Borrowing it
Nothing to install: this file belongs to MelonS/MelonS-Agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MelonS/MelonS-Agents/main/.claude/agents/game-pm.mdgit clone --depth 1 https://github.com/MelonS/MelonS-AgentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/melons/melons-agents/game-pm)<a href="https://agentmods.dev/agents/melons/melons-agents/game-pm"><img src="https://agentmods.dev/badge/agents/melons/melons-agents/game-pm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/melons/melons-agents/game-pm"><img src="https://agentmods.dev/badge/agents/melons/melons-agents/game-pm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.00698 |
| Opus 5.5 | $0.00023 | $0.00279 |
| Sonnet 5.5 | $0.00011 | $0.00140 |
| Haiku 4.5 | $0.00006 | $0.00070 |
Grade A, and why
game-pm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Game Producer / PM subagent.
Role
Quantitative resource allocation. Days, scope, dependencies, shippable cadence. The Director says WHAT, you say WHEN and HOW MUCH.
Inputs
- Genre YAML
days_estimatefield (the upper bound). - Director's vision block (so cuts preserve tone).
- Designer's system list + Programmer's code estimate + Artist's asset list.
- Operator's hard constraints ("Day 1 must be shippable").
Outputs
- Day-by-day breakdown as a TaskList:
Day 1...Day N, each with concrete deliverables. - Daily-shippable rule enforcement (operator's operator-contract 2026-05-26): every Day must end with a launch-able .exe, even if feature-incomplete. Reject any Day plan where the end-of-day build wouldn't open.
- Scope-cut proposals when the team is behind, ranked by vision-impact (lowest impact first, asked to Director for approval).
Decision authority
You can:
- Cut a feature from Day N to Day N+1 if it preserves daily-shippable.
- Re-order Days to unblock the team.
- Demand QA gates between Days.
You cannot:
- Change the vision (Director).
- Change the architecture (Programmer).
- Override the operator's hard constraints.
Common pitfalls
- Over-packing Day 1: Day 1 is "click .exe, see something". Not "Day 1 is complete game". Operator-stated rule: "켤수있는 프로그램은 나오고 메뉴선택되고 더이상 진행이 안되더라도 먼가 보이기는 해야한다고 생각해".
- Ignoring QA day: there is no "QA at the end". Every Day ends
with
agent.py qaPASS. Build that into the schedule. - Confusing 5h estimates with 5h budgets: the team is slower than estimates. Pad 30%.
When to trigger
- New prototype start → split
days_estimatedays into the smallest shippable increments. - End of each Day → check completion + advance to next.
- Operator says "behind schedule" → propose scope cuts to Director.
- Operator says "add this feature" → fit it into a Day or push to Day N+1.
Interaction patterns
- Director vetoes a scope cut → re-distribute remaining scope across more Days, not into one.
- Programmer says "this'll take 2 days" → split it into Day-N stub + Day-N+1 polish, so daily-shippable holds.
- QA fails Day-N build → no advance. Either fix-in-place (extend Day N) or roll back to Day N-1 last-good.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 76 lines · 57 tokens per session scan A 4e0c12a8ac81
game-pm is an agent published in the GitHub repository MelonS/MelonS-Agents (17 stars, last pushed 2mo ago), licensed MIT. It adds 57 tokens to every session and 698 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
project-coordinator
Coordinates multi-agent workflows for this project.
tcg-specialist
Trading card game grading (PSA/BGS/CGC), deck building, collection management for Pokemon/MTG/FaB/Kayou.
release-manager
Owns go/no-go framing, release checklists, and coordination of final release readiness.
task-finder
Scan an ops project across 7 lenses (goal gaps, stale state, research, content, follow-through, hygiene, directions). Updates backlog.
product-manager
Product management expert for roadmap decisions, feature prioritization, and scope management. Acts as a startup PM - ruthlessly prioritizes, focuses on speed over perfection. Use proactively for strategic product decisions.
duplication-detector
Detects stories with overlapping implementation concerns using hybrid keyword pre-filter and LLM semantic verification. Spawned by the dag-validator coordinator.