Findings & checks · 1 flagged
MEDIUMInstruction telling the agent not to ask for approvalSS-SKILL-INJECT-DONT-ASK-01 · Prompt injection · docs/EVALUATION_METHODOLOGY.md
The text {match} tells the agent to skip the normal "ask the user first" gate. Used adversarially it removes the human-in-the-loop check before destructive or sensitive actions, turning a normally-gated agent into a fire-and-forget executor.
- Delete blanket "don't ask / no need to confirm" directives from the skill.
- If the skill is a genuine autonomous job, restrict the opt-out to a named non-destructive action rather than all actions.
Trace & refs
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Materials Simulation Skills
Give your AI coding agent real expertise in numerical methods, simulation best practices, and computational materials science — so it stops guessing.
New to Agent Skills? A "skill" is a portable folder of instructions + small scripts that an AI coding agent discovers automatically and loads only when relevant. They follow the open Agent Skills standard and work across 20+ tools — Claude Code, Codex, Cursor, Antigravity, GitHub Copilot, and more. Nothing to wire up: drop them in and your agent gets smarter at the task.
What it solves
Simulation engineers repeat the same guidance to AI agents constantly: "Check the CFL number before running," "Use Richardson extrapolation for grid convergence," "Exit code 2 means bad input." General-purpose agents lack the domain knowledge to run reliable numerical simulations without heavy hand-holding — so they pick wrong time steps, miss convergence checks, and misread solver failures.
This project packages that domain knowledge into skills the agent finds and runs on its own:
You: Check if dt=0.001 is stable for my advection problem with v=2.0 m/s and dx=0.01.
Agent: I'll use the numerical-stability skill to check this.
[runs cfl_checker.py --dx 0.01 --dt 0.001 --velocity 2.0 --json]
CFL number = 0.2 (limit: 1.0) -- stable.
Recommended max dt = 0.005 with safety factor 1.0.No prompt engineering, no copy-pasting formulas. 24 skills span numerical methods, simulation workflows, HPC deployment, verification & validation, FAIR data, robustness, and materials ontologies.
Validated, not just curated
Most agent-skill collections are unverified prompt text. These are measured:
- Tested scripts — every skill ships Python CLIs with a pure-function core (1283 tests, Python 3.10–3.12).
- 100% deterministic eval coverage — every eval case runs the script and asserts its exact output, so docs can't silently drift from code. Enforced in CI.
- Measured uplift — skills are evaluated with vs. without the skill across coding-agent CLIs; value is the pass-rate delta, not a claim.
- Cited science + an enforced [protocol](docs/PROTOCOL.md) — rules reference authoritative standards (ASME V&V20, the SchedMD
sbatchspec, CMSO IRIs).
How and why: [evaluation methodology](docs/EVALUATION_METHODOLOGY.md) · [harness](docs/EVAL_HARNESS.md).
Install
The repo follows the open standard, so the ecosystem installers work out of the box:
npx skills add HeshamFS/materials-simulation-skills # any agent → ~/.agents/skills/
gh skill install HeshamFS/materials-simulation-skills --pin v1.0.0 # version-pinnedOr install a curated bundle into a specific agent with the bundled mss CLI:
mss bundles # list bundles
mss install --agent claude --bundle verification-and-validationClaude Code users can also /plugin marketplace add HeshamFS/materials-simulation-skills. Full per-agent guide → [docs/INSTALL.md](docs/INSTALL.md).
Use it
Once installed, just describe your task — the agent picks the right skill, runs the script, and interprets the result (as in the example above). You can also name a skill explicitly:
Use convergence-study to check if my mesh is in the asymptotic range:
h = 0.4, 0.2, 0.1 gave stress = 98.5, 99.6, 99.9 MPa.Browse the full catalog in [docs/SKILLS.md](docs/SKILLS.md) (or the machine-readable skills_index.json).
Try it locally / develop:
git clone https://github.com/HeshamFS/materials-simulation-skills.git
cd materials-simulation-skills && pip install -e ".[dev]"
mss list # list skills
mss run numerical-stability cfl_checker -- --dx 0.01 --dt 0.001 --velocity 2.0 --json
mss eval # run the deterministic eval gate
python -m pytest tests/ # full test suiteWhat's inside
24 skills across 8 categories (one-line summary; full tables in docs/SKILLS.md):
| Category | Skills | Focus |
|---|---|---|
| Core Numerical | 8 | stability, time-stepping, meshing, convergence, integration, differentiation, linear/nonlinear solvers |
| Simulation Workflow | 7 | validation, DOE/optimization, orchestration, post-processing, profiling, workflow mapping, MD analysis |
| HPC Deployment | 2 | SLURM script generation, runtime diagnosis |
| Verification & Validation | 1 | manufactured solutions, benchmarks, pass/fail reports |
| Data Management | 1 | FAIR reproducibility packaging |
| Robustness | 1 | cross-code failure triage & retry ladders |
| Ontology | 3 | CMSO/ASMO exploration, mapping, validation |
| Meta | 1 | skill-evaluator — evaluate any skill across any agent CLI |
Quality is gated in CI: spec-valid frontmatter, deterministic script_checks, the standardized Security section, and a fresh index — see [docs/PROTOCOL.md](docs/PROTOCOL.md) and [docs/SECURITY.md](docs/SECURITY.md).
Contributing
This is an open project and contributions are very welcome — a new skill, a fix, docs, or just an idea.
- Have an idea or a skill to propose? Open an issue (there are templates for bug reports and skill proposals) — early ideas are great even without code.
- Want to build a skill? [CONTRIBUTING.md](CONTRIBUTING.md) has a step-by-step guide, script/test templates, the skill taxonomy, and open categories. The bar that keeps this project trustworthy: a skill ships a tested script + an eval case with a deterministic `script_check`, and passes
mss validate+mss eval. - Where it's headed: the [ROADMAP.md](ROADMAP.md) — growing toward materials-physics and code-interface skills (LAMMPS, DFT) and a richer skill protocol.
Documentation
| Doc | What's in it |
|---|---|
| docs/SKILLS.md | Full skill catalog, how skills work, repo layout |
| docs/INSTALL.md | Install for every supported agent |
| docs/EVALUATION_METHODOLOGY.md | How skills are evaluated, with results |
| docs/EVAL_HARNESS.md | The three-layer evaluation harness |
| docs/PROTOCOL.md | The Materials Simulation Skill Protocol |
| docs/SECURITY.md | Safeguards and security tiers |
| CONTRIBUTING.md · ROADMAP.md | Contributing guide · direction |
Star History
<a href="https://www.star-history.com/?repos=HeshamFS%2Fmaterials-simulation-skills&type=date&legend=top-left"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=HeshamFS/materials-simulation-skills&type=date&theme=dark&legend=top-left" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=HeshamFS/materials-simulation-skills&type=date&legend=top-left" /> <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=HeshamFS/materials-simulation-skills&type=date&legend=top-left" /> </picture> </a>
License
Acknowledgements
- Agent Skills standard — open specification for portable agent capabilities
- Anthropic — original developer of the Agent Skills format
- agentskills/agentskills — reference implementation and validation library