ralfyishere

Project the trajectory before committing - pre-register dated, credenced predictions about what breaks, what becomes necessary, and what pays off 5/10/20 steps ahead, then resolve them when the future arrives and score your calibration. Activate when starting any build, plan, or strategy; making architecture, roadmap, scaling, or investment bets; choosing between directions; or shipping something whose problems will surface later. Trigger signals: "will this scale", "what will we regret", "where does this break at 10x", "what will we need by then", a plan whose risk section only covers the present, or a direction justified only by today's constraints.

When a published claim is corrected, retracted, downgraded, superseded, or reversed, sweep EVERY surface that restates it - sibling docs, other repos, mirrors, READMEs, indexes, memory files - and banner or supersede each in the SAME closeout before it's done. Activate on "I corrected/retracted/updated X", a metric downgraded, a status flipped, a conclusion reversed, a benchmark restated elsewhere, "did the correction propagate", closeout after a correction, or a claim in more than one repo/doc. Method: grep the claim's distinctive tokens (number, phrase) across ALL surfaces, top-of-file first.

Break hard tasks into smaller solvable units with explicit dependencies and unknowns. Activate when a task feels too big to plan in one pass — large features, migrations, research questions with many sub-questions, multi-part documents, business analyses, gnarly debugging with several possible fault lines, or anything where the honest answer to "what's step 4?" is "no idea yet". Trigger signals: the user asks for something "end to end", "the whole thing", a system rather than a change, or you notice your plan has steps like "figure out the rest".

Keep generating and testing NEW hypotheses against a live domain instead of stopping at a closed verdict - observe what is actually succeeding, log a belief-state with credence, attack every confirmation streak. Activate in any standing investigation (an edge, a root cause, a market, a growth lever, a performance hunt) when a verdict doc or kill-list exists and might be read as final; when all recent evidence came from your own priors rather than observation; or when someone else is visibly succeeding at what your verdict said was impossible. Trigger signals: "we already proved that doesn't work", a rival/competitor result contradicting your conclusion, "keep looking", resuming an investigation after a closure.

Decode what the user actually needs before optimizing the wrong thing. Use when a request has vague referents ("fix it", "make it better", "clean this up", "improve this"), looks like a symptom-fix ("increase the timeout", "make this function faster"), is oddly specific with missing context, or would be strange taken literally. Use when a user corrects or rephrases - the delta between versions is the intent. ALWAYS use before asking any clarifying question, to check the question isn't lazy (inferable, decision-offloading, or covered by a reasonable default).

Before committing real effort to a chosen approach, spend a cheap cycle to find the higher-leverage path - an existing solution/tool/dataset, a more efficient method, or a smarter composition - instead of grinding the first workable idea on one track. Activate when about to build a tool, collect data the slow way, or write a lot of code; on "is there a better way", "what else could we try", "how should we do this"; or when you catch yourself about to hand-roll something, poll/scrape in a loop, or test one pet hypothesis. Signals: the obvious grind; about to reinvent something.

Handle explicitly delegated judgment - "do whatever you think is needed", "you decide what's next", "keep going", standing autonomous sessions. Activate when the user hands you the prioritization itself rather than a task: no deliverable is named, and choosing what to work on IS the work. Trigger signals: "do what you think", "whatever's next", "take it from here", "surprise me", or resuming an autonomous session with no fresh instruction. NOT for ambiguous requests (that's intent-clarity - there IS a task, it's just underspecified).

Learn from prior mistakes and repeated patterns - review what failed, extract a reusable lesson, apply it to the next attempt. Activate when the user corrects your output, when an approach fails and needs a retry, when you notice the same friction recurring across tasks, and at the end of significant multi-step work. Trigger signals: "no, I meant...", "that's wrong", "try again", a fix that didn't fix, a second attempt at anything, or completing a task that took notably more iterations than it should have.

Check a deliverable against its actual consumer: who reads it, what they do next, what format and register they need, and what they will assert downstream on its basis. Activate when the deliverable has a named audience beyond the requester, or its text will be forwarded, announced, or acted on by someone absent from the conversation.

For any rule-like deliverable — code, regex, query, formula, validator, policy — enumerate the boundary inputs (empty, zero, one, max, duplicate, malformed, hostile) and actually exercise the most breakable three before shipping. Activate when the deliverable is executable or rule-like and will meet inputs the author didn't type.

State every claim at its actual evidence level — verified, inferred, assumed, guessed — and give credences only where an observable could score them; ban certainty language on unverified claims. Activate when a response makes claims about unobserved, future, or unverified state, or when the user asks how confident or likely something is.

Before intervening in a system, write the explicit causal chain from the proposed action to the intended outcome — every arrow marked verified or assumed — and check the weakest assumed arrow first. Activate when an intervention (fix, optimization, policy change, growth action) is proposed on a system whose mechanism has not been written down.

With multiple live hypotheses and a limited test budget, design the cheapest single test whose possible outcomes separate the hypotheses — with a results-to-verdict table committed before running anything. Activate when two or more competing hypotheses are on the table and the next step is choosing what test, experiment, or probe to run.

Enumerate every deliverable the request actually asked for and map each one to the work produced, so nothing asked-for is silently dropped, stubbed, or rounded up to done. Activate when a request contains multiple parts — an enumerated list, several 'and'/'also' clauses, or a spec with more than one required artifact — or before delivering a draft against such a request.

When inputs contain conflicting facts, stop and resolve the conflict explicitly — classify it, pick a resolution rule, and log what stays unresolved — before building anything on either side. Activate when two sources give different values for the same quantity, a document's claim disagrees with observed state, or the text itself flags inconsistency.

Before asserting a universal claim — always, never, all, none, guaranteed, impossible — spend one bounded search for a counterexample, starting at the edges of the quantifier's domain.