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Agent skill

Fps

by autonomous-ai in autonomous-ai/openharness

Design an arena level for Jev FPS (the ASCII map, the demons, the pace), then measure where Jev's decision rate stops being enough.

MITAuto-check passed

Install Fps

skills CLI
$ npx skills add autonomous-ai/openharness --skill fps -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install autonomous-ai/openharness fps --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/autonomous-ai/openharness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/store/agents/jev-fps/skills/fps .claude/skills/fps && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fps
GitHub stars
1.1k
Token cost
~656 tokens
SKILL.md length
372 words
Files
1
Skills in repo
100
Repo updated
First seen
Licence
MIT

At a glance

Design an arena level for Jev FPS (the ASCII map, the demons, the pace), then measure where Jev's decision rate stops being enough.

  • SKILL.md covers What Jev is asked, every tick, Drawing a good map, Setting the pace and Verifying
  • Calls node

What it does

Fps is an agent skill from autonomous-ai/openharness. Design an arena level for Jev FPS (the ASCII map, the demons, the pace), then measure where Jev's decision rate stops being enough.

Its SKILL.md is about 660 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across… The licence is MIT.

Example prompts

  • “/fps”

What it can do on your machine

Read from SKILL.md and the folder at commit 50da5db. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fps loads about 656 tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 372 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~656

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from autonomous-ai/openharness at commit 50da5db, republished under its MIT licence (© autonomous-ai). 372 words, ~656 tokens.

Download SKILL.mdSave it as .claude/skills/fps/SKILL.md (or your agent's skills folder).
name
fps
description
Design an arena level for Jev FPS (the ASCII map, the demons, the pace), then measure where Jev's decision rate stops being enough.

Craft: Jev FPS levels

Jev FPS is a first-person arena shooter that Jev plays by itself, one typed decision at a time. You shape the arena in level.json. The craft is a map that reads well in 3D and a pace that shows the real limit: about nine decisions a second.

What Jev is asked, every tick

One call, four questions, all answered in parallel from the same state text:

turn    choice  LEFT_HARD  LEFT  LEFT_FINE  AHEAD  RIGHT_FINE  RIGHT  RIGHT_HARD
move    choice  FORWARD  BACK  STRAFE_LEFT  STRAFE_RIGHT  HOLD
fire    noul    "Fire now? Yes only when a demon is in your crosshair and you have ammo."
threat  score   calm < watchful < pressed < critical

The state text is what a player could know: health, ammo, wall distances, each demon in sight with its bearing and distance, the nearest demon it can hear but not see, the automap route, and whether the crosshair is on a demon. The pane shows this text live under "What Jev reads".

Drawing a good map

  • Tiles: # stone, % tech panel, = hell brick, . floor, P start, D demon spawn, M medkit, A ammo. The border must be wall. Every row is the same width.
  • Give sight lines of 5 to 9 tiles. Very long halls make it a shooting gallery. Tight mazes hide demons until they are biting.
  • Put spawns on at least two sides of the start so Jev must turn to deal with them.
  • Pillars (single wall tiles in a room) make demons split and flank, which is fun to watch.
  • A level of about 24 by 16 fills the minimap nicely. Bigger maps mean long quiet walks.
Show full SKILL.md (143 more words)Show less

Setting the pace

demonSpeed is the dial. Demons weave as they charge, so speed is also how fast they cross the crosshair. Each wave adds one demon and 12% speed, so every run ends when the pace passes what nine decisions a second can handle.

feeldemonSpeeddemons
a stroll, long runs0.83
the default1.24
tense from wave 12.45
brutal4.08

tickMs changes the decision rate itself. Raising it to 250 shows what a slower decision loop costs: the same demons now win much earlier.

Verifying

bash
node "$JEV_DSH/toolchain/check.mjs"

Then watch the pane and read .harness/verdict.json: wave, kills, deaths, best wave, shots on target. Report the pace you chose, how far Jev gets, and the speed where it stops clearing waves. The pane's MOCK badge means the offline stand-in is playing, not the real model. Say so when you report.

© autonomous-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in store/agents/jev-fps/skills/fps of autonomous-ai/openharness.

Open the folder on GitHubat commit 50da5db

Compare with similar skills

Fps next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fps compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Fps this skillautonomous-ai/openharness1.1k—~656Automated safety check: PassMIT
Token Mapnexu-io/open-design100k—~1.4kAutomated safety check: PassApache-2.0
Maps Geographyasgeirtj/system_prompts_leaks69k—~717Automated safety check: PassCC0-1.0
Feature Maponyx-dot-app/onyx32k—~459Automated safety check: PassCustom licence
Source Mapsthedaviddias/Front-End-Checklist74k—~445Automated safety check: PassMIT
Customer Journey Mapphuryn/pm-skills27k—~816Automated safety check: PassMIT

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Questions about Fps

What does Fps do?

Design an arena level for Jev FPS (the ASCII map, the demons, the pace), then measure where Jev's decision rate stops being enough. Fps is an agent skill from autonomous-ai/openharness. Design an arena level for Jev FPS (the ASCII map, the demons, the pace), then measure where Jev's decision rate stops being enough.

How do I install Fps in Claude Code?

Run `npx skills add autonomous-ai/openharness --skill fps -a claude-code`. Or copy the skill folder (store/agents/jev-fps/skills/fps in autonomous-ai/openharness) into .claude/skills/fps in your project. Claude Code loads it when a task matches its description.

How do I install Fps in Codex?

Run `npx skills add autonomous-ai/openharness --skill fps -a codex`. Or copy the skill folder (store/agents/jev-fps/skills/fps in autonomous-ai/openharness) into .agents/skills/fps in your project. Codex loads it when a task matches its description.

Can I use Fps in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add autonomous-ai/openharness --skill fps -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fps, .gemini/skills/fps, .github/skills/fps and .opencode/skills/fps in your project.

What does Fps need to run?

Going by SKILL.md and its folder, Fps needs the command-line tools its instructions call (node).

Does Fps access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Fps safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fps use?

Fps is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fps use?

About 656 tokens (SKILL.md is roughly 2.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Fps?

Skills that share tags, products or a category with Fps: Token Map (nexu-io/open-design, 100k stars), Maps Geography (asgeirtj/system_prompts_leaks, 69k stars), Feature Map (onyx-dot-app/onyx, 32k stars) and Source Maps (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fps?

autonomous-ai (a GitHub organization) maintains it in autonomous-ai/openharness, which has 1,149 GitHub stars. The repository holds 100 skills in this directory. The repository was last updated on October 8, 2026.

Source: autonomous-ai/openharness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.