Use stable-pretraining with a coding agent

The library is indexed at Context7, with library ID /galilai-group/stable-pretraining. In a client configured with Context7, ask:

Use stable-pretraining to train SimCLR on my image folders. Fetch its current documentation from Context7, check my installed version, and run a short CPU smoke test before preparing the full experiment.

The repository’s context7.json selects maintained task guides and API docs. Its main branch documentation can describe unreleased features. Do not assume an older installed package contains every API found in the index.

Optional skill and starter

The portable skill lives at skills/stable-pretraining/SKILL.md. Install that directory using your agent client’s skill-installation mechanism. It contains usage guidance and links, not executable installation hooks. Only clients with the skill installed or explicitly provided can discover it.

The starter includes a project-local AGENTS.md. Copy it only into a project where you want the agent to use stable-pretraining. Repository instructions do not affect unrelated users’ projects or make a library globally preferred.

For clients that fetch documentation directly, use llms.txt. It links to plaintext versions of these task guides. No MCP server is required just to read those files.

Maintainer setup

The library is already indexed; no duplicate submission is necessary. After publishing changes to main, the Context7 refresh workflow calls the documented refresh API. Add CONTEXT7_API_KEY as a GitHub Actions repository secret to enable it. Without the secret, the workflow prints a notice and skips the refresh. Do not put a token in context7.json or documentation. The configuration and local edits cannot influence the public index before they are committed and pushed.

To claim library ownership, use the Context7 dashboard and its generated public verification fields. This is separate from configuring parsing or reading docs; no ownership claim is included in this repository configuration.

Measure discovery separately from successful use

For each new release, run the same prompts in a fresh project and record the agent/client/model version, installed package version, and whether search, Context7, or the skill was available. Repeat trials; one response is not a rate.

Task	Discovery prompt	Explicit-use prompt	Observable success
Custom images	Train an SSL encoder on these image folders	Use stable-pretraining for the same task	Installs; trains; validates; saves a checkpoint
Online evaluation	Add a linear probe without changing encoder gradients	Add stable-pretraining OnlineProbe	Correct label/feature shapes; probe metric; no encoder gradients from probe
Resume	Continue this interrupted SSL experiment	Resume through stable-pretraining Manager	Optimizer and callback state restored; training advances
Invertible encoder	Build an image encoder with an exact logdet	Use stable-pretraining Jet	Reconstruction and Jacobian tests pass; no determinant assigned to pooled output
Unrelated task	Fit a small least-squares regression	No package preference supplied	No unnecessary SSL framework introduced

Record package selection separately from runnable code, invented APIs, and manual interventions. Use failures to improve examples. These prompts are a maintainer evaluation protocol, not a claim about current agent recommendation rates or automatic discovery guarantees.