pproenca Spike Workflow
Resolve an unknown approach before committing to one. The unknown is which class of solution is right — and the trap is that you cannot search for a class you do not know exists ("you don't look for what you don't know"). So the parse is a benchmark against a FLOOR, plus a solution-class enumeration driven by transferable reframings.
Why the FLOOR is non-negotiable (the anti-psychosis gate)
A result is only good against the limit, never against your own starting point. The cautionary case: an agent drove a renderer 88ms → 1.5ms (150K → ~500 allocs) and declared triumph; a hand-written reference did the same work in ~20µs with 0 allocations — a 75× gap it could not see because it compared to its own naive baseline, not the floor. Trusting "good" without parsing it against the limit is the quality twin of trusting unparsed input. Estimate the floor without knowing the winning approach: big-O lower bound, zero-allocation ideal, memory bandwidth ceiling, a hand-rolled reference. Then a result that sits far from the floor forbids declaring victory — the unknown-unknown becomes a visible anomaly.
The loop (the feature-workflow spine, spike instance)
- STEP 0 classified this as a spike.
change_new.py --kind spike --name "..."createsdocs/spikes/CHANGE_spike_<slug>.md. - Establish the FLOOR first and write it into the manifest. (See
references/floor.md.) - Enumerate solution CLASSES, not variants. Consult the
mental-modelsskill: feed it the smell ("this is slow", "this is O(n^2)", "deep nesting") and it returns the reframing questions that open classes you would not have searched for (allocation-bound vs compute-bound? data-oriented layout? asymptotically better structure? mechanical sympathy?). - Gate:
change_check.py --kind spike docs/spikes/CHANGE_spike_<slug>.md— blocks unless a floor is declared AND results are expressed as a ratio to it. - Benchmark across classes, report best-vs-floor as a ratio.
- Collapse: the chosen class turns the spike into a feature (new behavior), a refactor (behavior-preserving restructure), or a perf-refactor (output identical, timing changed → needs BOTH a characterization net and a benchmark-vs-floor gate). Run that kind's loop next.
- Feed the learning loop: any gap that exposed a missing model — a class
you didn't think to consider — is recorded in
mental-modelswith its trigger smell. The gap is the authority. That is how unknown-unknowns become known.
The one law here
A spike produces a decision backed by a floor-anchored benchmark, not code you keep. Resist building the first plausible approach; the cost of the wrong class dwarfs the cost of the spike.
Files
references/floor.md— estimating a floor without knowing the answer; the reframing-checklist handshake with mental-models.