Reverse Minesweeper Prototype Astra

I’ll locate the old project and both transcripts, then recover them into a clearly named Codex folder so Fable’s work stays separate.

I found the Linux prototype and its movement-reference sources. The first session includes four HTML versions, ending with the robot-digging/human-descent loop. I’m locating the original second transcript and the preserved version files before copying them.

The four original HTML snapshots are intact, along with the latest working prototype and the Spelunky, Celeste, Terraria, Noita, Hollow Knight, and Prince of Persia reference files. The archive search is slow; I’m checking transcript contents to distinguish the controller session from other conversations that merely mention the game.

Both sessions are recovered. The second ends with a substantial controller research report and a proposed controller lab; no Anchor port had been implemented. I’ve copied the original transcripts, research subagent records, reference sources, and all four prototypes into reverse-minesweeper-codex, and verified every copied file against the archive with SHA-256.

Recovered both from the Linux archive into reverse-minesweeper-codex.

• First transcript: idea exploration and four HTML prototypes.
• Second transcript: human controller research, including ledges, ropes, walls, fall damage, and camera.
• Playable prototype: latest robot/human version.

Also preserved the original transcripts, research subagent records, reference sources, and four separate HTML snapshots. All 42 copied files passed hash verification.

The previous work stopped at the controller research report; the proposed Anchor controller lab wasn’t implemented. Our folder explicitly records the separation from Fable’s work. Nothing elsewhere was changed.

I’d build a human controller lab next, with the recovered HTML game kept as the reference for why the project was fun. The prototype already established something worth pursuing: the robot makes terrain recklessly, then the human has to negotiate the consequences. The uncertain part is whether that second stage can become satisfying enough to sustain the game.

For the first batch, I’d use Anchor 3 and a simple rectangle character in a small, fixed test map. Include walking, running, variable-height jumping, falling, and collision, then ledge grabbing, hanging, dropping, and climbing back up. Those ledge actions are especially important: explosions naturally create stepped crater walls, so we should establish whether descending those shapes is enjoyable before adding more movement abilities. Put a representative crater in the lab alongside predictable stairs, narrow shafts, and measured drops.

I would keep ropes for the following batch and wall slides/jumps for later. Wall abilities can substantially change what terrain means. If any wall lets you arrest a fall indefinitely, much of the need to choose a route or spend a rope disappears. That might eventually be the right game, but I’d first find out how much we get from ledges and limited ropes.

The research gives us useful starting points, but its proposed settings combine several games. I wouldn’t call that combination “Spelunky-exact.” We should choose one coherent baseline, measure its jump and fall behavior, and then change it according to your experience. Mechanical checks are mine; deciding whether movement feels good is yours. Character art can wait until this basic movement is convincing, though animation will eventually affect that judgment considerably.

Once ledges and ropes feel promising, I’d bring in one frozen cave generated by the robot prototype. A tidy test room can make a controller seem better than it is. Navigating the irregular terrain the actual game produces is the meaningful test before porting the whole loop.

For working with both of us, I’d give Codex and Fable the same small objective and let each produce an independent version in its own folder. You can compare concrete results, then tell us which behaviors to retain. I’d avoid exchanging implementations during the first pass so the two attempts give you distinct options.

My suggested immediate batch: an Anchor movement lab with basic locomotion and complete ledge interactions, using plain shapes and one crater-shaped test section. That is enough for your next play session to answer a specific question: does carefully descending this terrain feel good?

I’ll build the first controller lab in our Codex folder: locomotion, ledge grabs, hanging, dropping, and climbing, with a fixed crater section to try. I’m using the Anchor skill to check the current engine conventions and verification workflow.

I’ll use the iterate workflow for this first playable pass. The baseline will have a short variable jump, quick acceleration, and explicit ledge grabs while holding toward a wall. Down plus movement will lower you off an edge into a hang; climbing back up will require a fresh input. The lab will show drop distance and landing speed so we can judge fall behavior before settling the damage rule.

The lab’s controller and three test sections are in place. I’m checking collision and state transitions now, including fast falls past ledges, lowering from both sides, blocked climbs, and jump timing. Ropes and wall slides are deferred so this pass can test whether ledge-based descent works on its own.

The controller passes 30 checks, including 36,000 collision stress ticks, and Anchor’s static check reports no errors or warnings. The measured jump is about 0.77 tiles on a tap and 1.61 when held. I’ll now test the controls in a hidden Anchor instance and capture the rooms and ledge states to check the presentation.

The hidden engine checks pass for movement, jumping, lowering, hanging, climbing, room switching, pause, and reset. The captures show readable controls and ledge contacts. I’m finishing the handover with a local launcher and a preserved Round 01 folder so you can compare this version with Fable’s.

The first lab is ready. Double-click run.bat.

It includes walking/running, variable jumping, ledge grabs, lowering, hanging, dropping, and climbing. 1–3 switch between ledges, shafts, and the crater. Controls are displayed alongside the game.

Start with the ledges, then try descending the crater. The main question is whether approaching an edge, lowering yourself, and catching the next ledge feels reliable and satisfying. There’s no health loss in this practice round.

Verified: 30 controller checks, 36,000 collision stress ticks, hidden-engine input tests, and clean static analysis. Fable’s work and all recovered originals remain untouched.

I’ll correct the iteration guidelines so preserved artifact folders apply to HTML and similar files, while Anchor games use their replay history. I’ll also update this project’s instructions to allow normal edits in place.

The cause was the iterate skill’s blanket “every artifact” preservation rule, reinforced by the Codex adapter’s “each delivered version at its own path.” I’m using the skill-creator guidance to narrow those rules and explicitly exempt Anchor game source folders, whose history is preserved by replays.

Corrected the shared iterate skill, Codex adapter, global guidelines, and project notes.

Anchor games now explicitly use one working folder edited in place, with replays preserving log history. Separate preserved versions remain required for HTML and other standalone artifacts.

Both skills validated. The existing lab path still works; future changes won’t create new round copies.

I’ll move the controller lab to reverse-minesweeper-codex/lab/ and update its launcher references and project notes.

Moved it to lab/ and removed the empty rounds/ folder. Updated references and verified it starts from the new location. Replays are preserved.

Launch with lab/run.bat.

I’d add ropes, then put the human back into terrain created by the robot.

Ropes introduce the first resource decision into the descent: “Can I negotiate these ledges, or should I spend a rope here?” They also give you a way through stretches where the current movement has no safe answer. That would test the game’s central interaction more directly than adding wall slides or polishing the character.

For the next batch, I’d keep the movement you liked and add:

• Throw upward, with flight and downward unrolling.
• Place downward from an edge or while hanging.
• Catch a rope while falling, climb both directions, and jump off.
• Limited rope inventory, with failed placement costing nothing.

We’d extend the lab with a few rope situations and use the crater to try combinations of ropes and ledges. The thing to judge is whether placement and catching feel dependable, especially when you’re already falling.

After that passes, I’d port the robot digging stage and camp transition, keeping these plain visuals. The fixed crater has served its purpose, but the important test is navigating the damage you just caused yourself. That will expose problems a carefully arranged lab won’t: awkward overhangs, inaccessible corners, huge shafts, and whether the robot’s optimal digging strategy creates an interesting human route.

I’d still defer wall slides and jumps. Ropes impose a cost on bypassing difficult terrain; wall abilities might make that cost unnecessary. Let’s first see how much game the current ledges plus ropes give us.

I’ll add ropes to our lab, keeping the locomotion you liked: upward throws, downward placement from edges and hangs, mid-fall catches, climbing, and jumping off. I’ll use the Anchor and iterate skills for mechanical checks, then hand it back for you to judge how placement and catching feel.

Rope behavior is in place. I chose four eight-tile ropes per room, with E to throw, Down+E to place downward, and Up to catch. Throws rise before unrolling; downward placements unroll immediately. I’m checking failed placements, fast catches, climbing near terrain, and the lockout after jumping off before testing the controls in Anchor.

The original movement checks still pass, and the rope tests pass 35 checks plus 48,000 mixed rope-and-ledge simulation ticks. That includes placement from either side of a hang, ceiling collisions, inventory failures, and jumping off without immediately recatching. I’ll now capture the throw, climb, ledge placement, and crater descent in a hidden Anchor instance to check the visuals and actual key bindings.

The hidden engine tests pass, including placing a rope without dropping from a ledge, transferring onto it, and descending into the crater. The captures show readable ropes, inventory, and controls. I’m updating the lab notes and preserving the test replay; the next judgment is yours: whether placement and catching feel dependable during play.

Ropes are ready in lab/run.bat. The locomotion you liked is unchanged.

• 4 opens rope practice; 3 opens the crater.
• E throws upward; Down+E places downward from an edge or hang.
• Up catches; Up/Down climbs.
• Direction+Space jumps off; Down+Space drops.
• Four ropes per room; R resets. Failed placements cost nothing.

Verified: 65 checks, 84,000 stress ticks, hidden-engine controls, and inspected captures. Try placement and mid-fall catching first.

I’d reconnect the robot stage and camp loop now. You’ve liked both controller passes, so we have enough movement to test the actual game.

I’d port the recovered HTML rules into our Anchor copy: mining, exposed numbers, useful mine explosions, energy, and pickups. When energy runs out, leave the robot in place, return to the human at camp, and descend using the ledges and ropes we’ve built. Reaching the robot establishes the next camp and starts another digging cycle. Keep the plain visuals and existing balance as the baseline.

The important question is whether digging and descending make each other more interesting. Do you sometimes avoid a great explosion because it would leave a terrible route? Does the human journey make you notice and remember what you did as the robot? Or does it feel like an interruption before you can mine again? We need player-created terrain to answer that.

I would restore the prototype’s fall damage for this integrated version. Unlimited retries are useful in the lab, but the human’s fragility is what gives route choice and ropes their value. Preserve the lab as a practice mode, and treat the inherited damage thresholds as provisional.

I’d defer enemies, wall abilities, shops, and visual polish until you’ve played several complete cycles. The next batch should answer one question: does the combined loop hold up with this controller?

I’ll port the recovered robot digging rules into our Anchor copy and connect them to the human controller and camp loop. I’ll keep the lab available for practice, restore the prototype’s fall damage in the integrated game, and verify the full cycle in a hidden instance.

The prototype’s baseline is clear: 45 seconds of robot energy, +5 seconds per new ten-tile depth line, +2 per nugget, and four human HP. Falls of six tiles or more deal damage; each additional four tiles adds one damage. Reaching the robot heals one HP and refills the ropes.

I’ll retain those values and the accepted human movement. The integrated game will open by default, with L switching to the practice lab. I’ll also let pending explosions finish and the robot land before handing control to the human, so it can’t stop suspended over a freshly blasted hole.

The integrated rules pass their first checks, including two complete cycles driven by ordinary digging and falling, with no forced recharge or camp transition. The existing movement and rope tests still pass.

I also checked the damage ordering: a fatal landing near the robot must end the run before camp healing can apply. Next I’ll test mouse mining, flags, mode switching, and the full loop through Anchor’s input system, then capture the main stages.

The full input test now passes: two complete cycles, mouse mining, flags, retry/new-seed controls, and switching to practice without draining the game’s energy. It caught a startup camera drift that moved the targeted tile under a held mouse button; I fixed that.

The Lua port also matches the original JavaScript tile hash exactly across 480 samples and retains 18 checked balance values. I’m doing the final blast/cave visual check and finishing the controls and verification notes.

The integrated game is ready: run.bat.

Dig with the robot, descend through its excavations using the human controller, and reach it to establish camp and repeat. Original energy rewards and fall damage are restored.

• L: practice lab / return
• R: retry the same seed
• N: new seed

Verified 94 simulation checks, exact terrain-generation comparisons, and two complete cycles through actual engine inputs. Static checks are clean; replays are preserved.

Try several cycles and judge whether the human descent makes your digging choices more interesting.

I’ll check which DLLs are there and confirm where they came from.

Yes, they came from Anchor. They’re FFmpeg libraries bundled with its standard executable for video playback and processing, totaling roughly 112 MB. The largest are avcodec (~63 MB), avfilter (~24 MB), and avformat (~21 MB).

I copied the standard Anchor runtime to make this folder independently runnable. Reverse Minesweeper itself doesn’t use video.

Anchor already supports a novideo build that doesn’t require those DLLs. That would be the appropriate smaller runtime for this game; I used the standard build for the initial setup.

I’d work on what you can see before committing to a descent, then test the loop under real pressure.

The robot knows where it dug, but you may not remember the exact shape by the time the human arrives. The human needs enough information to choose a route: where the next landing is, whether a rope reaches it, and whether the drop is survivable. Otherwise deaths can come from camera limitations rather than a decision you made.

For the next batch, I’d focus on:

• Looking down from safety. Make the existing peek useful from the ground, a ledge, or a rope, with enough range to inspect the next section.
• Clear rope placement feedback. While holding Down, show where a rope would anchor and how far it would extend before spending one.
• Readable fall danger. A restrained indication of the current damage tier, using the actual landing rule. We should distinguish “this landing costs one HP” from “this kills me.”

Then I’d play several cycles specifically looking for whether ropes run out too early, whether camps are too far apart, and whether the human stage offers meaningful route choices. Your verdict so far supports continuing, but it doesn’t yet tell us which part of the loop needs more complexity.

I’d still hold off on enemies and wall abilities. If the descent is already engaging with better information, the next addition can complicate an existing decision. If it remains mostly following a shaft downward, we should change how the robot creates terrain or how the human navigates it before adding hazards.

I’ll add wall sliding and wall jumping alongside the descent feedback: a longer look-down, downward rope previews, and landing-damage indicators.

For walls, I’ll use hold-toward sliding and Space to jump away, with a brief push away from the wall to prevent immediate recatching. Once a slide has slowed the human to a safe speed, it will clear accumulated fall danger; otherwise the existing distance-based damage rule would punish a controlled descent.

Wall behavior and the feedback changes are implemented. I added C to look down without releasing a ledge or descending a rope; it also shows the downward rope preview. Down still performs its existing movement actions.

The slide cap is 3.5 tiles per second. Fast falls brake toward it rather than stopping instantly, and fall danger clears only once that speed is reached. I’m now checking both wall orientations and the interactions with ledges, ropes, and damage.

The regression checks pass, and the new checks cover sustained slides, jumps from either wall, braking from a dangerous fall, exact preview placement, and warnings based on remaining HP. There’s also a new 5: Walls practice room.

I’ll now test the real key bindings in a hidden instance and capture the slide, jump, look-down preview, and lethal-fall indicator.

The hidden checks pass. Wall jumps push away reliably, C keeps the human attached while looking down, and the preview matches the rope that gets placed. The captures also confirm that a fast wall contact remains marked dangerous until braking completes.

The indicator says “LAND NOW” deliberately: it shows the damage an immediate landing would cause, including whether that would be lethal at your current HP. It doesn’t predict the cost of a lower landing or assume you’ll catch something on the way down.

Added to both the game and lab: run.bat.

• Hold toward a wall: slide.
• Space at a wall: jump away.
• Hold C: look down safely and preview downward rope placement.
• LAND NOW: shows safe, HP cost, or lethal using current health.
• L, then 5: wall practice.

Slides brake progressively; accumulated fall danger clears once you reach safe sliding speed.

Verified 129 simulation checks, hidden-engine controls, inspected captures, and clean static analysis. Replays are preserved.

I’d test and tune the relationship between robot digging and human descent, before adding another mechanic.

Wall sliding changes that relationship substantially. A continuous wall is now a safe route down without spending ropes. That may be exactly what the game needed, but it means the interesting decisions will probably happen where explosions break that continuity: crossing a crater, reaching a wall from open space, or choosing between a detour and a rope.

For the next batch, I’d add human-descent retry: preserve the cave exactly as the robot left it, then let you restart the human stage from camp with its starting HP and ropes. Keep it clearly separate from continuing a normal run. That would let you compare routes and movement choices without repeating the digging stage every time.

Then we should examine a few caves produced through ordinary play:

• Can you mostly hold toward one wall and reach the robot?
• Where do ropes become useful?
• Does a different digging route produce a meaningfully different descent?
• Is the human stage still enjoyable after the novelty of the movement wears off?

I wouldn’t weaken wall sliding preemptively. If descent becomes too automatic, I’d first look at terrain shape and the robot’s incentives. Right now deeper is better, so narrow downward digging is naturally attractive. Buried rewards off to the sides might encourage caves with more crossings and more route choices, but I’d want actual runs to establish that problem before changing it.

The next useful result is a small set of replayable descents that tells us where the current game gets interesting.

I’ll use the end-session skill to publish this as Reverse Minesweeper Prototype Astra, keeping the project’s gated visibility and checking that the replays appear at the correct points in the log.