Reverse Minesweeper Prototype Astra

Summary

Reverse Minesweeper Prototype Astra recovers a Linux-era HTML mining prototype and controller-research conversation, then develops an independent Codex implementation in Anchor 3 alongside Fable's separate work. The session builds and iterates a human movement lab, adds ropes, reconnects robot excavation to human descent and camp progression, and adds wall sliding, wall jumping, look-down inspection, rope-placement previews, and damage feedback. The Lua port retains the prototype's deterministic mine layout and energy economy while replacing human movement with the accepted kinematic controller. Verification combines pure Lua simulation, JavaScript/Lua parity checks, hidden Anchor input runs, captures, and preserved presentation replays. The owner repeatedly approves the playable results, corrects artifact-preservation guidelines for Anchor games, and requests public publication of this session while the earlier log remains gated.

Recovery and independent workspace:

• Located the project in Z:/2025-2026/code/a327ex-linux-2026-09/sketches/2026-09-03-reverse-minesweeper/; recovered it only into reverse-minesweeper-codex/, visibly separate from Fable's implementation.
• Recovered original sessions 2b679f84-b3cd-4593-92d0-ec0e3edeed99 (idea exploration and HTML prototypes) and a98a36df-5875-45cc-ba30-a67bf1bff929 (human-controller research), with raw JSONL, readable text extractions, available research subagent records, and reference sources.
• Preserved four HTML deliveries: lava version, countdown/nugget version, robot/human version, and revised rope placement/human quick-start version. The latest snapshot matched the recovered index.html.
• Copied 42 files and verified SHA-256 equality. Archived sources and Fable's work stayed untouched. The research had proposed a controller lab, but no Anchor port had been implemented.

Controller-first development:

• Proposed testing the fragile human's navigation before rebuilding the full game. Robot explosions produce stepped crater walls; ledges were therefore the first descent mechanic to establish.
• Built a custom kinematic controller in tile units using plain Lua tables and swept X/Y collision at Anchor's 120 Hz update. controller.lua is independent of rendering and runs unchanged in simulation tests.
• Added walk/run/crawl, variable jump, coyote time and jump buffering, explicit toward-wall corner catches, hang/drop/jump-away, lowering over an edge, and climbing onto it. Lowering and climbing validate both movement segments.
• Baseline movement: 5/9/1.4 tiles/s walk/run/crawl, 0.625 by 0.875 tile body, 20 tiles/s terminal fall speed. Measured jump heights: 0.765 tiles tapped and 1.606 held. These are Spelunky-inspired values, not an exact reproduction.
• Added ledge, shaft, and fixed-crater practice maps, reset/room shortcuts, diagnostic readouts, and a camera. Practice applies no HP loss. Owner verdict: "This is really good."

Artifact and folder corrections:

• Initially delivered the lab under rounds/01-controller-lab/ because the iterate skill applied frozen artifact rules too broadly.
• Owner clarified that Anchor replays preserve what ran and are woven into logs; Anchor source folders do not need a new frozen copy for every handover. HTML and other standalone artifacts still do.
• Updated the shared iterate skill, Codex adapter, global guidelines, and project instructions. Both skills validated. Kept the explicitly requested separation between Codex and Fable.
• Moved the working application to reverse-minesweeper-codex/lab/, removed the empty rounds directory, updated references, and checked startup. Preserved replay history.
• Explained the large bundled DLLs: roughly 112 MB of FFmpeg libraries from the standard Anchor runtime. Anchor has a novideo build, but this session did not switch runtimes.

Ropes:

• Added ropes.lua: four ropes per room/descent, eight-tile maximum length, upward hook flight followed by unrolling, and downward placement from an edge or hang.
• Controls: E throws up; Down+E places down; Up catches; Up/Down climbs; Direction+Space jumps off; Down+Space drops. Failed placement consumes nothing. Placing from a hang does not release the human.
• Rope climbing is 3 tiles/s, with a short recatch lockout. Swept catches handle fast falls; terrain clips the rope. A fourth practice room exposes these interactions.
• Preserved accepted basic locomotion. Owner verdict: "This is really good too."

Integrated robot/human/camp game:

• Added pure simulation in game.lua, presentation/input mapping in game-view.lua, and a project-level run.bat. The integrated game opens by default; L switches to and from the same practice lab without draining game energy.
• Ported sparse infinite terrain, exposed Minesweeper numbers, target reach, flags, full-time mine digging, direct and delayed chain explosions, falling nuggets, energy rewards, shutdown, human descent, death, healing, and repeated camps.
• Retained 8% mines and 5% treasure; blast radii 2.5 direct and 1.6 chained; dig time 0.45 seconds plus 0.006 per depth tile capped at 1.4; reach four tiles horizontally and three vertically.
• Retained 45 seconds energy, +5 seconds per new ten-tile depth line, +2 per collected nugget, four HP, four ropes, and one HP camp healing. Damage starts at six tiles, adding one damage per additional four tiles.
• Exhausted robots finish pending chains and settle on actual support before handoff. Camp requires a living, grounded human arrival. Damage is applied before camp healing so a fatal landing cannot be rescued by proximity to the robot.
• Accounted for the human's larger hitbox when choosing a camp position. Allowed rope throws into surface sky. R retries the same seed; N creates a new seed.
• Fixed startup camera drift found by the mouse-input test. Checked 480 exact original-JavaScript/Lua hash samples and 18 original balance values. Two complete robot/human/camp cycles passed through actual engine input.

Walls and descent information:

• Owner approved better descent information and explicitly requested wall sliding/jumping as necessary, overriding the earlier suggestion to defer walls.
• Holding toward a wall brakes into a 3.5 tiles/s slide. Space jumps away with a 7.5 tiles/s sideways push and 0.16 seconds before normal steering resumes. Basic ground jump and rope tuning remain intact.
• Fast contact brakes progressively; accumulated fall danger clears only once controlled slide speed is reached. Sustained wall descent is safe without spending ropes. A brief terminal-speed touch is not an instant reset.
• C looks down from ground, hang, or rope without dropping or climbing. Up to 5.2 extra tiles of view; a dashed blue downward rope preview shares validation and terrain limits with actual placement. Down also requests the preview while keeping its movement action.
• LAND NOW reports safe, HP cost, or lethal using actual damage tiers and remaining health. It describes immediate landing cost, not a prediction of the next floor. Added a fifth wall practice room.
• Final verification: 129 named simulation checks; prior 84,000 movement/rope stress ticks plus 18,000 wall-room ticks; generated-terrain runs; hidden-engine bindings and captures; clean static analysis. Presentation replays preserved. No engine implementation or Fable project edits.

Next direction discussed, not implemented:

• Owner's final playable verdict: "This is really good."
• Suggested preserving robot-generated caves for a human-descent retry mode, so routes can be compared without repeating excavation. This remains a proposal.
• Wall sliding makes continuous walls a free safe route. The next question is where broken wall continuity, crossings, and rope decisions make descent interesting, and whether robot digging choices meaningfully affect that route.
• Avoid weakening walls preemptively. Examine ordinary runs before changing terrain incentives or adding enemies, shops, or visual polish. Side rewards were discussed as a possible incentive only if actual play establishes the need.
• Publication title: Reverse Minesweeper Prototype Astra. Owner explicitly authorized this log to be public; the earlier log remains gated. Replay weaving was explicitly requested.

There is a Reverse Minesweeper prototype project containing two transcripts, either on the old Windows or Linux machine. The first transcript was an exploration of the idea with a few HTML prototypes, the second was the start of a research into the human character's movement. Please recover those, we'll start working on this project here, however I will work on it simultaneously with both you and Fable, so when you create a folder for it make sure to name it visibly as yours, and if you do decide to copy files from the old project also copy them to your folder only to not contaminate any other work.

GPT-6 Astra medium

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.

Open artifact

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.

How do you suggest we proceed?

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?

Seems like a good plan, go ahead.

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.

**Building controller-lab round**

**Setting viewport layout**

**Building controller-lab round**

**Setting viewport layout**

**Implementing ledge traversal**

**Implementing Lua baseline tests**

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.

**Adjusting ceiling clearance**

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.

**Testing possible crater catch**

**Adding coyote tests**

**Testing possible crater catch**

**Adding coyote tests**

**Preparing crater traversal**

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.

**Preparing runtime manifest script**

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.

Haven't tested it yet, but an important correction, for Anchor games you don't need to do rounds like with HTML artifacts because all Anchor games generate replays that get added to the log afterwards. The care with HTML artifacts happens because otherwise they'd get overwritten and the reader wouldn't be able to see them, but with Anchor games this isn't an issue. Change whatever guidelines made you decide to do it like this for an Anchor game as well.

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.

Can you also fix the folder? Instead of doing rounds/01 do it kinda like Fable where it's just lab/.

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.

This is really good, what would you do next?

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.

Go ahead.

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.

**Implementing lab_tick rope updates**

**Implementing rope catch sweep**

**Implementing lab_tick rope updates**

**Implementing rope catch sweep**

**Tuning rope climbing checks**

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.

**Preparing fresh engine captures**

**Testing rope climb input**

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.

**Checking policy engine ownership**

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.

This is really good too. What would you do next?

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?

Go ahead.

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.

**Integrating dynamic world solids**

**Tuning camp spawn dimensions**

**Tuning camp spawn dimensions**

**Fixing floor detection**

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.

**Adding camp placement checks**

**Updating lab test resets**

**Removing redundant check**

**Checking hash parity**

**Starting clean simulation run**

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.

**Writing hash parity tests**

**Checking target alignment**

**Clipping depth labels**

**Monitoring running tests**

**Adding mine impact-check script**

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.

**Verifying test totals**

**Checking final anchor**

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.

Haven't tested yet, but why do you have some .dlls that are very big in the folder, are those from Anchor or something else?

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.

Great, the prototype so far seems good, what would you do next?

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.

Seems good. However I'd also like wall sliding and wall jumping to be added, these turn out to be necessary.

**Reviewing current context**

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.

**Refactoring rope preview**

**Refactoring rope preview**

**Refining lookdown controls**

**Refactoring rope preview**

**Refining lookdown controls**

**Adding fall-risk feedback**

**Adding wall-contact detection**

**Tuning low-speed gravity**

**Checking wall-slide transitions**

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.

**Planning descent tests**

**Refining rope preview feedback**

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.

**Checking contact reset logic**

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.

This is really good, what would you do next?

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.

Let's end this session here, name it Reverse Minesweeper Prototype Astra. Make sure the replays are weaved correctly into the log.

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.

You can make this particular log public, the other log remains gated.