Borrowing it
Nothing to install: this file belongs to volantlabs/vellis. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/volantlabs/vellis/main/.agents/skills/python-component-implementation/SKILL.mdgit clone --depth 1 https://github.com/volantlabs/vellisWrote 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/volantlabs/vellis/python-component-implementation)<a href="https://agentmods.dev/skills/volantlabs/vellis/python-component-implementation"><img src="https://agentmods.dev/badge/skills/volantlabs/vellis/python-component-implementation/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/volantlabs/vellis/python-component-implementation"><img src="https://agentmods.dev/badge/skills/volantlabs/vellis/python-component-implementation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.00829 |
| Opus 5.5 | $0.00020 | $0.00332 |
| Sonnet 5.5 | $0.00010 | $0.00166 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
python-component-implementation 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 2mo 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Component Implementation
Use the accepted SysML/KPAR model and its generated component view as the contract. Python is one realization of that language-neutral design.
Judge the realization at modeled boundaries. Matching private structure, algorithms, helper decomposition, or another implementation's incidental behavior is neither required nor preferred. The same accepted model may be realized in another language when it preserves the modeled structure, behavior, state effects, failures, invariants, composition contracts, and verification obligations.
Workflow
- Locate the component by stable ID in the repository's canonical model package.
- Resolve the accepted library package version and read its public values, actions, retained collaborator roles, state, required constraints, asserted satisfiers, failures, and verification cases. Treat top-level documentation as explanation, not as an unmodeled obligation.
- Locate the matching concrete realization and confirm its code root and symbol from
@ImplementationBinding; do not expect language bindings on the reusable logical component. - Implement or revise the consumer-facing Python protocol.
- Encapsulate canonical state and invariant enforcement inside the component root.
- Supply action-scoped collaborators and wire retained referential roles only through modeled public boundaries; do not reach through another component's internals.
- Consume structured implementation-neutral conformance objectives when available; add or update black-box tests derived from their subject-compatible verification cases and evidence groups.
- When implementation work forces an observable choice the model does not settle, stop treating it as a private coding decision: propose the language-neutral contract clarification or record realization drift. Incidental helper, library, and representation choices remain private.
- Run the narrowest component tests,
just model-check, and relevant repository checks.
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.
- 2mo ago First seen · 81 lines · 51 tokens per session scan A 4272714490f8
python-component-implementation is a skill published in the GitHub repository volantlabs/vellis (9 stars, last pushed 4d ago), licensed Apache-2.0. It adds 51 tokens to every session and 829 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 2026-08-31.
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