Spec-Driven Planning

Most AI research fails not during execution, but before it starts. Someone says "research X" and the agent charges off, produces 10 pages of surface-level findings, and misses the actual question. The user reads it, sighs, and starts over with more instructions. Repeat until exhaustion.

The fix is embarrassingly simple: don't start until you know exactly what "done" looks like. Interview the user. Surface the hidden requirements. Write a specification so precise that an agent can execute it autonomously at 3am and produce exactly what was needed.

This skill exists because of a hard-won lesson: the teams that sleep well during overnight research are the ones who did the most work before pressing go.

Why the Default Approach Fails

When you hand Claude a research task, it defaults to:

• The eager start -- begins researching immediately, filling gaps with assumptions instead of questions.
• The shallow spread -- covers everything at surface level, nothing at useful depth.
• The missing "why" -- produces facts without knowing what decision they inform.
• The ambiguity cascade -- one vague requirement produces five vague phases, each producing vague deliverables that nobody can evaluate.