Ako4all
TongmingLAIC/AKO4ALL
Drive an agentic loop that iteratively optimizes a GPU kernel for maximum speedup.
Fixes tool environment setup failures in proto-tools, either just for the current machine (eject the tool's standalone dir, patch it, and point PROTO<TOOLKITSTANDALONEDIR at it; works for any…
$ npx skills add evo-design/proto-tools --skill fix-env -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install evo-design/proto-tools fix-env --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/evo-design/proto-tools.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/fix-env .claude/skills/fix-env && 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 "fix-env" agent skill from https://github.com/evo-design/proto-tools/tree/main/.claude/skills/fix-env into .claude/skills/fix-env/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fix-env", 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/evo-design/proto-tools/tree/main/.claude/skills/fix-envType 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 evo-design/proto-tools --skill fix-env -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install evo-design/proto-tools fix-env --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/evo-design/proto-tools.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/fix-env .agents/skills/fix-env && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "fix-env" agent skill from https://github.com/evo-design/proto-tools/tree/main/.claude/skills/fix-env into .agents/skills/fix-env/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fix-env", 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 evo-design/proto-tools --skill fix-env -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install evo-design/proto-tools fix-env --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/evo-design/proto-tools.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/fix-env .cursor/skills/fix-env && 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 "fix-env" agent skill from https://github.com/evo-design/proto-tools/tree/main/.claude/skills/fix-env into .cursor/skills/fix-env/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fix-env", 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/evo-design/proto-tools.git --path .claude/skills/fix-env--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 evo-design/proto-tools --skill fix-env -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install evo-design/proto-tools fix-env --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/evo-design/proto-tools.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/fix-env .gemini/skills/fix-env && 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 "fix-env" agent skill from https://github.com/evo-design/proto-tools/tree/main/.claude/skills/fix-env into .gemini/skills/fix-env/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fix-env", 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 evo-design/proto-tools fix-envInstalls 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 evo-design/proto-tools --skill fix-env -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/evo-design/proto-tools.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/fix-env .github/skills/fix-env && 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 "fix-env" agent skill from https://github.com/evo-design/proto-tools/tree/main/.claude/skills/fix-env into .github/skills/fix-env/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fix-env", 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 evo-design/proto-tools --skill fix-env -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install evo-design/proto-tools fix-env --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/evo-design/proto-tools.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/fix-env .opencode/skills/fix-env && 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 "fix-env" agent skill from https://github.com/evo-design/proto-tools/tree/main/.claude/skills/fix-env into .opencode/skills/fix-env/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fix-env", 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.
fix-envFixes tool environment setup failures in proto-tools, either just for the current machine (eject the tool's standalone dir, patch it, and point PROTO<TOOLKITSTANDALONEDIR at it; works for any…
Fix Env is an agent skill from evo-design/proto-tools. Fixes tool environment setup failures in proto-tools, either just for the current machine (eject the tool's standalone dir, patch it, and point PROTO<TOOLKITSTANDALONEDIR at it; works for any install, including a non-editable pip install) or as a cross-platform fix contributed back to the repo. Same diagnosis for both: infrastructure failures (compute detection, env variable isolation, sitecustomize.py injection, micromamba install), PyTorch/CUDA issues (ABI mismatch, broken symlinks, triton coordination), JAX…
Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `PATTERNS.md`).
It sits in AI & LLM Engineering, covering Deep learning. It works with CUDA, Python and PyTorch. The repository describes itself as: A universal infrastructure layer for generative biology. The licence is MIT.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 64363bd. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteBashGlobGrepFrom allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
pytestpythonpipuvFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pip and uv, which can reach the network depending on how they are called.
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.
Fix Env loads about 2.5k tokens when it runs. Until then it costs about 224 tokens; SKILL.md has 1,032 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 noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Read, Write, Bash, Glob, GrepAutomated 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.
The full file from evo-design/proto-tools at commit 64363bd, republished under its MIT licence (© evo-design). 1,032 words, ~2,550 tokens.
.claude/skills/fix-env/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.When to use: a tool's environment fails to build or work, and you need to fix it — either just on the current machine, or as a fix the whole project should ship.
The diagnosis is identical; what differs is where you apply the fix and how compatible it has to be. Decide the mode first; the failure patterns and workflow below apply to both.
For when you (or a user you're helping) hit a broken env and want it working now, without modifying the installed package. Works for any install, including a non-editable pip install where the packaged files sit in read-only site-packages.
proto-tools eject-standalone <toolkit> # -> ./proto_standalone/<toolkit>/
export PROTO_<TOOLKIT>_STANDALONE_DIR=$PWD/proto_standalone/<toolkit>PROTO_ENV_VERBOSE=1 (streams setup.sh output to your terminal); diagnose with the patterns below.setup.sh, etc.) under ./proto_standalone/<toolkit>/.Never edit the installed package. Tell the user to export the variable per project (e.g. a direnv .envrc) so it applies only where they want. Reference: "Overriding a tool's standalone env" in notes/tool-environments.md.
For when you have the repo (editable install) and the fix belongs upstream so every platform benefits.
You will only be testing on the current machine. Assume the existing setup works on other clusters. Make surgical changes to standalone/setup.sh (and other standalone/ files) that fix the current machine while maintaining compatibility with other platforms, using defensive patterns (|| true, conditional checks, graceful fallbacks), then commit them.
Both modes edit the same files — the ejected copy (local) or the repo copy (contributed):
standalone/setup.shstandalone/requirements.txtstandalone/env_vars.txtstandalone/binary_config.pystandalone/python_version.txtstandalone/uv_version.txt (optional; only when the build breaks on the pinned uv)Never modify standalone/run.py, standalone/inference.py, or {toolkit}.py (core implementation).
| Category | Pattern | Symptoms | Solution |
|---|---|---|---|
| Infra | Compute Detection (Pattern 1) | Wrong torch installed, No GPU detected | Verify nvidia-smi, check DETECTED_* vars |
| Infra | Env Var Isolation (Pattern 2) | uv installs to wrong env, missing libs | Check CONDA_PREFIX, VIRTUAL_ENV, LD_LIBRARY_PATH |
| Infra | sitecustomize.py (Pattern 3) | ctypes.CDLL errors, CC not found | Verify generated file, check lib paths |
| Infra | Micromamba Install (Pattern 4) | Failed to download/extract micromamba | Check network, manual install to cache |
| PyTorch | ABI Mismatch (Pattern 5) | undefined symbol, ImportError: *.so | Cache clear + --refresh + validate deep imports |
| PyTorch | Broken CUDA Symlinks (Pattern 6) | libcudart.so: No such file | Auto-repair symlinks in cuda_env |
| PyTorch | Triton Version (Pattern 7) | PY_SSIZE_T_CLEAN crash | Upgrade triton AFTER all other installs |
| JAX | Version Downgrade (Pattern 8) | JAX uses CPU on GPU, wrong CUDA plugin | Re-apply JAX spec after dependency install |
| Compile | GCC/nvcc Mismatch (Pattern 9) | _Float32 undeclared, nvcc errors | Match GCC to CUDA version, pin sysroot |
| Compile | Platform Detection (Pattern 10) | No CUDA target, not supported on aarch64 | Platform guards + graceful fallbacks |
| Network | GitHub Wheel 404 (Pattern 11) | HTTP Error 404/502 | Switch to PyPI |
| Compile | OOM Source Build (Pattern 12) | Killed signal, exit -9 | Prefer wheels, MAX_JOBS=1 |
| Network | Binary Download (Pattern 13) | Failed after 3 attempts | Check network, platform support in binary_config.py |
| Platform | CUDA Headers (Pattern 14) | cuda_runtime.h: No such file | Conditional symlinks |
| Platform | Python Version (Pattern 15) | No wheel for Python 3.12 | python_version.txt |
| Device Mgmt | Standalone Helpers Import (Pattern 16) | ImportError: standalone_helpers | Verify source dir exists; check for a stale in-tree copy |
| Device Mgmt | CUDA Visibility Mismatch (Pattern 17) | No available device, wrong GPU | Check CUDA_VISIBLE_DEVICES vs BIO_TOOLS_MANAGED_DEVICES |
For detailed patterns with full bash examples: Read .claude/skills/fix-env/PATTERNS.md
cat tool_envs/{tool}_env/STATUS.txtCheck for error messages, DETECTED_* var values, and the exit point.
# Check actual hardware
nvidia-smi
# Check what the detection set
python -c "
from proto_tools.utils.compute_deps import detect_compute_environment
env = detect_compute_environment()
for k, v in sorted(env.items()):
print(f'{k}={v}')
"If nvidia-smi works but DETECTED_COMPUTE_PLATFORM=cpu, compute detection failed — see Pattern 1. Fix this first — everything downstream (torch, JAX) depends on correct detection.
# Inside a tool subprocess, check critical env vars:
# CONDA_PREFIX and VIRTUAL_ENV should point to tool_envs/{tool}_env
# LD_LIBRARY_PATH should include cuda_env/lib for CUDA JIT tools
# python sys.prefix should match tool env pathIf env vars point to the parent conda env instead of the tool env, see Pattern 2. Fix this before pattern-matching — wrong env isolation causes misleading package-not-found errors.
Match the error in STATUS.txt to patterns in the table above. Read the detailed pattern in PATTERNS.md for full debugging steps and bash examples.
Edit the ejected copy (local fix) or the repo's standalone/setup.sh (contributed fix). For a contributed fix, use defensive patterns (|| true, conditional checks, graceful fallbacks) that fix the current machine without breaking others; a local fix only has to work here.
Local fix: re-run the tool with PROTO_<TOOLKIT>_STANDALONE_DIR set — the env rebuilds from your patched copy; confirm the tool runs.
Contributed fix: rebuild the packaged env and run the tests:
rm -rf tool_envs/{tool}_env
pytest -k "tool_key" --all -sv
pytest --cpu-only --skip-ci
pytest --gpu-only --all # if GPU availableAdd comments explaining what, why, and why it's safe for other platforms.
|| true for operations that might fail on some platformsif [ -d ... ]) before filesystem operations2>/dev/null to suppress expected errorsnotes/tool-environments.mdnotes/tool-environments.md for hardware detectionnotes/tool-environments.md for version mappingnotes/tool-environments.md for TMalign/USalign patternnotes/tool-environments.md for python_version.txtnotes/tool-environments.md for uv_version.txtnotes/tool-environments.md for install_binary.py usagenotes/tool-environments.mdproto_tools/utils/device_manager.py and proto_tools/utils/tool_instance.py docstrings (auto-generated reference pages); see notes/tool-environments.md for to_device() protocolnotes/tool-environments.md for get_subprocess_device_env()© evo-design, 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 1 other file in .claude/skills/fix-env of evo-design/proto-tools.
Open the folder on GitHubat commit 64363bd
Fix Env 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 |
|---|---|---|---|---|---|---|
| Fix Env this skillevo-design/proto-tools | 135 | — | ~2.5k | Automated safety check: Notes | MIT | |
| Ako4allTongmingLAIC/AKO4ALL | 369 | — | ~4k | Automated safety check: Pass | MIT | |
| Paddle Op DevPaddlePaddle/Paddle | 24k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Migrate Workflow Ec2 To Osdcpytorch/test-infra | 113 | — | ~2k | Automated safety check: Pass | Custom licence | |
| Hyperpod Version Checkerawslabs/agent-plugins | 915 | 1 repos | ~910 | Automated safety check: Pass | Apache-2.0 | |
| Quark Env Preflightamd/Quark | 181 | — | ~1.4k | Automated safety check: Pass | MIT |
TongmingLAIC/AKO4ALL
Drive an agentic loop that iteratively optimizes a GPU kernel for maximum speedup.
PaddlePaddle/Paddle
PaddlePaddle (飞桨) C++ 算子开发指南。提供从 YAML 配置、InferMeta 函数、Kernel 实现、Python API 封装、单元测试到编译验证的完整算子开发流程指导。在以下场景使用此 skill:(1) 为 Paddle 框架新增 C++ 算子 (2) 修改或调试已有 Paddle 算子 (3) 编写算子的 YAML…
pytorch/test-infra
Step-by-step playbook for migrating a pytorch/pytorch .github/workflows/.yml from EC2 to OSDC (ARC) runners — covers both dial-up and 100% opt-in patterns, with the inputs that must be plumbed…
awslabs/agent-plugins
Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia)…
amd/Quark
Collect and normalize environment facts (OS, Python, GPU, CUDA/ROCm, container state) before Quark installation or PTQ planning.
amd/skills
Benchmarks LLM inference and drives GPU kernel optimization with Magpie.
evo-design/proto-tools
Implements a new bioinformatics tool wrapper in proto-tools using a parallelized agent pipeline.
Categories
Fixes tool environment setup failures in proto-tools, either just for the current machine (eject the tool's standalone dir, patch it, and point PROTO<TOOLKITSTANDALONEDIR at it; works for any…. Fix Env is an agent skill from evo-design/proto-tools. Fixes tool environment setup failures in proto-tools, either just for the current machine (eject the tool's standalone dir, patch it, and point PROTO<TOOLKITSTANDALONEDIR at it; works for any install, including a non-editable pip install) or as a cross-platform fix contributed back to the repo.
Fix Env fits situations like: A tools setup.sh fails; an env breaks after a system update; A standalone venv needs a fix — for your own use.
Run `npx skills add evo-design/proto-tools --skill fix-env -a claude-code`. Or copy the skill folder (.claude/skills/fix-env in evo-design/proto-tools) into .claude/skills/fix-env in your project. Claude Code loads it when a task matches its description.
Run `npx skills add evo-design/proto-tools --skill fix-env -a codex`. Or copy the skill folder (.claude/skills/fix-env in evo-design/proto-tools) into .agents/skills/fix-env 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 evo-design/proto-tools --skill fix-env -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fix-env, .gemini/skills/fix-env, .github/skills/fix-env and .opencode/skills/fix-env in your project.
Going by SKILL.md and its folder, Fix Env needs the command-line tools its instructions call (pytest, python, pip and uv). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Bash, Glob, Grep.
SKILL.md contains no URLs. Its commands use pip and uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Fix Env is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.5k tokens (SKILL.md is roughly 10k 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 Fix Env: Ako4all (TongmingLAIC/AKO4ALL, 369 stars), Paddle Op Dev (PaddlePaddle/Paddle, 24k stars), Migrate Workflow Ec2 To Osdc (pytorch/test-infra, 113 stars) and Hyperpod Version Checker (awslabs/agent-plugins, 915 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
evo-design (a GitHub organization) maintains it in evo-design/proto-tools, which has 135 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 7, 2026.
Source: evo-design/proto-tools on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.