The shipped 1000 levels have a hole: 568 of them need no rotation at all, and
none needs more than two — the mechanic the game is built on is mostly
decoration. This fills the empty band with levels that need at least three.
createDeepLock builds a four-piece chain where each link only releases at a
later orientation, and growDeepLevel packs the rest of the board around it
without opening a shortcut. Candidate pools are built per orientation and left
untruncated; ranking by bend count first starved the packer and capped levels
at four to six pieces instead of twelve.
deadTurnAudit enumerates every reachable (orientation, remaining-set) state to
prove no dead ends exist, and counts how long a player can be left with
nothing to do — solvable and feeling solvable are different things, which is
what "level 2 is impossible" turned out to be about.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Four ways in, depending on what you have: a shell agent driving bench.mjs, a
direct-API loop for models with only an endpoint (plain text in, one JSON line
out, so weak and local models can play), a rule-following bot that sweeps 1000
levels in 12 seconds, and a folder you can hand to someone else.
bench-kit.mjs builds that folder: CLI, engine, level pack, observer page and
tile atlas, about 1.5 MB, needing only Node 20 — no repo, no install, no
network. The engine files are discovered by following imports so a new module
can't be left behind, every path rewrite is asserted so a rename fails the
build loudly, and the builder plays a real game inside the result before
declaring it done.
bench-report.mjs aggregates two ways: per level (is this level any good) and
per model (how does this agent play). bench-import.mjs brings runs played
elsewhere back in under a tag.
bench-pack.mjs is the earlier, smaller export with no observer page; superseded
by bench-kit but kept in history rather than dropped silently.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Let any agent with a shell play a level the way a person does: look at the
board, decide what to tap or which way to turn, read the feedback, decide
again. Every step carries the agent's own reason, so a human can watch and
judge the level from how it was played.
observe() returns what the eye sees — each piece's drawn route, which cells it
crosses, who is sitting on it, and who it brushes past — never "which pieces
can leave". act() goes through the real Game rules; metrics() scores the run
afterwards with the solver.
Pieces are wide, so a route with nobody standing on it can still be blocked by
a neighbour the curve hugs. That is invisible in a cell listing, so every
obstacle is reported, on-route and grazing alike: reporting only the leading
one hid the rest, and in 113 of 581 such cases clearing the blocker sprang a
collision the agent had never been shown.
AGENT.md is the single file an agent reads — rules, board reading, commands,
rating scale, worked example.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Add the paper direction with thin tracks, outlined hexagons and line arrows, without exterior color markers or piece numbers. Add a unified catalog for five designs and their 25 themes, with direct review links.
Organize each design into an authored design.md, generated tokens.json and preview. Add Agent routing, shared integration guidance, a machine-readable manifest and an executable rendering example. Preserve legacy preview and document links.
Validation: 16 focused design, theme and package tests passed; npm run design and static build passed. Confirmed all eight authored documents survive generation unchanged. Browser checks covered mobile, tablet and desktop layouts, theme links and migrated previews. Native engine integration and visual verification remain pending.
Restore v0.1 and v0.2 archives with separate progress, publish the curved v0.3 campaign, and document the original 24-level design with reproducible evidence. Refine board presentation and movement, and keep video lab tooling within its own directory.
