MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Operate or inspect live ANSYS Workbench Mechanical structural simulations through the configured MCP, including model setup, materials, connections, loads, mesh, solve, convergence, validation, and…
$ npx skills add Cai-aa/CAE-Agent-Hub --skill ansys-structural-workbench -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Cai-aa/CAE-Agent-Hub ansys-structural-workbench --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/Cai-aa/CAE-Agent-Hub.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Skill/Ansys/ansys-structural-workbench .claude/skills/ansys-structural-workbench && 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 "ansys-structural-workbench" agent skill from https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/Ansys/ansys-structural-workbench into .claude/skills/ansys-structural-workbench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ansys-structural-workbench", 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/Cai-aa/CAE-Agent-Hub/tree/main/Skill/Ansys/ansys-structural-workbenchType 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 Cai-aa/CAE-Agent-Hub --skill ansys-structural-workbench -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Cai-aa/CAE-Agent-Hub ansys-structural-workbench --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Cai-aa/CAE-Agent-Hub.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Skill/Ansys/ansys-structural-workbench .agents/skills/ansys-structural-workbench && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ansys-structural-workbench" agent skill from https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/Ansys/ansys-structural-workbench into .agents/skills/ansys-structural-workbench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ansys-structural-workbench", 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 Cai-aa/CAE-Agent-Hub --skill ansys-structural-workbench -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Cai-aa/CAE-Agent-Hub ansys-structural-workbench --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Cai-aa/CAE-Agent-Hub.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Skill/Ansys/ansys-structural-workbench .cursor/skills/ansys-structural-workbench && 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 "ansys-structural-workbench" agent skill from https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/Ansys/ansys-structural-workbench into .cursor/skills/ansys-structural-workbench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ansys-structural-workbench", 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/Cai-aa/CAE-Agent-Hub.git --path Skill/Ansys/ansys-structural-workbench--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 Cai-aa/CAE-Agent-Hub --skill ansys-structural-workbench -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Cai-aa/CAE-Agent-Hub ansys-structural-workbench --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Cai-aa/CAE-Agent-Hub.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Skill/Ansys/ansys-structural-workbench .gemini/skills/ansys-structural-workbench && 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 "ansys-structural-workbench" agent skill from https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/Ansys/ansys-structural-workbench into .gemini/skills/ansys-structural-workbench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ansys-structural-workbench", 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 Cai-aa/CAE-Agent-Hub ansys-structural-workbenchInstalls 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 Cai-aa/CAE-Agent-Hub --skill ansys-structural-workbench -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Cai-aa/CAE-Agent-Hub.git skills-src && mkdir -p .github/skills && cp -r skills-src/Skill/Ansys/ansys-structural-workbench .github/skills/ansys-structural-workbench && 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 "ansys-structural-workbench" agent skill from https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/Ansys/ansys-structural-workbench into .github/skills/ansys-structural-workbench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ansys-structural-workbench", 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 Cai-aa/CAE-Agent-Hub --skill ansys-structural-workbench -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Cai-aa/CAE-Agent-Hub ansys-structural-workbench --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Cai-aa/CAE-Agent-Hub.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Skill/Ansys/ansys-structural-workbench .opencode/skills/ansys-structural-workbench && 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 "ansys-structural-workbench" agent skill from https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/Ansys/ansys-structural-workbench into .opencode/skills/ansys-structural-workbench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ansys-structural-workbench", 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.
ansys-structural-workbenchOperate or inspect live ANSYS Workbench Mechanical structural simulations through the configured MCP, including model setup, materials, connections, loads, mesh, solve, convergence, validation, and…
Ansys Structural Workbench is an agent skill from Cai-aa/CAE-Agent-Hub. Operate or inspect live ANSYS Workbench Mechanical structural simulations through the configured MCP, including model setup, materials, connections, loads, mesh, solve, convergence, validation, and reporting. Use whenever a user asks Codex to create, configure, inspect, mesh, solve, troubleshoot, or validate a live Workbench structural analysis. Before model decisions or mutation, load ansys-workbench-practical-reference once as the book-distilled router and then the matching narrow reference Skill. Load…
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 35 other files, including scripts, reference files and assets (for example `agents/openai.yaml`, `assets/structural-analysis-report-template.md` and `references/analysis-profiles.md`).
It works with Model Context Protocol. The licence is MIT.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 194ef49. 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 1 file in scripts/, 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.
Ansys Structural Workbench loads about 2.2k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 190 tokens; SKILL.md has 951 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 Cai-aa/CAE-Agent-Hub at commit 194ef49, republished under its MIT licence (© Cai-aa). 951 words, ~2,234 tokens.
.claude/skills/ansys-structural-workbench/SKILL.md (or your agent's skills folder). This skill also uses 32 other files; get the full folder from GitHub.Operate Workbench as an engineering workflow with explicit quality gates. Treat the MCP as the execution adapter and this Skill as the analysis, meshing, validation, and reporting policy.
Before inspecting settings for engineering acceptance, mutating the model, meshing, or solving, load ansys-workbench-practical-reference once and use it to select the physics-specific book-derived guidance. This is a required intake gate, not an optional source lookup.
ansys-workbench-static-modeling-reference.ansys-workbench-static-modeling-reference and ansys-workbench-nonlinear-contact-reference.ansys-workbench-nonlinear-contact-reference for plasticity, large deflection, buckling, stabilization, changing contact state, or nonlinear convergence.The book-derived Skills govern modeling assumptions and physical checks. This Skill governs live MCP actions, evidence, and safe execution. Record which guidance Skills were loaded in the task intake or final report. Do not repeatedly re-route after the applicable set has been loaded.
references/mcp-adapter.md. Require the registered server name ansys-workbench unless the user configured an equivalent alias.references/job-spec.md, including every applicable analysis profile. Use a temporary in-memory object unless a saved spec benefits the task.If the MCP lacks a required capability, use its generic Workbench/Mechanical script-execution tool when available. Otherwise report the exact unsupported gate; do not fabricate completion.
Always read:
references/core-workflow.mdreferences/quality-gates.mdThen read only what applies:
references/analysis-profiles.mdreferences/mesh-orchestration.md and references/mesh-profiles.mdreferences/mesh-closed-loop.mdreferences/solver-diagnostics.mdreferences/example-clevis-pin.jsonCAPABILITY_CHECK: prove that the MCP can inspect, modify, mesh, solve, query results, and export the evidence required by the selected profile set.INTAKE: normalize model source, units, materials, analysis profile, outputs, and acceptance criteria.MODEL_INSPECTION: inventory bodies, dimensions, materials, named selections, connections, analyses, loads, supports, and existing results.MODEL_SETUP: create only missing or authorized objects. Prefer existing named selections; otherwise use validated geometry predicates.MODEL_GATE: verify entity counts, geometry properties, material coverage, connection scoping, units, load directions, and rigid-body restraint.MESH_PLAN: classify bodies and critical regions before generating mesh.MESH_GATE: generate, extract normalized metrics and worst-element regions, run recommend_mesh_repairs.py, apply at most the configured repair limit through the MCP, regenerate, and reject unresolved hard failures.PRE_SOLVE_GATE: inspect initial contact state where applicable and verify load-step tables and solution controls.SOLVE: solve and retain solver output, warnings, errors, substep history, and final converged time.SOLVER_GATE: reject incomplete or unconverged final states even if the UI says Completed.RESULT_EXTRACTION: extract scoped engineering quantities, reactions, energy, contact quantities, modal/buckling values, mesh statistics, and evidence images.VALIDATION_GATE: run deterministic checks and compare with analytical, test, or user-defined criteria when available.CONVERGENCE_GATE: compare at least the final two mesh levels for every configured metric. Do not use a singular peak stress as the only metric.REPORT: state pass, warning, blocked, or unsupported; include assumptions, evidence paths, limitations, and unresolved warnings.Each gate must produce a structured status and evidence. Do not continue past a hard failure merely to create a report.
Run with a known-good Python interpreter:
python scripts/validate_job_spec.py job.json
python scripts/evaluate_mesh_quality.py mesh-quality.json
python scripts/recommend_mesh_repairs.py mesh-loop.json
python scripts/validate_results.py analysis-results.json
python scripts/validate_contact_results.py contact-results.json
python scripts/evaluate_mesh_convergence.py convergence.json
python scripts/normalize_units.py quantities.json
python scripts/calculate_reference_values.py references.json
python scripts/build_analysis_report.py report-data.json output-report.mdThe scripts emit JSON and return exit code 0 for pass, 1 for warning/fail, and 2 for invalid input. Read their output; do not replace it with qualitative judgment.
For a live mesh loop, prepend a JSON-compatible REQUEST object to scripts/mechanical_mesh_bridge.py and send the complete code through workbench_queue_execute_python_tool. Use probe_session, mesh_snapshot, apply_mesh_updates, or generate_mesh. Parse the ANSYS_STRUCTURAL_JSON: stdout marker. Supply exact existing object names and typed property updates; reject ambiguous names and unsupported metric properties.
Return the live project/system analyzed, execution status, assumptions, model and mesh summary, solver diagnostics, validation matrix, convergence table, result artifacts, and engineering limitations. Match the user's language.
© Cai-aa, 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 32 other files (scripts, references, assets) in Skill/Ansys/ansys-structural-workbench of Cai-aa/CAE-Agent-Hub.
Open the folder on GitHubat commit 194ef49
Ansys Structural Workbench 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 |
|---|---|---|---|---|---|---|
| Ansys Structural Workbench this skillCai-aa/CAE-Agent-Hub | 998 | — | ~2.2k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 64 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 5 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Figma use_figma Plugin API Ruleswarpdotdev/warp | 65k | 4 repos | ~4.4k | Automated safety check: Pass | AGPL-3.0 | |
| Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills | 8.4k | 6 repos | ~3.2k | Automated safety check: Notes | Apache-2.0 |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
shareAI-lab/learn-claude-code
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
anthropics/claude-plugins-official
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warpdotdev/warp
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google-labs-code/stitch-skills
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Works with
Operate or inspect live ANSYS Workbench Mechanical structural simulations through the configured MCP, including model setup, materials, connections, loads, mesh, solve, convergence, validation, and…. Ansys Structural Workbench is an agent skill from Cai-aa/CAE-Agent-Hub. Operate or inspect live ANSYS Workbench Mechanical structural simulations through the configured MCP, including model setup, materials, connections, loads, mesh, solve, convergence, validation, and reporting.
Ansys Structural Workbench fits situations like: A user asks Codex to create; validate a live Workbench structural analysis; theory-only questions; fluent/CFX operation.
Run `npx skills add Cai-aa/CAE-Agent-Hub --skill ansys-structural-workbench -a claude-code`. Or copy the skill folder (Skill/Ansys/ansys-structural-workbench in Cai-aa/CAE-Agent-Hub) into .claude/skills/ansys-structural-workbench in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Cai-aa/CAE-Agent-Hub --skill ansys-structural-workbench -a codex`. Or copy the skill folder (Skill/Ansys/ansys-structural-workbench in Cai-aa/CAE-Agent-Hub) into .agents/skills/ansys-structural-workbench 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 Cai-aa/CAE-Agent-Hub --skill ansys-structural-workbench -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ansys-structural-workbench, .gemini/skills/ansys-structural-workbench, .github/skills/ansys-structural-workbench and .opencode/skills/ansys-structural-workbench in your project.
SKILL.md names no scripts, command-line tools or credentials: Ansys Structural Workbench is instructions for the agent only. 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.
Ansys Structural Workbench is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.3k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Ansys Structural Workbench: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Cai-aa (a GitHub user) maintains it in Cai-aa/CAE-Agent-Hub, which has 998 GitHub stars. The repository holds 57 skills in this directory. The repository was last updated on September 30, 2026.
Source: Cai-aa/CAE-Agent-Hub on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.