reikhelm/combat

combat — a combined-pool combat system

A standalone experiment in the reikhelm sandbox. It borrows the repo's conventions (pure deterministic core, serde config, ChaCha8 RNG, headless-first, front-ends as consumers) but has no dependency on reikhelm-core. Its own Cargo workspace; one experiment, one isolated build graph.

Design spec: ../.dev/2026-06-02-combat-system/spec/design.md.

The idea

A real-time (tick-based) combat system in the EverQuest / Daggerfall lineage — auto-attack + skills + magic — built on one deliberately unusual idea: a single combined resource pool, Vigor, that replaces separate HP / mana / stamina. Everything you do and everything done to you draws on the same bar. Offense literally spends your survival.

It is a deterministic simulation first, a playable game second. "Feel" (fight duration, difficulty curve) is not eyeballed — it is measured across tens of thousands of simulated fights and tuned to target shapes.

The spine — two axes and a squeeze

Every combatant has one pool with three derived quantities:

Term	Meaning
Vigor (fill)	Current value. Also your absorb capacity for the stagger cascade.
Cap (ceiling)	max_vigor − fatigue. The highest fill can regen to right now.
Fatigue	Fight-scoped ceiling loss. Every point that leaves the pool adds some.

Every combat verb is a point on two axes — cost to fill and cost to ceiling. Offense costs both; auto-attack/defense cost a little ceiling; recovery is the inverse. You don't lose by emptying fill once — you lose because attrition grinds your ceiling down, a lower ceiling caps a lower fill, and a low fill lets the next hit overrun your capacity and trigger the stagger cascade (absorbed → dazed → unconscious → dead). Everything points at the same death.

Layout

combat/
  combat-core/   pure deterministic engine — the single source of truth
  combat-sim/    batch harness: load config, run sweeps, export CSV/JSON
  combat-wasm/   wasm-bindgen bridge — drive the engine from a browser
  combat-web/    a playable real-time battler (the front-end consumer, §14)
  analysis/      Python (pandas/matplotlib) over the exported data
  configs/       the tunable "table" (default.json)
  tools/         portraits.py — AI monster art via the LAN Z-Image stack

combat-core has no I/O, no rendering, no real-time. A fight is a pure function of (rules, seed, actors, controllers), reproducible bit-for-bit. The sim and (later) any visual front-end are just consumers.

Inside combat-core

Module	Role
fixed	integer fixed-point math — the only place magnitudes round (banker's rounding)
rng	vendored order-independent, version-stable deterministic RNG (+ integer chance_bp)
con	the level-delta → multiplier con curve
mitigation	the ordered "how much lands" pipeline (armor → con → defense → stance → buff)
effect / ability	the composable effect library + unified ability model (skills == spells)
actor	the symmetric combatant: combined pool, stagger cascade, statuses, competency, loadout
controller	pluggable intent — SwingOnCooldown, ScriptedPlayer { skill }, HoldAndPunish
config	the full tunable surface, compiled to an integer-only Rules
event	the structured event stream + metrics recorder
engine	the fixed-tick loop + canonical within-tick resolution order

Determinism is load-bearing

"Change one knob, observe the isolated delta" only holds if unrelated rolls don't reshuffle and arithmetic doesn't drift. Two guarantees make it true:

• Order-independent RNG forking — every roll is keyed root.fork("actor{id}:{kind}:tick{t}"), so adding an effect to one ability never shifts another actor's or tick's stream. (Vendored verbatim; a hand-rolled splitmix64 + FNV-1a mix, not DefaultHasher.)
• Integer fixed-point magnitudes — vigor/cap/fatigue and every formula are integer milli-units with one documented rounding rule; f64 is confined to config parsing. Bit-exact across compilers / opt levels / machines.

The within-tick order is itself an invariant (reordering changes outcomes), and decisions are simultaneous within a tick — all controllers read the same start-of-tick snapshot, then commit in id order — so a perfect mirror match is fair.

Build & run

# (cargo on PATH: export PATH="$HOME/.cargo/bin:$PATH")
cd combat
cargo test                     # unit + determinism integration tests
cargo build --release

# Run the sweeps → out/{fights.csv, summary.csv, sample_fight.json}
./target/release/combat-sim --config configs/default.json --out out --seeds 300

# Analyze → plots/*.png + a text read-out
cd analysis
python3 -m venv .venv && ./.venv/bin/pip install -r requirements.txt
./.venv/bin/python analyze.py --data ../out --plots plots

combat-sim runs two sweeps:

• con — player vs. a fixed competent benchmark across level delta × player policy (auto-only, skill 20/50/80/100). The auto-only policy is the §12 anti-turtle guardrail.
• archetypes — a skilled player against golem / rat / battlemage.

Play it (browser)

The same engine, real-time, with AI-rendered monster portraits — pick a foe and fight. See combat-web/README.md for details.

wasm-pack build combat-wasm --dev --target web \
  --out-dir ../combat-web/pkg --out-name combat_wasm   # build the bridge → pkg/
python3 -m http.server 8013 --directory .              # serve combat/ as root
open http://127.0.0.1:8013/combat-web/

What the data shows (default config)

With the shipped configs/default.json (these numbers are tuned placeholders — the whole point is that they're a table you edit):

• Duration tent — fights are trivially short at the con extremes (~1–5s) and longest/most-contested at even con (~30s). The intended "trivial → satisfying → lethal" shape.
• Skill matters — at even con, win rate climbs monotonically with policy: auto-only ~1% → skill20 10% → skill50 24% → skill80 29% → skill100 34%.
• Anti-turtle guardrail is green — in the contested band the full kit beats auto-attack-only decisively (≈29% vs ≈1% at even con). Spending on the interesting verbs is worth it, which is the whole thesis.
• Mirror matches are fair — even-con skill-vs-skill is ≈ symmetric.

Getting there was the sim working as designed: the first default numbers produced a one-tick cliff and 82% mutual-KO draws, and revealed the §15 deepest risk live — a poke skill that was strictly worse than the free auto-attack, so auto-only out-won the full kit. Tuning the table (con curve, competency coupling, ability efficiency, damage variance) moved it to the curves above. That loop — sim → measure → tune → re-sim — is the deliverable.

Tuning

Everything balance-relevant lives in configs/default.json. Editing it is the primary balancing act; only structural rule changes need a recompile. The levers the feel rides on (spec §15): the con curve shape, the regen vs. drain vs. spend rates (co-tuned or even-con fights are un-loseable or stagger-locked), and keeping abilities ahead of auto-attack on damage-per-resource or utility.