Segment Anything Model Guide
Orchestra-Research/AI-Research-SKILLs
Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
Entry point for creating PINA problems. An agent skill from PINA-org/PINA.
$ npx skills add PINA-org/PINA --skill create-problem -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install PINA-org/PINA create-problem --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/PINA-org/PINA.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.opencode/skills/create-problem .claude/skills/create-problem && 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 "create-problem" agent skill from https://github.com/PINA-org/PINA/tree/master/.opencode/skills/create-problem into .claude/skills/create-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-problem", 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/PINA-org/PINA/tree/master/.opencode/skills/create-problemType 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 PINA-org/PINA --skill create-problem -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install PINA-org/PINA create-problem --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/PINA-org/PINA.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.opencode/skills/create-problem .agents/skills/create-problem && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "create-problem" agent skill from https://github.com/PINA-org/PINA/tree/master/.opencode/skills/create-problem into .agents/skills/create-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-problem", 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 PINA-org/PINA --skill create-problem -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install PINA-org/PINA create-problem --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/PINA-org/PINA.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.opencode/skills/create-problem .cursor/skills/create-problem && 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 "create-problem" agent skill from https://github.com/PINA-org/PINA/tree/master/.opencode/skills/create-problem into .cursor/skills/create-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-problem", 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/PINA-org/PINA.git --path .opencode/skills/create-problem--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 PINA-org/PINA --skill create-problem -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install PINA-org/PINA create-problem --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/PINA-org/PINA.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.opencode/skills/create-problem .gemini/skills/create-problem && 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 "create-problem" agent skill from https://github.com/PINA-org/PINA/tree/master/.opencode/skills/create-problem into .gemini/skills/create-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-problem", 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 PINA-org/PINA create-problemInstalls 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 PINA-org/PINA --skill create-problem -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/PINA-org/PINA.git skills-src && mkdir -p .github/skills && cp -r skills-src/.opencode/skills/create-problem .github/skills/create-problem && 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 "create-problem" agent skill from https://github.com/PINA-org/PINA/tree/master/.opencode/skills/create-problem into .github/skills/create-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-problem", 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 PINA-org/PINA --skill create-problem -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install PINA-org/PINA create-problem --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/PINA-org/PINA.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.opencode/skills/create-problem .opencode/skills/create-problem && 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 "create-problem" agent skill from https://github.com/PINA-org/PINA/tree/master/.opencode/skills/create-problem into .opencode/skills/create-problem/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-problem", 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.
create-problemEntry point for creating PINA problems. An agent skill from PINA-org/PINA.
Create Problem is an agent skill from PINA-org/PINA. Entry point for creating PINA problems. Routes to sub-skills based on problem type (data-driven vs physics-driven), problem class selection, domain setup, equations, conditions, and discretisation.
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: opencode, codex, claude
It sits in AI & LLM Engineering, covering Deep learning. It works with Python. The repository describes itself as: Physics-Informed Neural networks for Advanced modeling. The licence is MIT.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 0cd8afb. 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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
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.
opencode, codex, claude
From compatibility in the SKILL.md frontmatter.
Create Problem loads about 1.9k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 414 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); files beside SKILL.md are not scanned.
The full file from PINA-org/PINA at commit 0cd8afb, republished under its MIT licence (© PINA-org). 414 words, ~1,918 tokens.
.claude/skills/create-problem/SKILL.md (or your agent's skills folder).[!IMPORTANT] Read RULES.md before using this skill — it applies to all skills.
This is the entry-point skill for building PINA Problems. It selects the problem type and routes to sub-skills for the deep work.
A PINA problem is a Python class that inherits from one or more of:
| Base class | When to use |
|---|---|
BaseProblem | Data-driven (supervised / unsupervised) problems |
SpatialProblem | PDE/ODE depending only on spatial coordinates |
TimeDependentProblem | Problems with a time dimension |
ParametricProblem | Problems with parametric dependencies |
InverseProblem | Problems with unknown physical parameters |
Problems can mix base classes via multiple inheritance (e.g.,
SpatialProblem + TimeDependentProblem for space-time PDEs).
| Base class(es) | Must define |
|---|---|
BaseProblem | input_variables, output_variables, conditions with input/target |
SpatialProblem | output_variables, spatial_domain, conditions |
TimeDependentProblem | output_variables, temporal_domain, conditions |
ParametricProblem | output_variables, parameter_domain, conditions |
InverseProblem | output_variables, unknown_parameter_domain, conditions |
Is your problem data-driven (you have input/target data) or physics-driven (you have a PDE/ODE with known equations)?
Data-driven → use BaseProblem. Load the condition-setup sub-skill for
data types and conditions.
Physics-driven → go to Step 2.
If the user is unsure:
(input, target), model learns to map one to the other.Does your problem involve:
- Spatial variables only (e.g.
x,y,z)? →SpatialProblem- Time as well? → also inherit
TimeDependentProblem- Parameters that vary? → also inherit
ParametricProblem- Unknown parameters to be discovered? → also inherit
InverseProblem
Choose the base class(es) that match.
spatial_domain,
temporal_domain, etc.).import torch
from pina import Condition, LabelTensor
from pina.problem import BaseProblem
input_data = LabelTensor(torch.randn(100, 1), "x")
target_data = LabelTensor(torch.randn(100, 1), "y")
class MySupervisedProblem(BaseProblem):
input_variables = ["x"]
output_variables = ["y"]
conditions = {
"data": Condition(input=input_data, target=target_data),
}
problem = MySupervisedProblem()from pina.problem import SpatialProblem
from pina.domain import CartesianDomain
from pina import Condition
from pina.equation import Equation
from pina.equation.zoo import FixedValue
class MySpatialProblem(SpatialProblem):
output_variables = ["u"]
spatial_domain = CartesianDomain({"x": [0, 1], "y": [0, 1]})
domains = {
"D": spatial_domain,
"boundary": spatial_domain.partial(),
}
conditions = {
"boundary": Condition(domain="boundary", equation=FixedValue(0.0)),
"D": Condition(domain="D", equation=Equation(my_pde)),
}
def solution(self, pts):
...from pina.problem import SpatialProblem, TimeDependentProblem
from pina.domain import CartesianDomain
from pina import Condition
from pina.equation import Equation
from pina.equation.zoo import FixedValue
class MySpaceTimeProblem(TimeDependentProblem, SpatialProblem):
output_variables = ["u"]
spatial_domain = CartesianDomain({"x": [-1, 1]})
temporal_domain = CartesianDomain({"t": [0, 1]})
domains = {
"D": spatial_domain.update(temporal_domain),
"ic": spatial_domain.update(CartesianDomain({"t": 0})),
"boundary": spatial_domain.partial().update(temporal_domain),
}
conditions = {
"boundary": Condition(domain="boundary", equation=FixedValue(0.0)),
"ic": Condition(domain="ic", equation=Equation(initial_cond)),
"D": Condition(domain="D", equation=Equation(my_pde)),
}
def solution(self, pts):
...from pina.problem import SpatialProblem, InverseProblem
from pina.domain import CartesianDomain
from pina import Condition
from pina.equation import Equation
from pina.equation.zoo import FixedValue
class MyInverseProblem(SpatialProblem, InverseProblem):
output_variables = ["u"]
spatial_domain = CartesianDomain({"x": [-2, 2], "y": [-2, 2]})
unknown_parameter_domain = CartesianDomain({"mu1": [-1, 1], "mu2": [-1, 1]})
domains = {
"D": spatial_domain,
"boundary": spatial_domain.partial(),
}
conditions = {
"boundary": Condition(domain="boundary", equation=FixedValue(0.0)),
"D": Condition(domain="D", equation=Equation(laplace_equation)),
"data": Condition(input=input_data, target=target_data),
}from pina.problem import SpatialProblem, ParametricProblem
from pina.domain import CartesianDomain
class MyParametricProblem(SpatialProblem, ParametricProblem):
output_variables = ["u"]
spatial_domain = CartesianDomain({"x": [0, 1]})
parameter_domain = CartesianDomain({"mu": [0.5, 2.0]})
domains = {
"D": spatial_domain.update(parameter_domain),
...
}
...output_variables is a list[str] naming the model outputsinput_variables is a list[str] naming the inputsspatial_domain, temporal_domain, parameter_domain, or
unknown_parameter_domain as appropriate)domains dict contains a key for every domain referenced in conditionsproblem.discretise_domain(n=..., mode=..., domains=...) called for
each physics domainproblem.are_all_domains_discretised is True after discretisationsolution(pts) method defined with correct analytical
solution returning LabelTensor with output_variables labels© PINA-org, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .opencode/skills/create-problem of PINA-org/PINA.
Open the folder on GitHubat commit 0cd8afb
Create Problem 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 |
|---|---|---|---|---|---|---|
| Create Problem this skillPINA-org/PINA | 797 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.3k | Automated safety check: Pass | MIT | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Paddle Design DistributedPaddlePaddle/Paddle | 24k | — | ~660 | Automated safety check: Pass | Apache-2.0 | |
| Onnxtxtonnx/onnx | 22k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Scaffold Examplecomet-ml/comet-examples | 175 | — | ~1k | Automated safety check: Pass | None |
Orchestra-Research/AI-Research-SKILLs
Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
Orchestra-Research/AI-Research-SKILLs
Explains OpenAI's CLIP model for zero-shot image classification, image-text similarity, semantic image search and content moderation, with install steps and code patterns.
PaddlePaddle/Paddle
A skill your agent uses when working with Paddle's distributed training system: understanding parallelism strategies (DP, ZeRO, TP, PP, SP), semi-automatic parallel with ProcessMesh + shardtensor…
onnx/onnx
Read or write ONNX text format ("onnxtxt"). An agent skill from onnx/onnx.
comet-ml/comet-examples
Scaffold a brand-new Comet example in this repo from the canonical template under templates/integration-example/.
Orchestra-Research/AI-Research-SKILLs
Shows how to organize PyTorch training with Lightning's LightningModule and Trainer, covering validation, DDP, callbacks and learning-rate scheduling.
PINA-org/PINA
Create, modify, and improve PINA skills. An agent skill from PINA-org/PINA.
PINA-org/PINA
Orchestrates a complete session from problem definition through trained solver.
PINA-org/PINA
Guides users through selecting, configuring, or creating a PyTorch model for a PINA problem.
PINA-org/PINA
Guides users through selecting the right PINA solver for their problem, or creating a custom solver when no built-in fits.
PINA-org/PINA
Guides users through configuring the PINA Trainer for solver training, including batching strategy, data splitting, and Lightning options.
PINA-org/PINA
Audits SKILL.md files in this repo's skills directory for references to functions, classes, or modules (mentioned by name in prose, e.g.
Works with
Categories
Entry point for creating PINA problems. An agent skill from PINA-org/PINA. Create Problem is an agent skill from PINA-org/PINA. Entry point for creating PINA problems.
Create Problem fits situations like: tasks that involve Deep learning.
Run `npx skills add PINA-org/PINA --skill create-problem -a claude-code`. Or copy the skill folder (.opencode/skills/create-problem in PINA-org/PINA) into .claude/skills/create-problem in your project. Claude Code loads it when a task matches its description.
Run `npx skills add PINA-org/PINA --skill create-problem -a codex`. Or copy the skill folder (.opencode/skills/create-problem in PINA-org/PINA) into .agents/skills/create-problem 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 PINA-org/PINA --skill create-problem -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/create-problem, .gemini/skills/create-problem, .github/skills/create-problem and .opencode/skills/create-problem in your project.
SKILL.md names no scripts, command-line tools or credentials: Create Problem is instructions for the agent only. Our summary lists: Python 3. Compatibility (from SKILL.md): opencode, codex, claude.
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. Review the folder before installing.
Create Problem is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.9k tokens (SKILL.md is roughly 7.7k 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 Create Problem: Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Paddle Design Distributed (PaddlePaddle/Paddle, 24k stars) and Onnxtxt (onnx/onnx, 22k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
PINA-org (a GitHub organization) maintains it in PINA-org/PINA, which has 797 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on October 6, 2026.
Source: PINA-org/PINA on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.