Wp Performance Review
elvismdev/claude-wordpress-skills
WordPress performance code review and optimization analysis.
Run automated Manning's n calibration against observed water surface elevations using Bayesian optimization or Nelder-Mead.
Install Hmt Calibrate
$ npx skills add psu-efd/pyHMT2D --skill hmt-calibrate -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install psu-efd/pyHMT2D hmt-calibrate --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/psu-efd/pyHMT2D.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/hmt-calibrate .claude/skills/hmt-calibrate && 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 "hmt-calibrate" agent skill from https://github.com/psu-efd/pyHMT2D/tree/main/.agents/skills/hmt-calibrate into .claude/skills/hmt-calibrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hmt-calibrate", 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/psu-efd/pyHMT2D/tree/main/.agents/skills/hmt-calibrateType 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 psu-efd/pyHMT2D --skill hmt-calibrate -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install psu-efd/pyHMT2D hmt-calibrate --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/psu-efd/pyHMT2D.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/hmt-calibrate .agents/skills/hmt-calibrate && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hmt-calibrate" agent skill from https://github.com/psu-efd/pyHMT2D/tree/main/.agents/skills/hmt-calibrate into .agents/skills/hmt-calibrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hmt-calibrate", 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 psu-efd/pyHMT2D --skill hmt-calibrate -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install psu-efd/pyHMT2D hmt-calibrate --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/psu-efd/pyHMT2D.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/hmt-calibrate .cursor/skills/hmt-calibrate && 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 "hmt-calibrate" agent skill from https://github.com/psu-efd/pyHMT2D/tree/main/.agents/skills/hmt-calibrate into .cursor/skills/hmt-calibrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hmt-calibrate", 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/psu-efd/pyHMT2D.git --path .agents/skills/hmt-calibrate--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 psu-efd/pyHMT2D --skill hmt-calibrate -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install psu-efd/pyHMT2D hmt-calibrate --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/psu-efd/pyHMT2D.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/hmt-calibrate .gemini/skills/hmt-calibrate && 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 "hmt-calibrate" agent skill from https://github.com/psu-efd/pyHMT2D/tree/main/.agents/skills/hmt-calibrate into .gemini/skills/hmt-calibrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hmt-calibrate", 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 psu-efd/pyHMT2D hmt-calibrateInstalls 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 psu-efd/pyHMT2D --skill hmt-calibrate -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/psu-efd/pyHMT2D.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/hmt-calibrate .github/skills/hmt-calibrate && 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 "hmt-calibrate" agent skill from https://github.com/psu-efd/pyHMT2D/tree/main/.agents/skills/hmt-calibrate into .github/skills/hmt-calibrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hmt-calibrate", 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 psu-efd/pyHMT2D --skill hmt-calibrate -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install psu-efd/pyHMT2D hmt-calibrate --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/psu-efd/pyHMT2D.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/hmt-calibrate .opencode/skills/hmt-calibrate && 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 "hmt-calibrate" agent skill from https://github.com/psu-efd/pyHMT2D/tree/main/.agents/skills/hmt-calibrate into .opencode/skills/hmt-calibrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hmt-calibrate", 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.
Facts
- Skill name
hmt-calibrate- GitHub stars
- 133
- Token cost
- ~597 tokens
- SKILL.md length
- 201 words
- Files
- 1
- Skills in repo
- 9
- Repo updated
- First seen
- Licence
- MIT
At a glance
Run automated Manning's n calibration against observed water surface elevations using Bayesian optimization or Nelder-Mead.
- Works in 9 steps: Check that a project is open. → Validate the observation file. → Show available materials. → …
- Wants to calibrate
- SKILL.md covers Prerequisites and Steps
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Hmt Calibrate is an agent skill from psu-efd/pyHMT2D. Run automated Manning's n calibration against observed water surface elevations using Bayesian optimization or Nelder-Mead. Use this when the user wants to calibrate, optimize, or fit model parameters to observations.
Its SKILL.md is about 600 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Business, Finance & HR, covering Performance reviews. The repository describes itself as: Python Hydraulic Modeling Tools - 2D. The licence is MIT.
When your agent uses it
- Wants to calibrate
- Fit model parameters to observations
Example prompts
- “/hmt-calibrate”
Workflow steps
9 steps, taken from the first numbered list in SKILL.md.
- Check that a project is open.
- Validate the observation file.
- Show available materials.
- Ask the user for calibration parameters. For each material to calibrate
- Build validated parameter specifications.
- Optional: single test evaluation at initial values to verify the setup.
- Ask for settings: number of iterations (default: automatic) and method ("gp" or "nelder-mead").
- Run the calibration.
- Report results: best parameter values table, best RMSE, iterations completed, history CSV path.
What it can do on your machine
Read from SKILL.md and the folder at commit 57645ff. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).
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
Hmt Calibrate loads about 597 tokens when it runs. Until then it costs about 58 tokens; SKILL.md has 201 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.
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 psu-efd/pyHMT2D at commit 57645ff, republished under its MIT licence (© psu-efd). 201 words, ~597 tokens.
Download SKILL.mdSave it as
.claude/skills/hmt-calibrate/SKILL.md (or your agent's skills folder).- name
- hmt-calibrate
- description
- Run automated Manning's n calibration against observed water surface elevations using Bayesian optimization or Nelder-Mead. Use this when the user wants to calibrate, optimize, or fit model parameters to observations.
Run automated Manning's n calibration against observed water surface elevations.
Prerequisites
- Project opened from a
base_case/subdirectory - Observation CSV: columns
x, y, wseorname, x, y, wse(comment lines start with#) - Solver paths configured in
hmt_config.json
Steps
Check that a project is open. If not, ask the user to run
/hmt-openpointing tobase_case/<project_file>.Validate the observation file. Ask the user for the observation CSV path (e.g.,
HWMs.dat).bash hmt-cli check_observation_format --args '{"csv_file": "<path>"}'Show the preview table. On error: report and ask for a corrected path.
Show available materials.
bash hmt-cli get_materialsAsk the user for calibration parameters. For each material to calibrate:
- Material name, minimum n, maximum n, optional initial guess
Build validated parameter specifications.
bash hmt-cli build_param_specs --args '{"specs": [ {"type": "manning_n", "material_name": "<name>", "min": <min>, "max": <max>, "initial": <init>}, ... ]}'Show the specs and confirm with the user.
Optional: single test evaluation at initial values to verify the setup.
bash hmt-cli evaluate_parameters --args '{"param_specs": <specs_with_initial_values>, "observation_csv": "<path>"}'If RMSE = 1e6, the simulation failed — check
pyHMT2D.logbefore proceeding.Ask for settings: number of iterations (default: automatic) and method (
"gp"or"nelder-mead").Run the calibration.
bash hmt-cli run_calibration --args '{"param_specs": <specs>, "observation_csv": "<path>", "n_iterations": <N>, "method": "gp"}'Periodically show progress while waiting:
bash tail -20 calib_progress.logReport results: best parameter values table, best RMSE, iterations completed, history CSV path.
Troubleshooting:
- RMSE = 1,000,000 → check
pyHMT2D.logfor solver errors - Material not found → name must match exactly; copy from
hmt-cli get_materialsoutput
© psu-efd, 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 .agents/skills/hmt-calibrate of psu-efd/pyHMT2D.
Open the folder on GitHubat commit 57645ff
Compare with similar skills
Hmt Calibrate 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 |
|---|---|---|---|---|---|---|
| Hmt Calibrate this skillpsu-efd/pyHMT2D | 133 | — | ~597 | Automated safety check: Pass | MIT | |
| Wp Performance Reviewelvismdev/claude-wordpress-skills | 235 | 1 repos | ~4.5k | Automated safety check: Pass | MIT | |
| Align Humanagentscope-ai/OpenJudge | 870 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Run Mv Hoi Reconstructionnvidia-isaac/video_to_data | 856 | — | ~1.5k | Automated safety check: Pass | Custom licence | |
| Company Analysiszhu1090093659/dsh-trading | 234 | — | ~4.2k | Automated safety check: Pass | Custom licence | |
| Windbg Diagnostic Methodmicrosoft/win-dev-skills | 465 | — | ~1.9k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Hmt Calibrate
What does Hmt Calibrate do?
Run automated Manning's n calibration against observed water surface elevations using Bayesian optimization or Nelder-Mead. Hmt Calibrate is an agent skill from psu-efd/pyHMT2D. Run automated Manning's n calibration against observed water surface elevations using Bayesian optimization or Nelder-Mead.
When should I use Hmt Calibrate?
Hmt Calibrate fits situations like: wants to calibrate; fit model parameters to observations.
How do I install Hmt Calibrate in Claude Code?
Run `npx skills add psu-efd/pyHMT2D --skill hmt-calibrate -a claude-code`. Or copy the skill folder (.agents/skills/hmt-calibrate in psu-efd/pyHMT2D) into .claude/skills/hmt-calibrate in your project. Claude Code loads it when a task matches its description.
How do I install Hmt Calibrate in Codex?
Run `npx skills add psu-efd/pyHMT2D --skill hmt-calibrate -a codex`. Or copy the skill folder (.agents/skills/hmt-calibrate in psu-efd/pyHMT2D) into .agents/skills/hmt-calibrate in your project. Codex loads it when a task matches its description.
Can I use Hmt Calibrate 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 psu-efd/pyHMT2D --skill hmt-calibrate -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hmt-calibrate, .gemini/skills/hmt-calibrate, .github/skills/hmt-calibrate and .opencode/skills/hmt-calibrate in your project.
What does Hmt Calibrate need to run?
SKILL.md names no scripts, command-line tools or credentials: Hmt Calibrate is instructions for the agent only.
Does Hmt Calibrate 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 Hmt Calibrate 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 Hmt Calibrate use?
Hmt Calibrate 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 Hmt Calibrate use?
About 597 tokens (SKILL.md is roughly 2.4k 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 Hmt Calibrate?
Skills that share tags, products or a category with Hmt Calibrate: Wp Performance Review (elvismdev/claude-wordpress-skills, 235 stars), Align Human (agentscope-ai/OpenJudge, 870 stars), Run Mv Hoi Reconstruction (nvidia-isaac/video_to_data, 856 stars) and Company Analysis (zhu1090093659/dsh-trading, 234 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Hmt Calibrate?
psu-efd (a GitHub user) maintains it in psu-efd/pyHMT2D, which has 133 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on August 9, 2026.
Source: psu-efd/pyHMT2D on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.