GR-O01 states 2-6 players; every scenario in the corpus was 3-player.
Now all five counts play to GameEnded under both policies and reproduce
at the same seed, with scenarios at both boundaries and the CLI
transcript run at 2p, 3p and 6p.
Nothing broke — the rules are seat-count-generic. What the boundaries
exposed is arithmetic: with the standard preset's placeholder Problem
values (value = priority), the best total any game can reach is 3 at 2p,
6 at 3-4p, 10 at 5-6p, against GR-E01 thresholds of 5, 7 and 9. Group
success is unreachable below five seats regardless of play, and no
scenario noticed because none had played to scoring with everything
claimed.
GR-S01 calls the fixture a stand-in for scenario Problem data, so this
is evidence the stand-in is not neutral, not that GR-E01 is wrong. It is
pinned by a passing scenario, an arithmetic test, and a provisional
marker owned by ground-game so it ages in `make coverage`. The test
states its own delete-by: it is expected to fail when Problem values
become real data, and that failure is the signal to delete it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
A human seat is a Policy like any bot, so the CLI adds no second driver:
HumanPolicy renders the projection, lists the legal commands and reads an
index or `pass`. `make play` runs it; `--all-bots` watches one.
K13's Project trait gains its first implementor after six passes with
none. Hidden: other seats' face-down selections until Reveal, hands and
deck (counts only), a face-down Problem's suit and value, and the seed —
not secret content, but a seat holding it can compute the deck.
A played session becomes an artifact: --record writes it as a scenario
the runner executes, --replay writes a .cbreplay bundle. record.rs is the
inverse of parse_command and its warrant is a round-trip test over every
command shape.
The acceptance test for the projection passed vacuously twice. First it
asserted the text contained "face-down", which every render does because
of Problems. Counted, it then reported zero inspected entries: seats are
asked in order, so a human at P1 is prompted before anyone has selected.
Seated at P3 it inspects ten entries and dies when the projection is
mutated to reveal everything. Counting what the harness examined caught
both, which is the second time that remedy has worked where a stronger
predicate would not have.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
A Policy trait, a seeded random policy and a greedy one with a stated
heuristic, a legal-command generator that filters candidates through
validate, and a driver that runs a 3-player all-bot game to GameEnded.
Same-seed runs are hash-identical (K8), and a different policy seed
produces a different game — without that second assertion the first is
satisfied by a bot that ignores its RNG.
Every failure is loud, because the one a bot driver must not have is the
silent one: no legal move, passing where an action is required, an
out-of-range index (not clamped), a rejected command, and a stall guard.
What the second consumer found, none of it fixed here:
- GR-A13 admits SOLVE against an already-claimed Problem and resolution
then does nothing — the action is silently wasted. The policy avoids
it; the rule is left for a ruling.
- The 3-player standard fixture cannot reach GR-E01's threshold of 7:
three Problems valued 1,2,3 cap the total at 6. No scenario noticed
because none plays to scoring.
- K13's Project trait still has zero implementors. T02 is its first.
Mutation-checked by hand. The first mutation was a no-op and survived;
removing the Resolve call outright turned three tests red for the stated
reason. Third instance of the weak-mutation class.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
