Can use candidate-framing generation review with a defined task, suitable inputs, and a basic validation check.
augmented skill · Stage 03
Candidate-framing generation review
Candidate-framing generation review is the discipline of evaluating an AI-drafted set of problem statements. It helps a designer convert evidence into a specific, falsifiable problem worth solving while keeping the final judgment traceable and human-owned.
Why it matters
This skill supports reliable work in Insight Generation and prevents speed from being mistaken for quality.
Convert the Stage 02 fact base into a defined, ownable problem statement. Promoted to its own stage because problem framing is the highest-leverage human-judgment step in the process and was previously buried as a sub-bullet
What good looks like
Frames candidate-framing generation review against a clear goal, relevant evidence, and the stage quality gate.
Directs AI with scoped inputs, reviews uncertainty, and redirects weak or unsupported output.
Documents the decision, trade-off, correction, or override so another designer can follow the reasoning.
Checkpoint application
Apply this skill before the Stage 03 gate is approved. The accountable designer must be able to explain how it changed the work, what evidence was considered, and why the result is safe to advance.
Common failure patterns
- Selecting the most polished framing rather than the one best supported by evidence.
- Accepting fluent or high-confidence output without testing its evidence, assumptions, exclusions, and downstream effects.
- Recording the final artefact while omitting the rejected option, correction, trade-off, or override that explains the decision.
Five-minute practice
Use it on the work you already have.
Take one current insight generation artifact. Apply candidate-framing generation review for five minutes, then record one AI contribution, one human correction, and the evidence that justified the final decision.
Evidence of capability
- A reviewed problem statement, framing rationale, or evidence trace showing the skill in use.
- A short rationale linking the decision to evidence, risk, and intended outcome.
- An example of an AI suggestion that was corrected, rejected, or deliberately accepted.
Leadership assessment questions
Can the designer demonstrate candidate-framing generation review on a real insight generation decision?
What observable evidence distinguishes competent performance from confident explanation?
Which anchor skill depends on this capability, and how would a gap surface in delivery?
Maturity ladder
Can diagnose weak output, redirect the work, combine sources, and explain the resulting trade-off.
Can decide when not to delegate, own the human checkpoint, and remain accountable for the outcome.
Backlinks and relationships