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 torch-geometricgit 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/torch-geometric)<a href="https://agentmods.dev/skills/sandbaseai/sandbase-skills/torch-geometric"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/torch-geometric/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/torch-geometric"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/torch-geometric.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 | $0.00040 | $0.00613 |
| Opus 5.5 | $0.00016 | $0.00245 |
| Sonnet 5.5 | $0.00008 | $0.00123 |
| Haiku 4.5 | $0.00004 | $0.00061 |
Grade A, and why
torch-geometric 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 19d 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.
This is a copy
64% identical to accessibility-review — 21 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
PyTorch Geometric
Use this Skill to produce a bounded, verifiable PyTorch Geometric 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
- Validate graph direction, feature and label shapes, batching, split leakage, and negative sampling; compare against a simple baseline before scaling message passing.
SandBase boundary
Keep the core PyTorch Geometric 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.
- 19d ago First seen · 47 lines · 40 tokens per session scan A 3c04dc84bdc7
torch-geometric is a skill published in the GitHub repository sandbaseai/sandbase-skills (201 stars, last pushed 20d ago), licensed Apache-2.0. It adds 40 tokens to every session and 613 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 64% identical to accessibility-review, differing in 21 lines, and is treated as a copy.
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