implementation

Start by asking Claude to investigate architecture and reference patterns in parallel tracks.

Research tracks often include:

• current-state architecture,
• target-state design options,
• edge-case behavior,
• migration constraints,
• dependency risks.

The objective is not code changes. The objective is understanding and decision boundaries.

Phase 2: write a spec as source of truth

Convert research into one practical spec document.

A useful spec includes:

1. current state summary,
2. target state definition,
3. phased migration plan,
4. task checklist,
5. acceptance criteria,
6. rollback notes.

This file should be explicit enough that implementation tasks can reference it directly without interpretation gaps.

Phase 3: refine the spec with Q&A

Before touching code, run a clarification pass:

• unresolved tradeoffs,
• data migration expectations,
• compatibility decisions,
• fallback behavior,
• test scope.

You are reducing ambiguity while changes are still cheap.

Phase 4: task orchestration with dependency control

Break implementation into task units with clear dependencies.

Rules:

• one task = one cohesive outcome,
• avoid mixed-scope tasks,
• preserve ordering where dependencies exist,
• run independent tasks in parallel where safe.

This keeps context focused and improves reproducibility.

Phase 5: atomic commits + automated backpressure

Each completed task should map to an atomic commit and pass the same quality gate.

Practical backpressure loop:

Task complete -> run checks -> commit
If checks fail -> fix immediately in-task
If checks pass -> move to next dependency

This prevents low-quality work from accumulating late in the flow.

Phase 6: recovery protocol

When a session goes off-track:

1. reopen spec,
2. identify current task status,
3. paste error context,
4. continue from task graph instead of memory reconstruction.

The spec becomes your recovery anchor.

If I Had to Start from Zero Today

If I had to set this up for a team this week, this is the sequence I would run.

Day 1: establish templates

• create spec template,
• create task template,
• define commit/check policy.

Day 2: run one pilot refactor

• choose a real but bounded migration,
• execute full research -> spec -> task flow.

Day 3: harden checkpoints

• define mandatory quality checks,
• add dependency tagging rules,
• add rollback criteria to spec template.

Day 4: improve handoff clarity

• ensure tasks include purpose, files touched, and acceptance criteria,
• ensure status is visible and persistent.

Day 5: publish team operating doc

• document workflow,
• add examples of good and bad tasks,
• assign ownership for spec quality.

A small pilot with strict structure beats a big rollout with weak contracts.

Examples and Counterexamples

Counterexample: “code now, document later”

You move quickly at first, then lose coherence when edge cases appear.

Better: “research now, spec now, code with contract”

You spend slightly longer upfront and save significant recovery time later.

Counterexample: one giant implementation task

Task is hard to review, hard to test, and hard to roll back.

Better: dependency-aware task slicing

Each change unit is understandable, verifiable, and reversible.

Counterexample: quality checks only at the end

Bugs accumulate and debugging scope explodes.

Better: in-task validation

Catch issues where they originate.

Counterexample: session memory as primary source of truth

Session reset means heavy re-explanation overhead.

Better: spec + task state as durable memory

Recoverability improves dramatically.

Mistakes to Avoid

1. Writing specs that are aspirational instead of executable.
2. Creating tasks without acceptance criteria.
3. Mixing unrelated concerns into one commit.
4. Skipping clarification passes because “we can fix later.”
5. Treating the orchestrator as a coder instead of coordinator.

Summary Table

Implementation Checklist

• Created a reusable spec template.
• Added current-state and target-state sections.
• Added phased task list with dependencies.
• Defined acceptance criteria per task.
• Enforced in-task validation and atomic commits.
• Documented a recovery protocol.

FAQ

Is spec-driven development slower?

It can feel slower at the start, but total cycle time is often shorter because rework and recovery overhead drop.

When should I use this workflow?

Use it for multi-file refactors, migrations, and any task with meaningful dependency complexity.

Do I need subagents for this to work?

No. Subagents help with parallelism, but the core value comes from spec quality and task structure.

What should the spec never omit?

Acceptance criteria, dependency order, and rollback assumptions.

How do I keep this lightweight?

Keep the spec practical and scoped. It should guide execution, not become a thesis.

Conversion CTA

If you want, I can help you design a spec-driven workflow for your team and turn your next large refactor into a controlled delivery plan.

• Message us on WhatsApp
• Email us

You can also explore related systems: CLAUDE.md Progressive Disclosure and Next.js Consent-Gated Analytics.

Closing Synthesis

Spec-driven development is not bureaucracy. It is leverage.

When Claude Code is used with a contract-first process, you get better context efficiency, safer execution, and faster recovery under complexity.

That is the real advantage: not just coding faster, but shipping complex changes with less chaos.