agentleFS
LazyIsEfficientLazyIsEfficient/agentic-os/.claude/skills/game-balancer/SKILL.md

game-balancer

Skill17 starsChanged 24 days agoMITView on GitHub

Use when tuning the numbers in a game — economy curves, progression rates, difficulty pacing, drop tables, win/loss odds, time-to-X targets, and currency velocities. Triggers on "balance pass", "tune the economy", "progression curve", "difficulty curve", "drop rate", "XP curve", "TTK", "time-to-content", "balance the economy", "spreadsheet model", "economy sim", or a system spec from game-systems-designer with `TBD` placeholders. Produces an economy spreadsheet, balance pass plan, sim outputs, and per-system balance tables. Stops at the numbers — does not change system rules or pricing. For systems design see game-systems-designer; for store catalog see iap-manager.

  • Deletes or force-pushes

The file

We split this skill into the rules it gives an agent

7 rules. About 0 are common, 6 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If the systems themselves are wrong (no amount of tuning will fix them), stop and route to game-systems-designer.

    line 37
  2. Distinctive:

    Identify what kind of balance work this is: Use references/balance-pass-types.md to pick: economy balance, progression balance, difficulty …

    line 43
  3. Very specific:

    Build the spreadsheet model: Use the appropriate skeleton from assets/.

    line 45
  4. Distinctive:

    Sandbox / simulate: Run the model against representative player profiles (whale / dolphin / minnow / non-spender, casual / median / …

    line 51
  5. Distinctive:

    Sources and sinks must balance over the player's lifetime: Not over a session. Not over a week.

    line 65
  6. Distinctive:

    Numbers are a starting point, not a final answer: Every number ships behind a *measurement plan*: what telemetry validates it post-launch …

    line 67
  7. Distinctive:

    Do not change system rules: If the only way to balance a system is to change its rules, hand it back to game-systems-designer rather than …

    line 69

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Game Balancer

Your job is to fill in the numbers in a system spec — economy rates, progression curves, difficulty pacing, drop tables, currency velocities — and produce the spreadsheet model, simulation, and balance-pass plan that proves the numbers hold up. You do not change system rules (that's game-systems-designer) or prices in dollars (that's iap-manager).

The two failure modes:

  • Vibes balance. "It feels about right." No spreadsheet, no simulation, no monitoring plan. The economy collapses 30 days into live ops and nobody knows why.
  • Spreadsheet trance. Beautiful model, plays terribly. The numbers prove the design is balanced; the player feels nothing. Balance is not optimization; it is tuning systems to produce the dynamics the design promised.

The right stance: model the system in a spreadsheet so you can reason about it; playtest the model in the engine so you can see what it feels like; instrument live so you can tell when it drifts.

When this skill applies

  • A system spec from game-systems-designer arrives with <TBD by game-balancer> placeholders.
  • The team needs a curve (XP, levels, gear, season pass) but doesn't have a shape yet.
  • Live data shows an economy is drifting and needs a re-tune.
  • A new content drop changes the balance and needs to be re-validated against existing numbers.
  • A monetization decision requires the economy to be re-tuned to fit a target ARPDAU / spend pattern.

If the systems themselves are wrong (no amount of tuning will fix them), stop and route to game-systems-designer.

Procedure

  1. Read the system spec(s). Identify the variables marked <TBD> and the design intent attached to each ("should make the player choose X about 60% of the time", "session length impact ≈ +20%").
  2. Identify what kind of balance work this is. Use references/balance-pass-types.md to pick: economy balance, progression balance, difficulty balance, content balance, or live-game re-tune.
  3. Build the spreadsheet model. Use the appropriate skeleton from assets/. The model should reproduce the system enough to predict behavior under varying inputs. See references/economy-design.md for sources / sinks / faucets / drains.
  4. Pick the curve shape(s) from references/progression-math.md. Linear, exponential, log, stepped, capped, resetting. The shape decision precedes the constants.
  5. Solve for the constants that hit the design intent + any KPI floors (session length target, time-to-content X, win-rate target, ARPDAU target if provided). See references/curve-fitting.md.
  6. Sandbox / simulate. Run the model against representative player profiles (whale / dolphin / minnow / non-spender, casual / median / hardcore). Identify break points where the system collapses (whale outpaces content; minnow bounces; mid-funnel grinds out). See references/simulation-and-playtesting.md.
  7. Write the balance pass plan. Fill assets/balance-pass-checklist.md. What to validate in playtest, what telemetry to capture in soft launch, what KPIs to gate on.
  8. Hand off the tuned numbers. Each system spec gets a balance table appended. Each curve gets a chart. Each KPI gets a target floor and an alert threshold for live ops.
  9. For live games, propose the re-tune via references/live-rebalancing.md: how to roll out, what to watch, what to roll back if a guardrail trips.

Universal rules

  • Capture design intent before solving. Without intent, you're tuning to a number that the team will overrule when it "feels wrong." With intent, you're tuning to a behavior the team agreed on.
  • Pick the shape before the constants. Linear vs exponential vs stepped vs capped is a qualitative decision; pick it first, then solve.
  • Always model multiple player profiles. A balance that works for the median player but breaks for whales / minnows / non-spenders has shipped a broken economy.
  • Always model time. Sessions per day × days per week × weeks per arc. Most balance failures are temporal failures — the rate at which players consume content is wrong.
  • Sources and sinks must balance over the player's lifetime. Not over a session. Not over a week. Over the intended player lifetime. Excess sources → inflation (whales bored, world devalued). Excess sinks → frustration (players quit before the next reward).
  • Spreadsheet first, engine second. Tuning live in the engine without a model is how you tune one curve and break three others.
  • Numbers are a starting point, not a final answer. Every number ships behind a measurement plan: what telemetry validates it post-launch and what triggers a re-tune.
  • Do not set prices. Prices in dollars are the responsibility of iap-manager (catalog). You set the exchange rates inside the game; it sets the exchange rate to real money.
  • Do not change system rules. If the only way to balance a system is to change its rules, hand it back to game-systems-designer rather than silently rewriting the spec.
  • Symmetry is not balance. Symmetric games (PvP) need fairness, not equality. Asymmetric balance is harder; it is also usually more interesting.

References

  • references/balance-pass-types.md — what kind of balance pass this is (economy, progression, difficulty, content, live re-tune)
  • references/economy-design.md — sources, sinks, faucets, drains; soft vs hard currency; multi-currency hygiene
  • references/progression-math.md — curve shapes (linear, exponential, log, stepped, capped, resetting); when to use which
  • references/curve-fitting.md — solving constants given KPI floors; common parameterizations
  • references/difficulty-and-pacing.md — DDA, rubber-banding, flow-channel design, encounter difficulty
  • references/simulation-and-playtesting.md — modeling player profiles, monte carlo sims, what playtest is for
  • references/pvp-balance.md — symmetric vs asymmetric, matchmaking interactions, meta health, patch cadence
  • references/live-rebalancing.md — how to retune a live game without burning trust
  • references/balance-anti-patterns.md — power creep, dominant-strategy collapse, treadmill economies, broken whale dynamics

Assets

  • assets/economy-spreadsheet-skeleton.md — recommended spreadsheet layout (sources, sinks, currencies, conversions, player profiles, time-windows)
  • assets/progression-curve-skeleton.md — XP / level / tier / gear curve scaffold
  • assets/balance-pass-checklist.md — what to validate before declaring a balance pass complete
  • assets/balance-table-template.md — per-system filled balance table appended to the system spec

Related skills

  • game-systems-designer — produces the system specs this skill numbers; receives back balance tables
  • iap-manager — needs currency velocities and grind-time targets to size store SKUs and bundles
  • godot-engineer — implements tunable parameters as data, not magic numbers; ships the telemetry that validates the model post-launch
  • growth-engine (marketing pack) — runs A/B tests on balance variants once the game is live
  • content-ops (marketing pack) — expert-panel scoring of the balance plan before live launch

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.