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.
npx skills add HolyMonkey/youtube-example-ai-studio --skill team-combatgit clone --depth 1 https://github.com/HolyMonkey/youtube-example-ai-studioWrote 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/skills/holymonkey/youtube-example-ai-studio/team-combat)<a href="https://agentmods.dev/skills/holymonkey/youtube-example-ai-studio/team-combat"><img src="https://agentmods.dev/badge/skills/holymonkey/youtube-example-ai-studio/team-combat.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.00826 |
| Opus 5 | $0.00025 | $0.00413 |
| Sonnet 5 | $0.00010 | $0.00165 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
team-combat 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 3d 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.
This is a copy
100% identical to team-combat — 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.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When this skill is invoked, orchestrate the combat team through a structured pipeline.
Decision Points: At each phase transition, use AskUserQuestion to present
the user with the subagent's proposals as selectable options. Write the agent's
full analysis in conversation, then capture the decision with concise labels.
The user must approve before moving to the next phase.
Team Composition
- game-designer — Design the mechanic, define formulas and edge cases
- gameplay-programmer — Implement the core gameplay code
- ai-programmer — Implement NPC/enemy AI behavior for the feature
- technical-artist — Create VFX, shader effects, and visual feedback
- sound-designer — Define audio events, impact sounds, and ambient combat audio
- qa-tester — Write test cases and validate the implementation
How to Delegate
Use the Task tool to spawn each team member as a subagent:
subagent_type: game-designer— Design the mechanic, define formulas and edge casessubagent_type: gameplay-programmer— Implement the core gameplay codesubagent_type: ai-programmer— Implement NPC/enemy AI behaviorsubagent_type: technical-artist— Create VFX, shader effects, visual feedbacksubagent_type: sound-designer— Define audio events, impact sounds, ambient audiosubagent_type: qa-tester— Write test cases and validate implementation
Always provide full context in each agent's prompt (design doc path, relevant code files, constraints). Launch independent agents in parallel where the pipeline allows it (e.g., Phase 3 agents can run simultaneously).
Pipeline
Phase 1: Design
Delegate to game-designer:
- Create or update the design document in
design/gdd/covering: mechanic overview, player fantasy, detailed rules, formulas with variable definitions, edge cases, dependencies, tuning knobs with safe ranges, and acceptance criteria - Output: completed design document
Phase 2: Architecture
Delegate to gameplay-programmer (with ai-programmer if AI is involved):
- Review the design document
- Design the code architecture: class structure, interfaces, data flow
- Identify integration points with existing systems
- Output: architecture sketch with file list and interface definitions
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.
- 3d ago First seen · 75 lines · 50 tokens per session scan A 18625e28937a
team-combat is a skill published in the GitHub repository HolyMonkey/youtube-example-ai-studio (11 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 826 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 team-combat, differing in 0 lines, and is treated as a copy.
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