Image to Three.js Model
img2threejs/img2threejs
Rebuilds the object in a reference image as a procedural, animation-ready Three.js model written entirely in code, using staged sculpting with quality checks.
Build benchmarks of realistic, hard agent tasks, researched from real work, admitting only verified, shortcut-resistant, calibrated tasks and reporting how well the suite separates systems.
$ npx skills add DanMcInerney/orchflows --skill benchmaker -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install DanMcInerney/orchflows benchmaker --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/DanMcInerney/orchflows.git skills-src && mkdir -p .claude/skills && cp -r skills-src/example-workflows/benchmaker/skills/benchmaker .claude/skills/benchmaker && rm -rf skills-srcUse ~/.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/
Install the "benchmaker" agent skill from https://github.com/DanMcInerney/orchflows/tree/main/example-workflows/benchmaker/skills/benchmaker into .claude/skills/benchmaker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmaker", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/DanMcInerney/orchflows/tree/main/example-workflows/benchmaker/skills/benchmakerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add DanMcInerney/orchflows --skill benchmaker -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install DanMcInerney/orchflows benchmaker --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DanMcInerney/orchflows.git skills-src && mkdir -p .agents/skills && cp -r skills-src/example-workflows/benchmaker/skills/benchmaker .agents/skills/benchmaker && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "benchmaker" agent skill from https://github.com/DanMcInerney/orchflows/tree/main/example-workflows/benchmaker/skills/benchmaker into .agents/skills/benchmaker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmaker", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add DanMcInerney/orchflows --skill benchmaker -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install DanMcInerney/orchflows benchmaker --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DanMcInerney/orchflows.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/example-workflows/benchmaker/skills/benchmaker .cursor/skills/benchmaker && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "benchmaker" agent skill from https://github.com/DanMcInerney/orchflows/tree/main/example-workflows/benchmaker/skills/benchmaker into .cursor/skills/benchmaker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmaker", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/DanMcInerney/orchflows.git --path example-workflows/benchmaker/skills/benchmaker--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add DanMcInerney/orchflows --skill benchmaker -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install DanMcInerney/orchflows benchmaker --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DanMcInerney/orchflows.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/example-workflows/benchmaker/skills/benchmaker .gemini/skills/benchmaker && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "benchmaker" agent skill from https://github.com/DanMcInerney/orchflows/tree/main/example-workflows/benchmaker/skills/benchmaker into .gemini/skills/benchmaker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmaker", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install DanMcInerney/orchflows benchmakerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add DanMcInerney/orchflows --skill benchmaker -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/DanMcInerney/orchflows.git skills-src && mkdir -p .github/skills && cp -r skills-src/example-workflows/benchmaker/skills/benchmaker .github/skills/benchmaker && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "benchmaker" agent skill from https://github.com/DanMcInerney/orchflows/tree/main/example-workflows/benchmaker/skills/benchmaker into .github/skills/benchmaker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmaker", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add DanMcInerney/orchflows --skill benchmaker -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install DanMcInerney/orchflows benchmaker --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DanMcInerney/orchflows.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/example-workflows/benchmaker/skills/benchmaker .opencode/skills/benchmaker && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "benchmaker" agent skill from https://github.com/DanMcInerney/orchflows/tree/main/example-workflows/benchmaker/skills/benchmaker into .opencode/skills/benchmaker/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmaker", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
benchmakerBuild benchmarks of realistic, hard agent tasks, researched from real work, admitting only verified, shortcut-resistant, calibrated tasks and reporting how well the suite separates systems.
Benchmaker is an agent skill from DanMcInerney/orchflows. Build benchmarks of realistic, hard agent tasks, researched from real work, admitting only verified, shortcut-resistant, calibrated tasks and reporting how well the suite separates systems.
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 82 other files, including scripts (for example `agents/openai.yaml`, `scripts/benchkit/INTERFACE.md` and `scripts/benchkit/__init__.py`).
It sits in Game Development. The repository describes itself as: 2 skills, composable into workflows, which can then be built into more complex workflows. The licence is MIT.
7 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 8d16eb3. It shows what the files ask for, not the result of running them.
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.
Ships 16 files in scripts/ (Python, from the files we listed), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Benchmaker loads about 2k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 1,116 words of instructions outside code blocks.
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.
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); the scripts in this folder are not scanned.
The full file from DanMcInerney/orchflows at commit 8d16eb3, republished under its MIT licence (© DanMcInerney). 1,116 words, ~2,015 tokens.
.claude/skills/benchmaker/SKILL.md (or your agent's skills folder). This skill also uses 79 other files; get the full folder from GitHub.Apply library context. Accept a target: path, callable, command, endpoint, native workflow, skill or capability description. Also accept an optional claim, comparisons, budget and output location. Infer ordinary choices; clarify only material claim, permission or budget decisions. Without an executable target, a named representative stands in for it as the quality card defines; a description with no executable representative supports only a draft.
A benchmark samples realistic, hard, valuable work and separates systems that differ in the claimed ability. Most candidate tasks should fail admission. A smaller budget admits fewer tasks and reaches a lower stage; it never weakens tasks or skips admission checks. Spend follows earned evidence: a task version that fails a criterion gets no further checks, so costly judgments go to tasks that cheaper evidence has not disqualified.
The coordinator owns every assignment and every target, comparison, known-order check, calibration and adversary launch. Other agents return results and execution requests without delegating. Composing targets run in separate top-level sessions under core docs/hosts.md#workflow-trials. Target, comparison, known-order check and calibration attempts run through the package's runner, which enforces their caps; deadlines given to native helpers are requests, so record overruns.
Before launching, declare and show planned research workers, candidates, per-task revision limit, repeats, calibration and known-order check launches for admission and measurement, concurrency, authoring and attempt time, deadlines and estimated spend. When caller limits cannot fit building or solving work at the difficulty target, say so before launching. A wall-clock limit covers the whole build: plan when measurement, review and delivery must start for each to finish inside it. When earlier work runs long or stops converging, reduce scope to fewer tasks or families, or a lower stage, so later steps still run. Caller limits on launches or sessions count every launch they name. When a limit names only target runs, report authoring, auditing, adversary, calibration and known-order check launches separately. Record measurement conditions separately from caller limits; failed attempts consume execution limits. Record each launch and each task's admission state durably before dependent work, so an interrupted build resumes without relaunching completed work and can still deliver a draft.
Claim. Inspect the target, representative user work, research lessons and relevant primary precedents. State the solver's goal: the work it does, for whom, and what success looks like. Record the claim entries of the quality card, including the systems the benchmark must measure; ask the user when neither the request nor the target settles them. Choose the comparison set, known-order check, calibration systems and, without an executable target, its representative under benchmarking guidance and the card.
Research the work. Fan out web research guided by the solver's goal: where people do this work, what real instances look like, how it goes wrong, what makes real cases hard, and the most complex real work people attempt, including work beyond today's strongest systems. Give each fresh research worker a distinct scope with research guidance; workers return sources and findings, not tasks. Gather them into a catalog of realistic scenarios and hard cases, recording sources, capture dates and reuse constraints. Include the real material that could become tasks, such as repositories, issues with their fixes, datasets, incident reports and practitioner accounts. Report uncovered ground as a gap. Without web access, research what the caller supplies and record the gap.
Source. Apply common and Make benchmarking guidance. Draw more candidate work than the suite needs from the catalog and the target's own failures: real work first, then reconstructions from real material, and synthetic work only as a labeled fallback. Each candidate states its value, why it is hard for the claimed ability and an estimated expert time. Spread candidates across families, independent source groups and the catalog's range of difficulty, including its hardest real work, before expanding any one.
Build and admit. Authors build each candidate under the benchmark contract: public instruction and interface, environment, reference solution and verifier. Freeze each built task, then gather its admission evidence:
Auditors and adversaries may cover several tasks they did not author. When the suite holds many short items, audit, adversary and realism judgments may cover each family through a recorded sample. Revise or reject by the admission criteria; a revision reruns the checks it affects and counts toward the per-task limit.
Measure. Freeze the admitted suite. Run the comparison set and the known-order check with predeclared repeats, measuring the actual target or its representative. Compute the card. Classify failures from transcripts as ability, task defect, grading, infrastructure, refusal, cut-off or unknown, and audit passing transcripts, or a recorded sample of them, for unearned credit.
Review and repair. Apply shared:review-revise-once with benchmarking and task guidance, using a fresh reviewer who neither authored, audited nor reviewed the realism of tasks. Its inputs are the frozen suite, card, rejection log, admission evidence and a transcript sample that includes successes. Scope repairs and checks to the package. Review repairs count toward each task's revision limit. Changed tasks receive new identities and rerun affected admission and measurement within remaining allowance. A repaired task that fails re-admission leaves the suite, which then receives a new identity, or stays draft.
Deliver. Return the suite, commands, card, research catalog, rejection log, per-family results with denominators, measured time and cost, requested and achieved stage, and gaps. State whether measured headroom and separation support the claim. Missing required execution, judgment or review, or an unresolved validity defect, leaves affected claims draft. An interrupted or limit-stopped build delivers these items as a draft from its records. Name the next unmet stage.
© DanMcInerney, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 79 other files (scripts) in example-workflows/benchmaker/skills/benchmaker of DanMcInerney/orchflows.
Open the folder on GitHubat commit 8d16eb3
Benchmaker 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Benchmaker this skillDanMcInerney/orchflows | 117 | — | ~2k | Automated safety check: Pass | MIT | |
| Image to Three.js Modelimg2threejs/img2threejs | 18k | 1 repos | ~8.2k | Automated safety check: Pass | Apache-2.0 | |
| Web CloneJane-xiaoer/claude-skill-web-clone | 1k | 1 repos | ~2.7k | Automated safety check: Pass | MIT | |
| Threejs Game Directormajidmanzarpour/threejs-game-skills | 2.4k | — | ~2.2k | Automated safety check: Pass | MIT | |
| Game Asset Generatorhtdt/godogen | 7.1k | — | ~2.8k | Automated safety check: Pass | MIT | |
| Threejs Gameplay Systemsvalkor-ai/loom | 1.2k | 1 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 |
img2threejs/img2threejs
Rebuilds the object in a reference image as a procedural, animation-ready Three.js model written entirely in code, using staged sculpting with quality checks.
Jane-xiaoer/claude-skill-web-clone
网站复刻 / 克隆方法论。USE WHEN 用户说 复刻网站、克隆网站、clone website、抄个站、仿站、 照着这个站做一个、reproduce site、还原某个网页效果、把这个站搬下来改成我的、 复刻某个交互/WebGL/Canvas/Three.js 效果。提供「先拿真源码 → 判路径 → 逆向拆解 → 搭工程 → 替换内容」的可移植决策树,覆盖静态站 /…
majidmanzarpour/threejs-game-skills
Entrypoint for building, upgrading, and finishing Three.js browser games.
htdt/godogen
Generates game art from text prompts: PNG images, GLB 3D models, rigged characters, animations and sprites, with background removal.
valkor-ai/loom
Build and iterate playable Three.js game systems: starter scaffold, architecture, design briefs, core loops, level and encounter design, entities, input, camera, collision and physics, scoring…
CyberAgentGameEntertainment/NovaShader
Execute C with Unity APIs when existing uloop tools cannot inspect or edit enough.
DanMcInerney/orchflows
Iteratively improve any artifact and its improvement harness, inventing evaluation when needed; supports bounded runs, tournaments and continuous resumable search.
DanMcInerney/orchflows
Build a complete Three.js game or bounded production phase through mechanics experiments, Blender assets, QA and independent playtests.
DanMcInerney/orchflows
Develop a project endgoal through N bounded cycles of brainstorm and research, design, implementation, comparison testing and analysis.
DanMcInerney/orchflows
Export an Orchflows workflow as a standalone native skill with a portability report and bounded trial.
DanMcInerney/orchflows
Drive an ambitious goal past a real-world quality bar by splitting it into pieces, each looped through a builder and a fresh, harsh, blind critic until ours reaches the bar or the caller stops.
DanMcInerney/orchflows
Review Orchflows against its purpose and design principles (architecture, workflow design, wording and bugs) and apply reviewed improvements on unmerged branches.
Categories
Build benchmarks of realistic, hard agent tasks, researched from real work, admitting only verified, shortcut-resistant, calibrated tasks and reporting how well the suite separates systems. Benchmaker is an agent skill from DanMcInerney/orchflows. Build benchmarks of realistic, hard agent tasks, researched from real work, admitting only verified, shortcut-resistant, calibrated tasks and reporting how well the suite separates systems.
Benchmaker fits situations like: game Development work in your project.
Run `npx skills add DanMcInerney/orchflows --skill benchmaker -a claude-code`. Or copy the skill folder (example-workflows/benchmaker/skills/benchmaker in DanMcInerney/orchflows) into .claude/skills/benchmaker in your project. Claude Code loads it when a task matches its description.
Run `npx skills add DanMcInerney/orchflows --skill benchmaker -a codex`. Or copy the skill folder (example-workflows/benchmaker/skills/benchmaker in DanMcInerney/orchflows) into .agents/skills/benchmaker in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add DanMcInerney/orchflows --skill benchmaker -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/benchmaker, .gemini/skills/benchmaker, .github/skills/benchmaker and .opencode/skills/benchmaker in your project.
Going by SKILL.md and its folder, Benchmaker needs Python for the scripts in its folder. Our summary lists: Python 3.
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.
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Benchmaker is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Benchmaker: Image to Three.js Model (img2threejs/img2threejs, 18k stars), Web Clone (Jane-xiaoer/claude-skill-web-clone, 1k stars), Threejs Game Director (majidmanzarpour/threejs-game-skills, 2.4k stars) and Game Asset Generator (htdt/godogen, 7.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
DanMcInerney (a GitHub user) maintains it in DanMcInerney/orchflows, which has 117 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 4, 2026.
Source: DanMcInerney/orchflows on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.