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KohulanKohulan/orthonym-skills/skills/enumerate-first/SKILL.md

enumerate-first

Skill2 starsChanged 46 days agoMITView on GitHub

Replaces a reachability or likelihood argument with a seeded, generated sample of the input space and a failure count. Use when about to argue whether a case is reachable, likely, rare, or worth fixing — triggers on 'probably unreachable', 'edge case', 'in practice this can't happen', 'unlikely to matter', or deciding whether a reported defect is real. Also use before optimizing or tuning any decision point, to confirm it executes at all. Not for cases where a reproduction already exists.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Keep it as a gate: if it is fast enough, and mutation-test it (see prove-invariant).

    line 33
  2. Distinctive:

    Run the check that needs no oracle, no JVM, no network first.

    line 40

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

enumerate-first

The rule

Generate and measure instead of arguing. Report counts, not adjectives.

Reachability arguments are cheap to make and almost always wrong, because they reason from the design you intended rather than the code you have.

Why this exists

A tie that a code review called narrow and probably unreachable produced 1207 violations in three defect classes when 35,888 generated cases were fuzzed: none was the hypothesised one, and most sat in a component the argument had dismissed. A 625-configuration grid search once tuned a selector that executed zero times. Each was one measurement away.

Procedure

  1. Define the space. What varies — inputs, orderings, configurations, geometries? Write it down; the gaps in your list are where defects hide.
  2. Generate broadly, seeded. Random with a fixed seed, or exhaustive if the space is small. Reproducibility matters more than elegance.
  3. Run the property, count the failures. Print the count and a handful of concrete examples.
  4. Classify before fixing. Group violations — three defect classes look like one bug until you group them.
  5. Keep it as a gate if it is fast enough, and mutation-test it (see prove-invariant). If the gate passes on the mutant, tighten it and return to Step 3.

Cheap before expensive

Order probes by cost and let the cheap one filter for the expensive one:

  • Run the check that needs no oracle, no JVM, no network first.
  • Escalate only the survivors to the expensive check.
  • Batch the expensive check once over all survivors rather than per item.

Before tuning anything

Confirm the decision point executes: count its calls for the target inputs (spy-site). Documented central logic called zero times is common, not exotic.

Red flags

ThoughtReality
"This is a narrow edge case"You have a hypothesis, not a measurement.
"It would take an unusual input"Generate unusual inputs. That is the whole job.
"The review overstated it"Or understated it. Both are findings; only counting distinguishes them.
"I'll reason about which branch wins"Enumerate the branches and let the oracle decide.
"Too slow to fuzz"Then the sample is smaller, not absent.

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