game-design-leaderboard-builder — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited game-design-leaderboard-builder (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
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Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Design a complete leaderboard system grounded in the game's context, player base, economy, and production constraints.
This skill asks eight intake questions before producing anything. Each question has a suggested default — accept it if you don't know the answer yet, or override it with specifics. The more precise the inputs, the more precisely calibrated the design.
A leaderboard is not a feature you bolt on — it is a metagame you design. Its structure determines how many goals players have, how long they stay engaged, and whether the competition feels fair and meaningful or frustrating and pointless. Get the inputs right before designing anything.
Present all eight questions. For each, offer the default assumption if the user does not provide an answer. Accept partial answers — use defaults for whatever is missing and flag those assumptions clearly in the output.
What metric are players competing on? How quickly can it change — can it shift meaningfully in a single play session, or does it accumulate slowly over days or weeks?
Why this matters: A slow-changing or lifetime-accumulated score produces a stagnant leaderboard regardless of how well the rest of the structure is designed. This is the most critical input.
Default assumption if unknown:
Session-based score that can change meaningfully in a single play session. Score resets at the start of each competition round. Assumed moderate velocity — a committed player can move 10-20 positions per active session.
How many players will be actively competing in a single competition window? (Active = played at least once during the round.)
Why this matters: Pool size determines whether you need group segmentation, how many tiers the league ladder needs, and the pyramid shape of the whole structure.
Default assumption if unknown:
10,000 active competing players — a mid-size feature launch or a moderately successful mobile F2P title at a stable point in its lifecycle.
How long does one competition round last before scores reset and standings are settled?
Why this matters: Round length affects urgency, the scoring velocity requirement, the reset-acceptance by players, and how often reward brackets pay out.
Default assumption if unknown:
7 days (weekly). The most tested and widely accepted cadence for mobile/F2P. Long enough to feel like a meaningful campaign; short enough to feel urgent.
Genre, platform, and typical session length. What kind of game is this leaderboard sitting inside?
Why this matters: Session length affects what group size feels natural. Genre affects what competition feels fair. Platform affects notification and re-engagement patterns tied to the leaderboard cadence.
Default assumption if unknown:
Mobile F2P. Sessions of 5–15 minutes. Daily play expected from active players. Core loop repeatable within a single session.
What prizes are available to distribute to bracket winners at the end of each round? Do you have meaningful differentiation between brackets, or is the reward pool limited?
Why this matters: The reward bracket structure only works if the rewards are worth competing for. The design of brackets depends entirely on what you can actually offer. If rewards are weak, the design must lean harder on intrinsic prestige.
Default assumption if unknown:
Soft currency (moderate amount) for all rewarded positions. Exclusive cosmetic or rare content for top bracket. Diminishing but meaningful value down through mid-tier brackets. Consolation prize (small soft currency) for the bottom reward bracket. Nothing for players outside all brackets.
Roughly, what is the spread from casual to competitive players? Are there significant spenders (whales) who could dominate groups if mixed with free players?
Why this matters: Random group composition with a large spending gap produces whale-dominated leaderboards where most players have no real chance. Knowing the mix determines whether and how to segment groups.
Default assumption if unknown:
Standard F2P bell curve: approximately 60% casual (low engagement, low or no spend), 30% mid-tier (regular engagement, occasional spend), 10% competitive or paying (high engagement, meaningful spend). Meaningful whale population present but small.
How much development complexity can this leaderboard system absorb? A full league ladder with promotion, demotion, and group management is significantly more complex than a flat group-based leaderboard.
Why this matters: The right design for a small team with a constrained roadmap is different from the right design for a live service with engineering capacity to build a multi-tier system.
Options:
Default assumption if unknown:
Standard — league ladder with 4–5 tiers recommended. This is the minimum structure that generates full goal density across all time horizons. If Simple is required, flag the goal density tradeoff explicitly.
When players are assigned to competition groups, should assignment be random or based on some form of matching (progression tier, activity level, spend band)?
Why this matters: Random grouping is simple but can produce consistently unfair matchups — new players against veterans, free players against heavy spenders. Matching increases fairness and competitive integrity but adds system complexity.
Default assumption if unknown:
Broad-band progress matching — players grouped with others at a similar progression stage or activity level. Not strict skill matching (too complex), but enough to prevent total mismatches. Groups refreshed each round.
After receiving answers (or accepting defaults), briefly restate what was collected:
Confirmed inputs: - Score: [summary] - Player pool: [number] - Round length: [duration] - Game context: [summary] - Rewards: [summary] - Player mix: [summary] - Complexity budget: [Simple / Standard / Full] - Group composition: [Random / Matched] - Assumptions used: [list any defaults applied]
Ask: "Does this look right? Say go to proceed, or correct anything first."
Once inputs are confirmed, produce the full design across these seven components.
Design the group size and total number of groups.
Calculate:
ceil(player_pool / group_size)Explain why this group size is recommended for this specific context.
Design the pyramid structure.
For Standard and Full complexity:
Calculate the pyramid:
round_size / promotions_per_group| League | Name suggestion | Groups | Players | Notes |
|---|---|---|---|---|
| 5 (Top) | ... | 1 | [group_size] | Top league — 1 group only |
| 4 | ... | [n] | [n × group_size] | |
| 3 | ... | [n] | [n × group_size] | |
| 2 | ... | [n] | [n × group_size] | |
| 1 (Entry) | ... | [n] | [n × group_size] |
Suggest league names appropriate to the game's theme. Flag the naming scalability problem if more leagues are likely to be added in future.
For Simple complexity:
Specify the rules for moving between leagues.
Design:
Example values for a group of 100:
Flag sandbagging risk if top reward bracket aligns with promotion threshold — this incentivizes intentional underperformance before a round ends. Recommend mitigations if risk is present.
Define the full rhythm of one competition cycle.
Specify:
Address the reset acceptance problem: if scores reset fully each round, veteran players lose lifetime score advantage. Frame this positively in UX: "A new season begins — everyone starts fresh."
Design the bracket structure for one group of the default size.
Recommended starting structure for a group of 100:
| Bracket | Positions | Players | Reward |
|---|---|---|---|
| Grand Prize | 1 | 1 | [top reward from confirmed input] |
| Major Prize | 2–5 | 4 | [strong reward] |
| Minor Prize | 6–20 | 15 | [moderate reward] |
| Consolation | 21–50 | 30 | [small reward] |
| Nothing | 51–100 | 50 | — |
Adjust bracket boundaries to fit the confirmed reward pool. If rewards are limited, compress the structure. If rewards are abundant, widen mid-tier brackets.
Evaluate coverage:
Note: also design per-league reward differentiation if using a ladder. Higher leagues should receive more valuable rewards for equivalent bracket positions — this makes league promotion feel economically meaningful, not just prestigious.
Flag reward inflation risk if the same reward is distributed every week at the same rate — over time it may devalue. Recommend seasonal variation or rotating exclusive items.
Specify how players are assigned to groups each round.
Based on confirmed input (Random or Matched):
If Matched:
If Random:
Anti-sandbagging measures:
Address what happens when a player reaches and holds the top position.
Design one of the following approaches based on complexity budget and game context, and explain the tradeoff:
Option A — Expanding ladder Add a new top league whenever the existing top league is full and promotion candidates exist. Name leagues with a scalable convention (numbered seasons, themed names drawn from a pre-defined list). Tradeoff: top league loses permanence over time.
Option B — Periodic full reset Reset the entire league ladder periodically (e.g., each season). All players re-enter the bottom league and reclimb. Previous top position is commemorated but not held. Tradeoff: veterans lose status; must be designed in from the beginning to be accepted.
Option C — Prestige layer Top-league players who have won accumulate a prestige score or title that exists outside the ladder. The ladder remains competitive; prestige is additive, not substitutive. Tradeoff: requires additional design and display work.
Recommend the most appropriate option given the confirmed complexity budget and player mix.
Map the resulting design to the full goal hierarchy and verify coverage:
| Goal tier | Mechanism in this design | Assessment |
|---|---|---|
| Immediate — overtake the player above me | Visible neighbor scores in group | [present / absent / weak] |
| Short-term — reach the next reward bracket | Bracket thresholds within group | [present / absent / weak] |
| Medium-term — earn promotion | League promotion threshold | [present / absent / weak] |
| Long-term aspirational — reach the top | League ladder | [present / absent / weak] |
| Defensive — avoid demotion | Demotion zone | [present / absent / weak] |
If any tier is absent or weak, flag it and suggest how to address it.
Summarize the resource flow this system will generate per round:
List the top risks in the proposed design and their mitigations:
| Risk | Likelihood | Mitigation |
|---|---|---|
| Sandbagging | Medium–High if rewards are large | Late-round score freeze; score floor |
| Whale group dominance | Medium if no matching | Spend-band separation in group assignment |
| Top-league stagnation | High over 6+ months | Choose and implement top-of-ladder strategy from Component 7 |
| Reward inflation | Medium over time | Rotate exclusive content; seasonal reward variation |
| Reset backlash | Medium if introduced post-launch | Communicate as "season system" from day one |
| Low participation in early rounds | Medium for new features | Seed initial groups manually or use provisional standings |
If the user provides most answers upfront, skip the question phase and go straight to design — but list the assumptions used at the top of the output.
If the user provides almost nothing, ask all eight questions in a single clean list. Do not proceed until at least Q1 (score type) and Q2 (player pool) are answered or defaulted.
The goal is not to produce the most sophisticated leaderboard. It is to produce the right leaderboard for this game, this player base, and this team — one that generates maximum goal density within the confirmed constraints, and that a designer can hand directly to an engineer as a specification.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.