Escape the Dungeon · Emergent
escape-the-dungeon-emergent/v3
Composite score
0.000
Rate-limited run · excluded from cross-round comparisons
This run hit an account-level rate limit before completing. The data captured here reflects partial progress only — it is not included in the freedom-hypothesis scatter, the Results card grid, or any aggregate comparison on the site (decision gad-63). Preserved here as a data point for planning-differential analysis and honest documentation.
Details: Hit account-level rate limit at tool_uses=40 (lowest of three conditions). Shared rate bucket with parallel GAD + Bare runs.
Full round 4 partial-results finding →Human review note
RATE LIMITED before main.ts entry point was written. Build fails objectively at link time. Architectural signal: emergent agent wrote 6 modular files before the integration layer, matching the state-composition inherited skill's pattern. Interesting emergence data point despite incomplete run. DO NOT include in cross-round comparisons against completed runs.
Dimension scores
Where the composite came from
Each dimension is scored 0.0 – 1.0 and combined using the weights in evals/escape-the-dungeon-emergent/gad.json. Human review dominates on purpose — process metrics alone can't rescue a broken run.
| Dimension | Score | Bar |
|---|---|---|
| Requirement coverage | 0.042 | |
| Planning quality | 0.050 | |
| Per-task discipline | 0.000 | |
| Time efficiency | 0.533 |
Composite formula
How 0.000 was calculated
The composite score is a weighted sum of the dimensions above. Weights come from evals/escape-the-dungeon-emergent/gad.json. Contribution = score × weight; dimensions sorted by contribution so you can see what actually moved the needle.
| Dimension | Weight | Score | Contribution |
|---|---|---|---|
| time_efficiency | 0.05 | 0.533 | 0.0267(76%) |
| requirement_coverage | 0.20 | 0.042 | 0.0084(24%) |
| implementation_quality | 0.15 | 0.000 | 0.0000(0%) |
| skill_reuse | 0.15 | 0.000 | 0.0000(0%) |
| workflow_quality | 0.10 | 0.000 | 0.0000(0%) |
| iteration_evidence | 0.05 | 0.000 | 0.0000(0%) |
| human_review | 0.30 | 0.000 | 0.0000(0%) |
| Weighted sum | 1.00 | 0.0351 |
Note: The weighted sum above (0.0351) doesn't exactly match the stored composite (0.0000). The difference is usually the v3 low-score cap (composite < 0.20 → 0.40, composite < 0.10 → 0.25) or a run with an older scoring pass.
Skill accuracy breakdown
Did the agent invoke the right skills at the right moments?
Skill accuracy data isn't relevant for this run (no expected trigger set).
What the agent built for itself
Emergent workflow artifacts
Bare and emergent runs don't have a framework giving them structure — they author their own methodology on the fly. These are the files the agent wrote into its own game/.planning/during this run. When a file appears here that isn't in the inherited bootstrap set, the agent invented it.
Skills written(7)
content-pack-loading.md1.3 KBcreate-skill.md4.9 KBfind-sprites.md5.9 KBgame-loop-verification.md1.7 KBkaplay-scene-pattern.md1.6 KBprevious-workflow.md2.9 KBstate-composition.md1.1 KB
Gate report
Requirement coverage
Reviewer notes on gates
BUILD FAILS — main.ts entry point missing. index.html references /src/main.ts but only 6 supporting modules exist. Rollup error: 'failed to resolve import /src/main.ts'. All gates NOT MET because the build itself fails.
Process metrics