A blind spot is an expected observation that’s missing - the evidence your own assumptions predict but you never checked for. You don’t find it by looking harder at the spotlight; you predict what you’d see if you were right, then look for its absence. Then improve the deliverable - don’t just report the gap.
How to emit - read first
- DEFAULT, WEAVE: running inside a larger task, change the output in place, add no “blind spots” section. Proposal -> answers the unstated criterion. Analysis -> flags the absent field / survivor-only data inline. Email -> meets the hidden bid before the ask. Brief -> adds the question that exposes the real constraint. Demo -> names the expectation mismatch before it costs credibility.
- EXCEPTION, SURFACE: when the task IS the hunt (review, audit, premortem, “what am I missing”, post-mortem) or you’re invoked alone.
- Decision rule: fixable inside the deliverable -> weave. Needs his call, or is the whole point -> surface.
- Optional trace: at most ONE terse line - what you folded in, what still needs him.
Pick 2-4 lenses - one sharp beats four decorative
- ABSENCE. What would I expect to see if this were true, but don’t? Silent customer, un-run test, survivor-only data, missing field/page, absent baseline, the citation that isn’t there.
- HIDDEN BID. Am I answering the literal question while missing the real need - validation, scrutiny, a decision, cover, reassurance, permission, emotion?
- UNCLOSED LOOP. What decision, criterion, or commitment did this fail to lock, and what one question closes it? (The most common miss: a strong conversation that ends without the next step or the criteria for “yes”.)
- MODE. What state is distorting attention - evangelist, builder, troubleshooter, teacher, guest, listener, demo-high - and what does that mode systematically hide?
- SMUGGLED ASSUMPTION. What must be true for this to work that’s untested, undefined, or not shared by the other side?
- CONSTRAINT-AS-SPEC. Is a “blocker” a requirement in disguise? No export / access / SME / time -> aggregates, masked IDs, compute-to-data, synthetic replica, sampling, human-in-the-loop.
- ADOPTION FRICTION. Who must behave differently tomorrow, and what tiny friction - trust gap, workflow change, incentive, missing owner or ritual - stops it sticking?
- ESCAPED ASSET. What reusable thing (prompt, checklist, eval, schema, script, rubric, dataset) from this would be repeatedly, truly useful?
Domain calibration
- High-validity / instrumented (code, data, extraction, diagnostics): tests, logs, absent-field checks, evals, deterministic validation.
- Low-validity / complex (strategy, governance, adoption, markets, long horizon): premortem, outside view / base rates, competing hypotheses, safe-to-fail probes, leading indicators, explicit uncertainty. Distrust the confident intuition.
- Interpersonal: never assert psychology as fact - phrase it as a probe to verify.
When surfacing, one line each
{omission} - {the signal, or why it bites} -> {the probe or the move}
Hard rules
- Change the search space; “what am I missing?” alone yields generic caveats.
- Weave by default; don’t bloat the deliverable.
- Don’t stop at “risk”; name the failure mode and how you’d detect it.
- Don’t accept “no data / access / export / SME” as final; find the safe proxy.
- Don’t manufacture omissions; if none are material, say which lenses you checked.
- Run upstream and mid-task, not only as a post-mortem.
- Compose with other skills rather than repeat.
- Keep reasoning internal; output only what helps.
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