- Add KillScore config field (default: 1 point per kill)
- Increment killer's score in executeCombat() when tracking CombatDeaths
- Makes killing enemy bots worth real score, not just foraging
- Keeps kill_score configurable for balance tuning
Co-Authored-By: Claude <noreply@anthropic.com>
Bead-Id: bf-22vc5
Attempting to trigger acb-images-build workflow by pushing to master.
The ai-code-battle-ci-sensor should pick up this push and trigger
the enrichment image build to ronaldraygun/acb-enrichment.
- Updated acb-eventsensor.yml to ensure acb-build trigger is present
- Push will trigger webhook → acb-build → builds all ACB images including enrichment
- Workflow will update declarative-config with real image SHA
## Summary
The combat_turns column migration has been successfully deployed to declarative-config.
All code changes are complete, committed, and pushed.
## Status
- Migration SQL: ✅ Present in acb-schema-init.yml (line 305)
- Rollout annotation: ✅ Bumped to v7
- Pushed to declarative-config: ✅ (commit 503724e)
- Cluster verification: ⏸️ BLOCKED - apexalgo-iad has insufficient CPU
## Blocking Issue
All pods in ai-code-battle namespace are stuck in Pending state due to
'Insufficient cpu' error. Index-builder cannot schedule to verify the fix.
Re-verified that all required components are in place:
- CI workflow exists at starters/csharp/.github/workflows/build.yml
- Plan §5.8 includes acb-starter-csharp at line 986
- Plan §11.2 includes acb-starter-csharp at line 2629
- Directory listing includes csharp/ at line 2565
The task was originally completed in commits 55c594c and 9d4a311.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Add /opt to nsjail bindmounts so Rust toolchain (/opt/rust) is accessible
during sandboxed validation of Rust bots
- Explicitly enable Alpine community repository in Dockerfile to ensure
nsjail package can be installed (nsjail lives in community, not main)
- nsjail integration was already optional (falls back to plain exec if
unavailable), but these changes ensure it actually works when enabled
This addresses bead bf-3f29: nsjail was listed in apk add but /opt wasn't
bindmounted, causing Rust validation to fail when UseNsjail=true.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Verified that the feature implemented in commits 8e0aa5e (engine) and 323c1e8 (viewer) is complete and working:
Engine (engine/turn.go):
- executeCombat emits EventCombatDeath with killers[] array
- Each killer entry includes bot_id, owner, and position
Viewer (web/src/replay-viewer.ts):
- drawCombatEffects() handles combat_death events with killers[]
- Draws solid colored arrows from each killer to the victim
- Includes red explosion flash and X marker for death
- Maintains backward compatibility with old replays (proximity-inference fallback)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Modified executeCombat to emit EventCombatDeath events with a killers
array containing all enemy bots within attack radius of the killed bot.
Each killer entry includes bot_id, owner, and position, matching the
replay schema specification.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The bug fix was already applied in commit 9bcbd56. Updated notes to reflect:
- Fix is already in the codebase (using p.Won instead of p.BotID == m.WinnerID)
- Index builder will automatically regenerate static JSON on next 15m cycle
- No manual intervention required - production deployment will pick up the fix automatically
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The code fix was already applied in commit 6fe778b. This commit
adds documentation noting that the index builder needs to be run
to regenerate static JSON with correct winner badges.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Fix TestBuildNarrativePrompt_Comeback to check for current ELO
instead of old rating (comeback arc shows bottom 25%→top 25%)
- Fix TestDetectRivalryArcs to use 10+ matches (grudge match spec)
instead of only 5 matches
Story arc detection (per §3.7 chronicles):
✓ Comeback bots: recovered from bottom 25% to top 25%
✓ Grudge matches: same pair meets 10+ times
✓ Underdog victories: bottom-10 beats top-10
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Fix /docs/api route to go directly to API docs page instead of redirecting
- Fix docs.ts link to point to /compete/docs/api instead of itself
- Add download button for replay-schema-v1.json in API docs
- Reorder router routes to ensure /docs/api is matched before /docs
The API documentation at /docs/api now correctly shows the OpenAPI-style
endpoint documentation for all static JSON file paths on Pages, R2, and B2,
including the versioned replay format specification.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Evolver writes live.json to R2 every cycle. Observatory page polls and
renders live feed + lineage tree + meta shift chart.
- Added ACB_R2_UPLOAD_ENABLED env var to enable automatic R2 upload during run loop
- CycleState tracks real-time evolution cycle status (generation, phase, candidate, validation, evaluation)
- Export() now includes cycle info when cycleState is provided
- runCycle() integrated with live observatory exports at each phase transition
- exportLiveQuiet() for mid-cycle status updates without verbose logging
- Fixed function signature mismatches for exportLiveQuiet calls
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The win probability sparkline component is now fully integrated:
1. Worker (engine/winprob.go): Monte Carlo rollout computes per-turn win
probabilities, detectCriticalMoments identifies turns where win prob
shifts >15% with template-based descriptions.
2. Replay storage (engine/replay.go): win_prob and critical_moments arrays
stored in replay JSON, written by match worker after each match.
3. Web component (web/src/components/win-prob.ts): WinProbSparkline class
renders the graph with critical moment markers (dashed vertical lines),
click-to-scrub interaction, and current turn indicator.
4. Replay page integration (web/src/pages/replay.ts): initWinProb() sets up
the sparkline with player colors, legend, prev/next critical moment
navigation buttons, and keyboard shortcuts ([/]).
The sparkline displays one line per player with area fill gradient,
percentage labels (0%, 50%, 100%), critical moment diamonds with
delta labels, and updates in real-time as the replay plays.
- Client-side event extraction from replay turn data
- Icon ribbon overlaid on replay viewer timeline
- Click-to-jump to event moment
- Computed events: mass death (5+ bots), spawn wave (3+ spawns),
momentum shift (win prob crosses 50%), critical moment (>15% shift)
- Energy milestone detection (3+ energy collected)
- Hover tooltips with event descriptions
- Updated icons matching plan §14.8 specification
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- All 5 presets implemented: Landscape (1920×1080), Square (1080×1080), Portrait (1080×1920), GIF compact (640×360), GIF square (480×480)
- MP4/WebM export via MediaRecorder API
- Custom GIF encoder with LZW compression and 256-color palette
- Share panel with Twitter, Reddit, Discord integration
- Web Share API support for native sharing
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- WASM game engine (cmd/acb-wasm/) with loadState/step/runMatch API
- Pre-compiled 6 strategy bots to WASM (web/public/wasm/bots/)
- In-browser sandbox with Monaco editor (web/src/pages/sandbox.ts)
- WASM upload mode with interface validation
- Opponent selector (up to 3 opponents for 2-4 player matches)
- Replay viewer integration with fog-of-war toggle and view modes
- All tests pass, go vet clean, npm build succeeds
The reveal() function was trying to return the result of setXP()
which returns void. Fixed by setting XP first, then returning
the threshold value.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Add Clone derive to AppState for axum compatibility
- Import Digest trait from sha2 for hash computation
- Use String instead of &str in response headers for lifetime safety
- Add Position import to grid.rs module
- Make Position Copy for easier cloning
- Replace constant_time_eq with custom hmac_equal function
- Add musl-dev to Dockerfile for Alpine build compatibility
The Rust starter kit now compiles and builds successfully with
cargo check and Docker, matching the requirements from plan §5.3
and §12 (Phase 2).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Wire up the acb-map-evolver to run automatically on a weekly schedule
(Sunday 03:00 UTC by default) from the evolver deployment.
The map evolution ticker:
- Waits until the next scheduled time (weekday:hour:minute UTC)
- Runs acb-map-evolver --once to evolve maps for all player counts
- Repeats every 7 days
The schedule can be configured via ACB_MAP_EVOLUTION_SCHEDULE env var
(format: WEEKDAY:HH:MM, e.g., "0:03:00" for Sunday 03:00 UTC).
Enable via ACB_MAP_EVOLUTION_ENABLED=true or --enable-map-evolution flag.
Per plan §14.6: the weekly map evolution loads engagement scores,
runs MAP-Elites evolution, promotes high-scoring variants, and updates
the active map pool in the database.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per plan §13.3, implements user-requested AI replay commentary with:
- HMAC bot authentication via shared_secret
- Rate limiting: 5 requests/day per bot
- Match validation (exists and completed)
- Idempotency via enrichment_requested_at column
- Enqueues to Valkey for acb-enrichment service
- Returns 202 Accepted with estimated wait time
Also adds:
- AllowN() method to ratelimit package for multi-token checks
- enrichment_requested_at column to matches table (idempotency)
- enrichLtr rate limiter (5/day per bot)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The rating recovery CLI mode (-mode=recalc-ratings) was using
glicko2Tau (0.5) instead of glicko2DefaultSigma (0.06) for the
default sigma value when resetting ratings. This caused the reset
sigma to be ~8x higher than the schema-defined default.
Added glicko2DefaultSigma constant (0.06) and updated ResetAllRatings
and recalcRatings to use it correctly.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Add comprehensive JSON Schema for replay format (v1) as specified in
plan §15.2. This enables third-party tooling to validate and understand
replay files programmatically.
Schema documents:
- Root replay object (format_version, match_id, config, timestamps)
- Match result (winner, reason, scores, stats)
- Player information
- Map data (walls, cores, energy nodes)
- Turn-by-turn state (bots, cores, energy, scores, events)
- Optional win probability curve and critical moments
- Event types (bot_spawned, bot_died, energy_collected, core_captured,
combat_death, collision_death)
- Debug telemetry for bot reasoning visualization
All fields include descriptions, types, constraints, and examples.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Implements plan §3.9 requirement for INV-6 invariant verification.
The test runs thousands of random scenarios across various grid
dimensions (30x30 to 200x200) and multiple random seeds to verify
that no bot, energy, core, or wall position ever has coordinates
outside the valid bounds [0, rows) x [0, cols).
Test coverage:
- Random wall placement with potentially out-of-bounds input
- 1000 random Wrap() calls with positions far outside bounds
- Move() operations from edge and corner positions in all directions
- Neighbors() and VisibleFrom() return value validation
The test uses a manual random-seed loop approach for maximum
control and reproducibility, testing 6 grid sizes × 10 seeds
for comprehensive coverage of the toroidal wrapping invariant.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Add map_engagement_test.go with tests for:
- Win prob dependency in map engagement (lead changes counted)
- Critical moments dependency in engagement score
- Empty/nil replay handling
- Complete ComputeWinProbability + SetWinProbability flow
This confirms the existing implementation already correctly:
- Computes win probability via Monte Carlo rollout (100 iterations)
- Sets win_prob and critical_moments on replay before serialization
- Calculates map engagement score from win_prob_crossings and critical_moments
- Writes engagement score to maps table via UpdateMapEngagement
Task: bf-qps
- Add engine.CalculateMapEngagement() to compute map engagement scores from replay data (win_prob_crossings, critical_moments, map_coverage_pct, closeness, turn_pct)
- Add DBClient.UpdateMapEngagement() to update map engagement using rolling average
- Worker now calculates and writes map engagement scores after each match
- Add test to verify win_prob array is non-empty in produced replays
This implements the win probability Monte Carlo array storage in replay JSON
feature. The engine already called ComputeWinProbability() in MatchRunner.Run(),
so this commit adds the missing map engagement tracking.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
