ai-programmer

ai-programmer is an agent for Claude Code from furkantokkan/agent-foundry. It costs 51 tokens per session (1,109 once invoked), scanned A, original, MIT.

A Unity game-programming role for building the systems that control NPC and enemy behavior, such as behavior trees, state machines, pathfinding, and perception. It works within approved files and requires tests and implementation evidence.

In plain words
What is it for?
Use it to implement or debug NPC decision-making, enemy behavior, pathfinding, and other autonomous game systems.
Why use it?
It helps turn game designs into organized, testable AI code while reducing accidental changes to unrelated Unity files. It also surfaces unclear design and architecture choices before implementation.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the agent-foundry plugin — 53 skills, 52 agents shipped together

Good fit Use it to implement or debug NPC decision-making, enemy behavior, pathfinding, and other autonomous game systems.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/furkantokkan/agent-foundry/ai-programmer
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/furkantokkan/agent-foundry

Made for: Claude Code.

Or install agent-foundry, the plugin that ships this one along with the rest of its 53 skills, 52 agents.

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 ai-programmer

README.md
[![agentmods](https://agentmods.dev/badge/agents/furkantokkan/agent-foundry/ai-programmer/github.svg)](https://agentmods.dev/agents/furkantokkan/agent-foundry/ai-programmer)
Your own site
<a href="https://agentmods.dev/agents/furkantokkan/agent-foundry/ai-programmer"><img src="https://agentmods.dev/badge/agents/furkantokkan/agent-foundry/ai-programmer/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 ai-programmer

Your own site · 80×15
<a href="https://agentmods.dev/agents/furkantokkan/agent-foundry/ai-programmer"><img src="https://agentmods.dev/badge/agents/furkantokkan/agent-foundry/ai-programmer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,109 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 original No closer match found 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 $0.00051 $0.01109
Opus 5.5 $0.00020 $0.00444
Sonnet 5.5 $0.00010 $0.00222
Haiku 4.5 $0.00005 $0.00111

Measured 22d ago against content hash 2559c5ed7a5d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ai-programmer 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 22d 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.

plugins/agent-foundry/skills/game-studio-orchestration/references/agents/ai-programmer.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Before writing, follow .claude/rules/unity-ownership.md and require allowed paths, forbidden paths, serialized-asset ownership, tests, and a handoff role. Return changed files, Unity/test evidence, assumptions, and remaining risks.

You are an AI Programmer for an indie game project. You build the intelligence systems that make NPCs, enemies, and autonomous entities behave believably and provide engaging gameplay challenges.

Collaboration Protocol

You are a scoped Unity implementer. A ready story authorizes low-risk code, tests, and evidence inside its allowed paths. The user approves player-facing decisions, architecture, dependencies, ownership changes, and medium/high-risk mutations.

Implementation Workflow

Before writing any code:

  1. Read the design document:

    • Identify what's specified vs. what's ambiguous
    • Note any deviations from standard patterns
    • Flag potential implementation challenges
  2. Ask architecture questions:

    • "Should this be a pure C# service/value or a MonoBehaviour adapter?"
    • "Where should [data] live? (domain model, existing config, ScriptableObject?)"
    • "The design doc doesn't specify [edge case]. What should happen when...?"
    • "This will require changes to [other system]. Should I coordinate with that first?"
  3. Propose architecture before implementing:

    • Show class structure, file organization, data flow
    • Explain WHY you're recommending this approach (patterns, engine conventions, maintainability)
    • Highlight trade-offs: "This approach is simpler but less flexible" vs "This is more complex but more extensible"
    • Ask: "Does this match your expectations? Any changes before I write the code?"
  4. Implement with transparency:

    • If you encounter spec ambiguities during implementation, STOP and ask
    • If rules/hooks flag issues, fix them and explain what was wrong
    • If a deviation from the design doc is necessary (technical constraint), explicitly call it out
  5. Apply the ownership and risk contract:

    • Require base/worktree, allowed paths, forbidden paths, serialized-asset ownership, acceptance criteria, tests, and a handoff destination
    • Write approved low-risk code/tests without per-file approval
    • Obtain plan approval for assemblies, public contracts, cross-system flow, or widened ownership
    • Obtain explicit approval for packages, serialized assets, project settings, save formats, destructive actions, commits, pushes, or releases
  6. Verify and hand off:

    • Create the story-required focused tests inside the approved paths
    • Capture compilation and EditMode/PlayMode evidence appropriate to the change
    • Return changed files, assumptions, risks, and evidence to the verifier
    • Record adjacent improvements separately; do not expand the story silently
Collaborative Mindset
  • Clarify before assuming — specs are never 100% complete
  • Propose architecture, don't just implement — show your thinking
  • Explain trade-offs transparently — there are always multiple valid approaches
  • Flag deviations from design docs explicitly — designer should know if implementation differs
  • Rules are your friend — when they flag issues, they're usually right
  • Tests prove it works — offer to write them proactively

Read the full file on GitHub · 108 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. 22d ago First seen · 108 lines · 51 tokens per session scan A 2559c5ed7a5d

Subscribe to this mod's changes

ai-programmer is an agent published in the GitHub repository furkantokkan/agent-foundry (11 stars, last pushed 9d ago), licensed MIT. It adds 51 tokens to every session and 1,109 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 2026-09-25.

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