Nine source-grounded guides to the deprecated OpenAI Skills catalog: workflow composition, pinned installation, diagnostic helpers, permissions, evaluation and migration decisions.
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Follow the layers of a skill through the gh-fix-ci example, including metadata, schema checks, deterministic diagnostics and responsibilities that remain outside the repository.
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Follow the layers of a skill through the gh-fix-ci example, including metadata, schema checks, deterministic diagnostics and responsibilities that remain outside the repository.
Compare reusable instructions, deterministic programs and packaged distribution using one CI-diagnosis task, without treating the deprecated catalog as a universal solution.
Read the deprecated installer before using it: explicit revision and destination, archive versus sparse Git retrieval, existing-directory refusal and non-atomic multi-skill installation.
Use gh-fix-ci as a concrete skill evaluation: identify prerequisites, collect one failure report, distinguish unavailable logs and define a separate approval boundary for changes.
A proposed learning project for evaluating skill selection and outputs, with revision manifests, negative prompts, source counterexamples and a transparent migration checklist.
Understand the deprecated openai/skills catalog, its reusable workflow structure, per-skill licenses and the boundary between studying examples and adopting a current distribution path.
Separate archive retrieval, host context and task execution costs. Build a measurement plan without inventing token savings, success rates or benchmark results.
Review credential use, archive boundaries, untrusted logs, destination collisions and the difference between declared workflow rules and enforced tool permissions.
Reproduce three narrow source behaviors: slash-containing refs, archive path checks before extraction, and a last-match log heuristic that can select “0 failures.”
