Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sandbaseai/sandbase-skills --skill umap-learngit clone --depth 1 https://github.com/sandbaseai/sandbase-skillsWrote 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/sandbaseai/sandbase-skills/umap-learn)<a href="https://agentmods.dev/skills/sandbaseai/sandbase-skills/umap-learn"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/umap-learn/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/sandbaseai/sandbase-skills/umap-learn"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/umap-learn.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 | $0.00043 | $0.00623 |
| Opus 5.5 | $0.00017 | $0.00249 |
| Sonnet 5.5 | $0.00009 | $0.00125 |
| Haiku 4.5 | $0.00004 | $0.00062 |
Grade A, and why
umap-learn 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UMAP-learn
Use this Skill to produce a bounded, verifiable UMAP-learn outcome. Preserve the user's chosen stack, source material, and authorization boundaries.
Read the SandBase API map only when the task genuinely needs an external data source or generative model.
Workflow
- Inspect the available files, runtime, versions, inputs, and existing conventions before deciding what to change.
- Restate the requested outcome, constraints, acceptance checks, and any assumption that could change the result.
- Produce the smallest complete implementation, analysis, or artifact that satisfies those checks.
- Verify the real output with appropriate tests, previews, calculations, or source comparison; do not infer success from file creation alone.
- Return the deliverable, evidence of validation, material assumptions, and unresolved limitations.
Quality gates
- Inspect shapes, types, units, missing values, sampling, target leakage, and train/test boundaries before modeling or transformation.
- Pin or record relevant library versions, random seeds, parameters, and environment assumptions for reproducibility.
- Validate against a baseline or independent calculation and report diagnostics, uncertainty, failure modes, and resource use.
Focus checks
- Scale features as appropriate, choose metric, neighbors, and minimum distance from the goal, fix seeds for comparison, and never treat a 2D embedding as proof of clusters.
SandBase boundary
Keep the core UMAP-learn work local. Use SandBase only for an explicitly requested external dataset or model inference step that is not part of the local analysis.
- Call
sandbase_discoverwith a short capability query. - Call
sandbase_inspectfor viable candidates and compare the live schema, coverage, limits, output, execution mode, and price. - Prefer a dedicated tool or API the user already has. Send only the minimum necessary data.
- Before any paid call, show the endpoint, important arguments, current unit price, call count, and total estimate or uncertainty, then obtain confirmation.
- Use
sandbase_accountbefore an approved multi-call batch and callsandbase_runonly with current schema-defined arguments. - Poll asynchronous work with
sandbase_run_getusing the same run ID; never resubmit merely because it is pending. - Use
sandbase_runsonly to recover status or reconcile observed cost.
What ships with it
1 file 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.
- 11d ago First seen · 47 lines · 43 tokens per session scan A 1bdfaadcc726
umap-learn is a skill published in the GitHub repository sandbaseai/sandbase-skills (201 stars, last pushed 11d ago), licensed Apache-2.0. It adds 43 tokens to every session and 623 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-27.
Other skills, from other repositories
quantum-vqe
A runnable guide to the variational quantum eigensolver (VQE), a quantum algorithm that estimates the lowest energy of a mathematical system by adjusting a parameterized circuit. It uses Pauli Hamiltonians, a specified circuit form, and SciPy optimization.
quantum-vqls
A runnable guide to the variational quantum linear solver (VQLS), a quantum method that estimates a solution to a system of linear equations by optimizing a parameterized circuit. It uses Qiskit and reports the numerical residual and why optimization stopped.
flask-expert
Expert-level Flask web development, REST APIs, extensions, and production deployment. Use when the user mentions Python, web framework, REST APIs, or Jinja2, or when the task involves Flask Fundamentals or Flask Extensions.
python-expert
Expert-level Python programming with PEP 8 standards and modern best practices. Use when writing Python code, debugging Python issues, explaining Python concepts, or reviewing Python code.
code_runner
Execute Python code snippets safely in an isolated subprocess with timeout protection. Use when: the user asks to run, test, or evaluate Python code, calculate expressions, or prototype logic. NOT for: running shell commands, executing other languages, or code that needs filesystem/network access.
frappe-backend
Frappe backend guidance for Python and backend-adjacent JavaScript surfaces such as client interaction patterns, hooks, APIs, patches, scheduler logic, reports, and server-side review. Use when implementing or reviewing Frappe backend behavior.