Docs Governance
affaan-m/ECC
Route broad documentation-governance requests to existing ECC skills and run an opt-in, read-only audit of mapped documentation roles, links, ADR indexes, and evidence references.
Run a governed same-model UMA/fairchem adsorption-energy screen for an explicit slab, isolated adsorbate, and adsorbed slab triplet, including provenance checks, composition/cell validation…
Custom licenceAuto-check passed
Install Uma Adsorption Energy Screening
$ npx skills add Tai609/NebulaMat --skill uma-adsorption-energy-screening -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Tai609/NebulaMat uma-adsorption-energy-screening --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/Tai609/NebulaMat.git skills-src && mkdir -p .claude/skills && cp -r skills-src/runtime/skills-bundle/uma-adsorption-energy-screening .claude/skills/uma-adsorption-energy-screening && 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 "uma-adsorption-energy-screening" agent skill from https://github.com/Tai609/NebulaMat/tree/main/runtime/skills-bundle/uma-adsorption-energy-screening into .claude/skills/uma-adsorption-energy-screening/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uma-adsorption-energy-screening", 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/Tai609/NebulaMat/tree/main/runtime/skills-bundle/uma-adsorption-energy-screeningType 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 Tai609/NebulaMat --skill uma-adsorption-energy-screening -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Tai609/NebulaMat uma-adsorption-energy-screening --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Tai609/NebulaMat.git skills-src && mkdir -p .agents/skills && cp -r skills-src/runtime/skills-bundle/uma-adsorption-energy-screening .agents/skills/uma-adsorption-energy-screening && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "uma-adsorption-energy-screening" agent skill from https://github.com/Tai609/NebulaMat/tree/main/runtime/skills-bundle/uma-adsorption-energy-screening into .agents/skills/uma-adsorption-energy-screening/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uma-adsorption-energy-screening", 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 Tai609/NebulaMat --skill uma-adsorption-energy-screening -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Tai609/NebulaMat uma-adsorption-energy-screening --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Tai609/NebulaMat.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/runtime/skills-bundle/uma-adsorption-energy-screening .cursor/skills/uma-adsorption-energy-screening && 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 "uma-adsorption-energy-screening" agent skill from https://github.com/Tai609/NebulaMat/tree/main/runtime/skills-bundle/uma-adsorption-energy-screening into .cursor/skills/uma-adsorption-energy-screening/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uma-adsorption-energy-screening", 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/Tai609/NebulaMat.git --path runtime/skills-bundle/uma-adsorption-energy-screening--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 Tai609/NebulaMat --skill uma-adsorption-energy-screening -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Tai609/NebulaMat uma-adsorption-energy-screening --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Tai609/NebulaMat.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/runtime/skills-bundle/uma-adsorption-energy-screening .gemini/skills/uma-adsorption-energy-screening && 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 "uma-adsorption-energy-screening" agent skill from https://github.com/Tai609/NebulaMat/tree/main/runtime/skills-bundle/uma-adsorption-energy-screening into .gemini/skills/uma-adsorption-energy-screening/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uma-adsorption-energy-screening", 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 Tai609/NebulaMat uma-adsorption-energy-screeningInstalls 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 Tai609/NebulaMat --skill uma-adsorption-energy-screening -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Tai609/NebulaMat.git skills-src && mkdir -p .github/skills && cp -r skills-src/runtime/skills-bundle/uma-adsorption-energy-screening .github/skills/uma-adsorption-energy-screening && 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 "uma-adsorption-energy-screening" agent skill from https://github.com/Tai609/NebulaMat/tree/main/runtime/skills-bundle/uma-adsorption-energy-screening into .github/skills/uma-adsorption-energy-screening/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uma-adsorption-energy-screening", 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 Tai609/NebulaMat --skill uma-adsorption-energy-screening -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Tai609/NebulaMat uma-adsorption-energy-screening --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Tai609/NebulaMat.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/runtime/skills-bundle/uma-adsorption-energy-screening .opencode/skills/uma-adsorption-energy-screening && 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 "uma-adsorption-energy-screening" agent skill from https://github.com/Tai609/NebulaMat/tree/main/runtime/skills-bundle/uma-adsorption-energy-screening into .opencode/skills/uma-adsorption-energy-screening/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uma-adsorption-energy-screening", 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
uma-adsorption-energy-screening- GitHub stars
- 100
- Token cost
- ~1.8k tokens
- SKILL.md length
- 799 words
- Files
- 3 (incl. references)
- Skills in repo
- 15
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Run a governed same-model UMA/fairchem adsorption-energy screen for an explicit slab, isolated adsorbate, and adsorbed slab triplet, including provenance checks, composition/cell validation…
- Works in 8 steps: Require an explicit triplet. Obtain… → Normalize the cohort first. Use… → Check surface consistency. Confirm the… → …
- A user asks to calculate adsorption energy
- SKILL.md covers Workflow, Parameter Rules, Tool Contract and Failure Handling
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Uma Adsorption Energy Screening is an agent skill from Tai609/NebulaMat. Run a governed same-model UMA/fairchem adsorption-energy screen for an explicit slab, isolated adsorbate, and adsorbed slab triplet, including provenance checks, composition/cell validation, optional consistent relaxation, sign-convention reporting, and uncertainty limits. Use whenever a user asks to calculate adsorption energy, binding energy, Eads, or UMA/fairchem screening for a surface and adsorbate.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `agents/openai.yaml` and `references/parameter-policy.md`).
The repository describes itself as: NebulaMat scientific materials research workbench.
When your agent uses it
- A user asks to calculate adsorption energy
- UMA/fairchem screening for a surface and adsorbate
Example prompts
- “/uma-adsorption-energy-screening”
Workflow steps
8 steps, taken from the first numbered list in SKILL.md.
- Require an explicit triplet. Obtain three distinct structures: the standardized slab, the isolated adsorbate, and the combined adsorbed…
- Normalize the cohort first. Use standardize_uma_adsorption_structure_set when the three files are not already one passing adsorption-set…
- Check surface consistency. Confirm the slab and adsorbed manifests share the same Miller index and layer count, and retain the surface…
- Choose single-point versus relaxation. Default to relax=false, which preserves the supplied MatterSim-relaxed or manually prepared…
- Use one model, task, and device. Default to logical model uma-s-1p2p1, task oc25, and cuda. Use the pinned local weights when available…
- Run the governed tool. Prefer MCP run_uma_adsorption_energy_screen; use materials-uma-screen only as the project CLI fallback. Write JSON…
- Audit the result. Require status=completed. Record all three input paths and SHA-256 hashes, formulas, atom counts, standardization…
- Interpret conservatively. Under the recorded convention, E_ads < 0 is exothermic and E_ads > 0 is endothermic…
What it can do on your machine
Read from SKILL.md and the folder at commit c906ed5. 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.
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
Uma Adsorption Energy Screening loads about 1.8k tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 110 tokens; SKILL.md has 799 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
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 799 words (~1,833 tokens).
“Treat UMA as an initial machine-learning energy/force screen, not as DFT or a free-energy calculation. Never invent an adsorbate, place it automatically, or turn one uncalibrated adsorption value into an activity, selectivity, stability, or experimental claim.”
- name
- uma-adsorption-energy-screening
Files
SKILL.md and 2 other files (references) in runtime/skills-bundle/uma-adsorption-energy-screening of Tai609/NebulaMat.
- SKILL.md
- agents/openai.yaml
- references/parameter-policy.md
Open the folder on GitHubat commit c906ed5
Compare with similar skills
Uma Adsorption Energy Screening 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 |
|---|---|---|---|---|---|---|
| Uma Adsorption Energy Screening this skillTai609/NebulaMat | 100 | — | ~1.8k | Automated safety check: Pass | Custom licence | |
| Docs Governanceaffaan-m/ECC | 277k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Adsorption EnergyHello-QM/catgo-LRG | 205 | — | ~1.4k | Automated safety check: Pass | AGPL-3.0 | |
| Bio Crispr Screens Screen QcFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | — | ~2.1k | Automated safety check: Pass | None | |
| Energy Procurementaffaan-m/ECC | 277k | 4 repos | ~7.4k | Automated safety check: Pass | Apache-2.0 | |
| Energy Procurementaffaan-m/ECC | 277k | 2 repos | ~2.5k | Automated safety check: Pass | Apache-2.0 |
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Questions about Uma Adsorption Energy Screening
What does Uma Adsorption Energy Screening do?
Run a governed same-model UMA/fairchem adsorption-energy screen for an explicit slab, isolated adsorbate, and adsorbed slab triplet, including provenance checks, composition/cell validation…. Uma Adsorption Energy Screening is an agent skill from Tai609/NebulaMat. Run a governed same-model UMA/fairchem adsorption-energy screen for an explicit slab, isolated adsorbate, and adsorbed slab triplet, including provenance checks, composition/cell validation, optional consistent relaxation, sign-convention reporting, and uncertainty limits.
When should I use Uma Adsorption Energy Screening?
Uma Adsorption Energy Screening fits situations like: A user asks to calculate adsorption energy; UMA/fairchem screening for a surface and adsorbate.
How do I install Uma Adsorption Energy Screening in Claude Code?
Run `npx skills add Tai609/NebulaMat --skill uma-adsorption-energy-screening -a claude-code`. Or copy the skill folder (runtime/skills-bundle/uma-adsorption-energy-screening in Tai609/NebulaMat) into .claude/skills/uma-adsorption-energy-screening in your project. Claude Code loads it when a task matches its description.
How do I install Uma Adsorption Energy Screening in Codex?
Run `npx skills add Tai609/NebulaMat --skill uma-adsorption-energy-screening -a codex`. Or copy the skill folder (runtime/skills-bundle/uma-adsorption-energy-screening in Tai609/NebulaMat) into .agents/skills/uma-adsorption-energy-screening in your project. Codex loads it when a task matches its description.
Can I use Uma Adsorption Energy Screening 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 Tai609/NebulaMat --skill uma-adsorption-energy-screening -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/uma-adsorption-energy-screening, .gemini/skills/uma-adsorption-energy-screening, .github/skills/uma-adsorption-energy-screening and .opencode/skills/uma-adsorption-energy-screening in your project.
What does Uma Adsorption Energy Screening need to run?
SKILL.md names no scripts, command-line tools or credentials: Uma Adsorption Energy Screening is instructions for the agent only.
Does Uma Adsorption Energy Screening 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 Uma Adsorption Energy Screening 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 Uma Adsorption Energy Screening use?
Uma Adsorption Energy Screening has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Uma Adsorption Energy Screening use?
About 1.8k tokens (SKILL.md is roughly 7.3k 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 1.1k tokens, read only when the agent opens those files.
What are the alternatives to Uma Adsorption Energy Screening?
Skills that share tags, products or a category with Uma Adsorption Energy Screening: Docs Governance (affaan-m/ECC, 277k stars), Adsorption Energy (Hello-QM/catgo-LRG, 205 stars), Bio Crispr Screens Screen Qc (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Energy Procurement (affaan-m/ECC, 277k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Uma Adsorption Energy Screening?
Tai609 (a GitHub user) maintains it in Tai609/NebulaMat, which has 100 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on September 6, 2026.
Source: Tai609/NebulaMat on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.