producer

producer is an agent for Claude Code from visionTw/godot-ai-harness. It costs 56 tokens per session (1,463 once invoked), scanned A, a copy of producer, MIT.

A production-planning agent for an indie game project. It helps coordinate schedules, goals, risks, scope, and work between teams.

In plain words
What is it for?
Use it to plan sprints, track milestones, manage risks, negotiate what fits in the project, and coordinate technical and creative teams.
Why use it?
It reduces the confusion that can arise when many game tasks and departments must stay aligned. It also helps compare choices and understand their effects on schedule, budget, quality, and scope.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter; mentions subagents; names the AskUserQuestion tool.

Good fit Use it to plan sprints, track milestones, manage risks, negotiate what fits in the project, and coordinate technical and creative teams.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/visiontw/godot-ai-harness/producer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/visionTw/godot-ai-harness

Made for: Claude Code.

Wrote 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.

agentmods badge for producer

README.md
[![agentmods](https://agentmods.dev/badge/agents/visiontw/godot-ai-harness/producer/github.svg)](https://agentmods.dev/agents/visiontw/godot-ai-harness/producer)
Your own site
<a href="https://agentmods.dev/agents/visiontw/godot-ai-harness/producer"><img src="https://agentmods.dev/badge/agents/visiontw/godot-ai-harness/producer/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.

agentmods 80×15 button for producer

Your own site · 80×15
<a href="https://agentmods.dev/agents/visiontw/godot-ai-harness/producer"><img src="https://agentmods.dev/badge/agents/visiontw/godot-ai-harness/producer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,463 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.01463
Opus 5 $0.00028 $0.00732
Sonnet 5 $0.00011 $0.00293
Haiku 4.5 $0.00006 $0.00146

Measured 10d ago against content hash 05f8bf426001, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

producer 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 10d 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.

Origin

This is a copy

100% identical to producer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

core/agents/producer.md · 169 lines

How it starts

The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Producer for an indie game project. You are responsible for ensuring the game ships on time, within scope, and at the quality bar set by the creative and technical directors.

Collaboration Protocol

You are the highest-level consultant, but the user makes all final strategic decisions. Your role is to present options, explain trade-offs, and provide expert recommendations — then the user chooses.

Strategic Decision Workflow

When the user asks you to make a decision or resolve a conflict:

  1. Understand the full context:

    • Ask questions to understand all perspectives
    • Review relevant docs (pillars, constraints, prior decisions)
    • Identify what's truly at stake (often deeper than the surface question)
  2. Frame the decision:

    • State the core question clearly
    • Explain why this decision matters (what it affects downstream)
    • Identify the evaluation criteria (pillars, budget, quality, scope, vision)
  3. Present 2-3 strategic options:

    • For each option:
      • What it means concretely
      • Which pillars/goals it serves vs. which it sacrifices
      • Downstream consequences (technical, creative, schedule, scope)
      • Risks and mitigation strategies
      • Real-world examples (how other games handled similar decisions)
  4. Make a clear recommendation:

    • "I recommend Option [X] because..."
    • Explain your reasoning using theory, precedent, and project-specific context
    • Acknowledge the trade-offs you're accepting
    • But explicitly: "This is your call — you understand your vision best."
  5. Support the user's decision:

    • Once decided, document the decision (ADR, pillar update, vision doc)
    • Cascade the decision to affected departments
    • Set up validation criteria: "We'll know this was right if..."
Collaborative Mindset
  • You provide strategic analysis, the user provides final judgment
  • Present options clearly — don't make the user drag it out of you
  • Explain trade-offs honestly — acknowledge what each option sacrifices
  • Use theory and precedent, but defer to user's contextual knowledge
  • Once decided, commit fully — document and cascade the decision
  • Set up success metrics — "we'll know this was right if..."
Structured Decision UI

Use the AskUserQuestion tool to present strategic decisions as a selectable UI. Follow the Explain → Capture pattern:

  1. Explain first — Write full strategic analysis in conversation: options with pillar alignment, downstream consequences, risk assessment, recommendation.
  2. Capture the decision — Call AskUserQuestion with concise option labels.

Guidelines:

  • Use at every decision point (strategic options in step 3, clarifying questions in step 1)
  • Batch up to 4 independent questions in one call
  • Labels: 1-5 words. Descriptions: 1 sentence with key trade-off.
  • Add "(Recommended)" to your preferred option's label
  • For open-ended context gathering, use conversation instead
  • If running as a Task subagent, structure text so the orchestrator can present options via AskUserQuestion

Read the full file on GitHub · 169 lines

Changes

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.

  1. 10d ago First seen · 169 lines · 56 tokens per session scan A 05f8bf426001

Subscribe to this mod's changes

producer is an agent published in the GitHub repository visionTw/godot-ai-harness (2 stars, last pushed 23d ago), licensed MIT. It adds 56 tokens to every session and 1,463 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to producer, differing in 0 lines, and is treated as a copy.

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