Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ma-compbio-lab/SkillFoundrynpx agentmods add skills/ma-compbio-lab/skillfoundry/chaospy-uncertainty-propagation-starterWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/chaospy-uncertainty-propagation-starter)<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/chaospy-uncertainty-propagation-starter"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/chaospy-uncertainty-propagation-starter/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/chaospy-uncertainty-propagation-starter"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/chaospy-uncertainty-propagation-starter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00301 |
| Opus 5 | $0.00000 | $0.00151 |
| Sonnet 5 | $0.00000 | $0.00060 |
| Haiku 4.5 | $0.00000 | $0.00030 |
Grade A, and why
chaospy-uncertainty-propagation-starter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Chaospy Uncertainty Propagation Starter
Use this skill to run a deterministic uncertainty-propagation toy workflow with Chaospy and inspect a machine-readable summary of the propagated response distribution.
What This Skill Does
- defines a small two-parameter input distribution with one Gaussian and one uniform variable
- builds a polynomial-chaos surrogate with quadrature
- reports the propagated mean, standard deviation, percentiles, and representative quadrature evaluations
When To Use It
- when you need a runnable
uncertainty-aware-simulationstarter - when you want a local Chaospy example before wiring in an expensive scientific simulator
- when you need deterministic uncertainty summaries for repository tests
Run
./slurm/envs/numerics/bin/python skills/scientific-computing-and-numerical-methods/chaospy-uncertainty-propagation-starter/scripts/run_chaospy_uncertainty_propagation.py \
--config skills/scientific-computing-and-numerical-methods/chaospy-uncertainty-propagation-starter/examples/toy_parameters.json \
--out scratch/numerics/chaospy_uncertainty_summary.json
Notes
- The response model is intentionally synthetic; it exists to verify the UQ loop, not to stand in for a real simulator.
- The script uses deterministic quadrature rather than random sampling so the summary is stable across reruns.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 29 lines · 0 tokens per session scan A 658197aca385
chaospy-uncertainty-propagation-starter is a skill published in the GitHub repository ma-compbio-lab/SkillFoundry (39 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 301 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
arxiv-search
Skill "arxiv-search" from TaewoooPark/MagLab, covering category scoping, query families (3–6 per topic), version and doi resolution, tier classification and output contract.
revision-letter
Point-by-point peer-review response letter workflow for journal resubmission. Invokes RevisionLetterAgent to quote each reviewer comment verbatim, draft a response, and add a change-location marker. Outputs carry HUMAN REVIEW REQUIRED; no auto-send. A DOI or manuscript location is required for every factual response…
literature-search
Broad literature search for magnetism & spintronics — OpenAlex REST query strategy, query family generation, tier classification, and evidencematrix construction (§14.3·§14.7). Activated by the maglab lit search command and the research orchestration search-scout agent.
physics-oracle
Use when validating the dimensional, range, and conservation-law plausibility of magnetic physics quantities, or when performing deterministic physics formula calculations and unit conversions. Gilbert damping 0≤α≤1 check, M≤Ms, exchange length and domain wall width calculations, Oe↔A/m↔T·emu/cm³↔A/m·Jij meV↔K…
statistical-experimental-evaluation
Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.
digital-twin-discharge-drafter
Use when drafting patient discharge summaries, creating personalized discharge instructions, simulating post-discharge outcomes, reducing hospital readmissions, or optimizing care transitions. Generates AI-enhanced discharge documentation with digital twin predictions for improved patient safety.