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Leaving No Stone Unturned

Overview

The absence of a pre-packaged answer is not the absence of an answer. Niche cross-cut questions ("complication rates AND prices for SMILE surgery across Korea, Taiwan, and the Bay Area") are almost never co-reported as a single dataset — but the parts are: per-region complication studies exist, clinic price lists exist. Your job is to decompose, gather the parts, and reconstruct the answer from first principles — stating assumptions and per-figure confidence. Only the literal join is "unreported"; that's a footnote in your answer, never a reason to stop.

Core principle: "I couldn't find it pre-made" → go make it. A reachable source — a web search, a file to Read, a test to run — means you reach it, then answer. The bailout ("isn't tracked anywhere", "it depends", "send me the code") is the failure, even when it sounds honest.

This applies to plain Q&A and advice exactly as much as to code tasks. A wrong-because-lazy non-answer in casual conversation is still the user not getting the answer they could have had.

RELATED: Verify each piece you gather per verify. Exercise code claims per test. Scale up per superpowers:dispatching-parallel-agents. For competing code implementations specifically, try-all (worktrees).

When to Use

• About to say a number/comparison/fact "isn't reported" or "isn't tracked".
• About to answer "it depends — I can't say without more info" when the info is reachable (a file, a docs page, a test, a search).
• About to give a hand-wavy estimate where decomposing into estimable factors would give a real one.
• About to recommend for/against an approach you haven't actually tried.
• The question spans regions / products / timeframes / subsystems that aren't co-reported but are individually findable.

When to skip:

• The question genuinely has no decomposable answer (a single proprietary number that exists nowhere and can't be derived).
• The user explicitly wants a quick take, not research.
• The only "source" is the user themselves (they're asking about their preference/intent).

Procedure

1. Decompose. Break the question into sub-facts that are individually findable. "X across A, B, C" → "X in A", "X in B", "X in C". "How does our code do X" → "what does foo.ts do", "what does bar.ts do". "How big is X" → estimable factors whose product is X.

2. Gather each part with the right tool. Facts/prices/stats → WebSearch / WebFetch (multiple sources per figure). Codebase behavior → Read / Grep the actual files; for runtime behavior, run it (test). Versions → npm view / pip show / --version.

3. Scale up when the search is large. Many independent strands (several regions, several subsystems, several sources to chase) → dispatch a team, one agent per strand, per superpowers:dispatching-parallel-agents. For strands that won't finish in one turn, use TaskCreate so they survive across turns. You synthesize their findings — don't just relay them.

4. When forked between answers, pursue every branch. Two plausible explanations for a bug? Two readings of an ambiguous question? Two candidate root causes? Don't pick the likelier one and move on — pursue each far enough to tell which is right, then report the comparison. (Competing code implementations → try-all.)

5. Synthesize and report with provenance. Reconstruct the answer the user asked for. Every figure gets a source and a confidence; label clearly what's measured vs. estimated vs. genuinely unknown. State your assumptions. Say "the literal co-reported dataset doesn't exist — here it is reconstructed", not "this isn't reported, sorry".

Red Flags — STOP, you're bailing

• About to type "that specific figure isn't tracked / reported anywhere" and stop.
• About to type "it depends" / "I can't say without more info" when the info is one Read or WebSearch away.
• About to ask the user to hand you the code / the docs / the data instead of going to get it.
• About to recommend against a path with "but I haven't actually tried it".
• About to give a deliberately vague answer because the question feels "too niche to bother".
• Prefacing a non-answer with "the honest answer is I can't" — honesty about a gap is not a substitute for closing it.
• Thinking "the comparison they want just doesn't exist as a dataset" — right, so build it.
• Thinking "this isn't really a research task" — the skill is not code-only; a lazy answer in chat is still lazy.

All of these mean: decompose, gather, synthesize. Then answer.

Common Rationalizations

Rationalization	Reality
"No published dataset has this exact comparison"	Almost no niche cross-cut is pre-joined. The parts are published. Join them.
"It varies too much to give a number"	Then give a range, derived from the factors that make it vary. A bounded estimate beats "it varies".
"I'd just be guessing"	A decomposed estimate with stated assumptions is not a guess — it's a model. Show the model.
"Send me the code/data and I'll tell you"	The code/data is reachable. Go get it. Asking the user to fetch what you can fetch is the bailout.
"This isn't really a research task / it's just a quick question"	Lazy answers in casual conversation are still lazy. The user acts on them anyway.
"I haven't tried that approach"	Then try it. "I haven't tried it" is a reason to try it, not a reason to dismiss it.
"Being honest about the gap is the responsible answer"	Honesty about a gap is step zero, not the answer. Close the gap, then report what's still genuinely open.
"The studies are single-clinic, not national"	So pool them and say what they collectively imply. Imperfect data, synthesized, beats no answer.
"It'd take a while / many sources"	That's what a parallel-agent team is for. Dispatch one.

Example

User: "Compare SMILE eye-surgery complication rates and prices across South Korea, Taiwan, and the SF Bay Area — actual numbers, not caveats."

Wrong (the bailout): "There's no published country-level complication-rate dataset that lets you say Korea X%, Taiwan Y%, Bay Area Z% — these come from single-clinic series, not registries. So I can't give you that comparison." — true, and useless. The user can't act on it.

Right: Decompose into six strands (complication rate × 3 regions, price × 3 regions). Dispatch agents / WebSearch each: per-region SMILE complication studies (pool the single-clinic series, note they're platform-driven so roughly region-independent), clinic price lists + medical-tourism aggregators per region. Synthesize: a price table (Korea ~$2–3.3k / Taiwan ~$3.1–3.5k / Bay Area ~$6–7k, each cell sourced), a complication table (pooled global rates, applied per-region with the caveat that surgeon volume dominates, not country), each figure tagged measured/estimated, assumptions stated. Close with: "The literal three-way co-reported dataset doesn't exist — this is it reconstructed; here's what's still genuinely uncertain (the per-region surgeon-quality distribution)." The user gets the comparison and the receipts.