Read docs/PROMPT_caps_rename_operator_usage.md

Prompt — raise caps, spell out "GP" as "genetic programming", and add DSL operator-usage

Three changes, verified against the code (web/app/Lab.tsx, api/app.py, engine_v2/gp.py). Airgap untouched. pytest + tsc after.

1. Raise the Generations / Population ceilings (web/app/Lab.tsx)

The parameter list (~lines 1202-1208) caps generations at max: 100 and population at max: 500. Raise both:

• generations → max: 1000
• population → max: 3000 Keep them BOUNDED (not removed) so an accidental huge value can't hang the backend. Leave min/step and the other params unchanged. (Context, not a code requirement: with the current early convergence, more generations alone won't help much — the useful long run is "Maintain diversity" ON + larger population.)

2. Spell out "GP" as "genetic programming" in all USER-FACING copy (web/app/Lab.tsx)

Replace the abbreviation everywhere it appears in visible text — captions, tooltips, headings, labels, axis titles, sort buttons, table headers. Use "genetic programming" in prose; for the compact fitness label use "Genetic-programming fitness" (hyphenated; wraps fine). Known locations (search for GP to catch any others):

• Parameters subtitle (~1222): "GP knobs…" → "Genetic-programming knobs…"
• Run subtitle (~1374): "Starts a GP run on the backend." → "Starts a genetic-programming run on the backend."
• Copy/tooltips at ~197, 227, 244, 2119, 2588-2591, 4012, 4586 → "genetic programming" / "the genetic-programming search".
• The "GP fitness" label everywhere shown: the sort-key label (4617 ["gp_fitness", "GP fitness"]), the table header (4682 <th>GP fitness</th>), the scatter axis title (4233), the scatter heading (4008 "Landscape — GP fitness × Synergy"), the median-guide label (4185), and the tooltip dt (4407) → "Genetic-programming fitness" (scatter heading → "Genetic-programming fitness × Synergy"; axis → "Genetic-programming fitness — what the engine preferred →").
• Code COMMENTS that say "GP" (~286, 2160, 2237, 2435, 3800, 4156) are not user-facing — optional. Do NOT change the internal field/key name gp_fitness (API contract) — only human-readable labels.

3. DSL operator-usage across the whole search (API + frontend)

Show, after a run completes, how often each DSL operator (Select, Reduce, Combine, Split, Associate, Effect, Fit/Apply, Search) was used across every candidate program in every generation (Generations × population). Airgap-safe — operator names are DSL keywords, no gene names.

3a. API — new endpoint GET /runs/{run_id}/operator-usage

Mirror _compute_module_ranking (which already walks run.log):

• Iterate every candidate in every persisted generation (run.log[*].candidates). Each candidate has a program_repr string (e.g. Combine(Reduce(Select(M,[…]),min),Reduce(Select(M,[…]),var),protected_div); built in engine_v2/gp.py ~line 132).
• Per operator, count occurrences of its token Name( in program_repr — Select(, Reduce(, Combine(, Split(, Associate(, Effect(, FitApply(, Search(. (Matching Name( avoids false hits; MatrixTerminal/M is not an operator.) Sum across ALL candidates across ALL generations.
• Return per operator: total_uses (sum of occurrences) and programs_using (count of candidate-instances containing it ≥ once); plus run totals (n_generations, n_candidates). Note that persistent elites are counted once per generation they appear in (the "Generations × population" grid the user asked for).
• Opaque/airgap-safe (operator keywords + integer counts only). Cache per run; 425 while running, 404 unknown.

3b. Frontend — show it on the "DSL vocabulary" tiles

After a run completes, fetch /runs/{id}/operator-usage and on each operator tile show its usage:

• Under each description: "used 12,431× — in 38% of programs" (total_uses, and programs_using / n_candidates).
• A small inline bar per tile (relative to the most-used operator) so the distribution is scannable — Select/Reduce dominate; Effect/Split/Fit-Apply/Search are rare or 0 (Search off by default → 0).
• Before a run / while running, tiles show just descriptions (gate the counts to a completed run).
• Tile-panel "?" / caption: "Counts every use of each operator across all programs the search tried (generations × population). Select and Reduce are the backbone; Effect, Split, Fit/Apply and Search appear only where the engine reached for them."

CONSTRAINTS

• Airgap: the operator-usage endpoint emits keywords + counts only — no gene IDs/names. Engine/GP search untouched. gp_fitness data key unchanged.
• Frontend reads the new endpoint; no change to the run flow.

Checkpoint

• Generations accepts up to 1000, Population up to 3000.
• No visible "GP" remains in the UI ("genetic programming" / "Genetic-programming fitness"); gp_fitness key unchanged.
• GET /runs/{id}/operator-usage returns per-operator counts over all generations × population, opaque-safe; tiles show count + share + bar after a completed run (Search reads 0 when off).
• pytest green; airgap untouched; tsc clean.