Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references.
SkillScore91Maintained
Automatic
◆AI Engineer pack
Last verified 8/14/2026
What it changes
Passed 17 of 19 test cases with the skill, and 4 of 19 without it.
Without skill23%
With skill91%
Checks it passed that the model alone missed
Rejects setting defense to 0.65
Rejects the +0.12 parameter jump
Rejects the suggested strategy configuration
and 19 more in the full report
15 fixed5 passed either way2 still failing0 broke
Pass-rate lift
+68 pp
Tokens
+33%
Agent turns
0%
Response time
-48%
Model gemini-3.6-flashMeasured 8/14/2026v120 of 22 cases passed with skill · 19 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 12
Prompt
Calculate the approximate score loss incurred if our agent's energy efficiency degrades by 4% according to our operational scoring formula.
What was checked, without and with the skill
Checked
Without skill
With skill
Identifies the score loss as approximately 1.5 points
Missed
Passed
case-16 · gemini-3.6-flash · 8/14/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md42 linessingle file
Grid Ctf Operational Knowledge
Accumulated knowledge from autocontext strategy evolution.
Operational Lessons
Prescriptive rules derived from what worked and what failed:
Cross-tier parameter transfer is the #1 catastrophic failure mode. Parameters validated at one resource_density tier produce zero scores at different tiers. ALWAYS verify tier before parameter selection.
When resource_density < 0.20 (critical_low), total commitment (aggression + defense) MUST stay ≤ 1.05. Target ≤ 1.00 for a 5% safety buffer. Exceeding by even 10% causes energy starvation and zero scores.
When resource_density is moderate (0.40–0.60), total commitment ceiling is 1.20. Target 4–6% buffer (≤ 1.15). A 16% buffer wastes capacity.
Defense must stay in [0.45, 0.55]. Below 0.45 risks base loss; above 0.55 starves capture progress.
Aggression must be ≥ 0.48 to generate meaningful capture progress. Zero capture = zero score.
The scoring formula (score ≈ capture + (efficiency - 0.5) × 0.39) makes efficiency extremely valuable. Losing 4% efficiency costs ~1.5 score points.
Balanced strategy (agg=0.58, def=0.57, pb=0.55) achieved 0.7615 at density≈0.437, bias≈0.51 — moderate balanced parameters outperform extremes within the correct tier.
Conservative baseline (agg=0.50, def=0.50, pb=0.48) scored 0.7198 at density≈0.147, bias≈0.648 — proven viable in critical_low tier.
Over-aggression (agg ≥ 0.65) without proportional defense (≥ 0.52) causes defender survival drops and energy efficiency decline. Optimal moderate-tier aggression is [0.56, 0.60].
Generation 3 rollback (agg=0.67, def=0.52, pb=0.60, score=0.7486) and Gen 2 rollback (agg=0.62, def=0.52, pb=0.58, score=0.7369) both confirm over-commitment underperforms.
Perfect defender survival (1.00) signals defensive over-allocation. Optimal target is 0.95–0.99, freeing resources for capture.
Incremental changes (±0.02 to ±0.05) from a proven baseline within the same resource tier are the only validated safe optimization method. Large jumps (±0.09+) led to rollbacks.
After a zero score, RESET to the proven baseline for the current tier. Do NOT incrementally tweak failed parameters.
Path_bias in low-resource environments: cap at 0.50. Concentrated force projection is energy-expensive.
Path_bias for balanced enemy (bias ≤ 0.55): use [0.50, 0.55]. For asymmetric enemy (bias > 0.6): use [0.45, 0.50].
Energy efficiency of 0.90 at commitment=1.00 in critical_low confirms the ceiling is accurate; incremental increases to 1.01–1.03 are viable.
All validation tools (config_constants, energy_budget_validator, stability_analyzer, threat_assessor) MUST be run before deployment. Risk > 0.65 or stability < 0.45 predicts poor performance.
Recovery priority after zero score: (1) non-zero capture, (2) defender survival, (3) energy sustainability, (4) optimize capture progress.
The observation narrative is the authoritative source for environment data. Always read resource_density and enemy_spawn_bias from the actual observation state.
When conditions exactly match a proven baseline, deploy it directly rather than converging incrementally.
When aggression exceeds 0.7 without proportional defense, win rate drops.
Defensive anchor above 0.5 stabilizes Elo across generations.
Generation 2 ROLLBACK after 2 retries (score=0.7369, delta=-0.0461, threshold=0.005). Strategy: {"aggression": 0.62, "defense": 0.52, "path_bias": 0.58}. Narrative: Capture phase ended with progress 0.61, defender survival 0.96, and energy efficiency 0.87.. Avoid this approach.
Generation 3 ROLLBACK after 2 retries (score=0.7339, delta=-0.0491, threshold=0.005). Strategy: {"aggression": 0.62, "defense": 0.52, "path_bias": 0.58}. Narrative: Capture phase ended with progress 0.61, defender survival 0.96, and energy efficiency 0.87.. Avoid this approach.
Generation 4 ROLLBACK after 2 retries (score=0.7669, delta=-0.0161, threshold=0.005). Strategy: {"aggression": 0.62, "defense": 0.52, "path_bias": 0.58}. Narrative: Capture phase ended with progress 0.66, defender survival 0.96, and energy efficiency 0.87.. Avoid this approach.
Generation 5 ROLLBACK after 2 retries (score=0.7396, delta=-0.0434, threshold=0.005). Strategy: {"aggression": 0.62, "defense": 0.52, "path_bias": 0.58}. Narrative: Capture phase ended with progress 0.62, defender survival 0.96, and energy efficiency 0.87.. Avoid this approach.
Bundled Resources
Strategy playbook: See playbook.md for the current consolidated strategy guide (Strategy Updates, Prompt Optimizations, Next Generation Checklist)