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 2233admin/design-pipeline --skill tune-enemy-aigit clone --depth 1 https://github.com/2233admin/design-pipelineWrote 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/2233admin/design-pipeline/tune-enemy-ai)<a href="https://agentmods.dev/skills/2233admin/design-pipeline/tune-enemy-ai"><img src="https://agentmods.dev/badge/skills/2233admin/design-pipeline/tune-enemy-ai/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/skills/2233admin/design-pipeline/tune-enemy-ai"><img src="https://agentmods.dev/badge/skills/2233admin/design-pipeline/tune-enemy-ai.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 | $0.00055 | $0.00323 |
| Opus 5 | $0.00028 | $0.00161 |
| Sonnet 5 | $0.00011 | $0.00065 |
| Haiku 4.5 | $0.00006 | $0.00032 |
Grade A, and why
tune-enemy-ai 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 6d 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 tune-enemy-ai — 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.
What it actually says
Tune Enemy AI
Make enemy choices legible, bounded, and reproducible.
Model decisions explicitly
Use a small state machine or utility layer with named states such as idle, investigate, pursue, reposition, windup, attack, recover, stagger, retreat, and defeated. State transitions must state their prerequisites, exit conditions, minimum dwell time, and cooldown effects.
Separate perception, intent, and motion
- Gather observable inputs: distance, line of sight, target state, occupancy, threat, health, and timers.
- Select one intention from constrained legal actions.
- Move and animate toward that intent without rewriting the decision mid-action.
Use authoritative collision and navigation results for movement success. Do not derive them from rendered pose or assumed path completion.
Preserve fair combat
Telegraph attacks before their active window. Prevent instant turn-and-hit behavior, perpetual chase, clipped attacks through blockers, and repeated recovery spam. Add spacing and commitment so the player can read and answer each enemy archetype.
Test the decision surface
Create deterministic fixtures for target acquisition, target loss, obstruction, path failure, close-range pressure, multiple enemies, retaliation, interrupt, stagger, boss phase, and reset. Assert transitions and outcomes, not only final positions. Run a real browser encounter after automated tests.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 29 lines · 55 tokens per session scan A 13bcd0294240
tune-enemy-ai is a skill published in the GitHub repository 2233admin/design-pipeline (9 stars, last pushed 6d ago), licensed MIT. It adds 55 tokens to every session and 323 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 tune-enemy-ai, differing in 0 lines, and is treated as a copy.
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