repo-maintenance
Maintain a BayesianOptimization checkout with safe install, test, lint, docs, CI-matrix, and release-boundary guidance.
- License 许可
- MIT
- 最近更新
- 2026-09-03
- Stars
- ★ 351
- Forks
- 27
01 前言 Front Matter
- name
- repo-maintenance
- license
- MIT
- metadata
- { "disco-role": "operating" }
- description
- Maintain a BayesianOptimization checkout with safe install, test, lint, docs, CI-matrix, and release-boundary guidance.
- disable-model-invocation
- true
02 资源文件 3
- 参考文档references/development-workflows.md8.5 KB
- 参考文档references/troubleshooting.md8.7 KB
- 脚本scripts/select_native_checks.py16.2 KB
03 SKILL.md 正文
Repo Maintenance
Use this sub-skill only when the user is maintaining or editing a
bayesian-optimization source checkout. It covers development installs,
focused native tests, lint/format decisions, docs/notebook checks, CI matrix
expectations, and safe debugging paths.
For package usage questions, route away from maintenance:
- Optimizer construction, registration/probing, fitting, save/load, or
prediction workflows:
../optimizer-workflows/SKILL.md - Acquisition-function selection or exploration/exploitation controls:
../acquisition-control/SKILL.md - Constraints, typed parameters, categorical/integer domains, sequential domain
reduction, or advanced examples:
../advanced-domain-features/SKILL.md
Maintainer workflow
- Identify touched checkout files and choose the smallest native checks from
references/development-workflows.md. - Prefer non-mutating verification first: focused
pytest, Ruff format check, and Ruff lint check. - Use
scripts/select_native_checks.pyto print recommended command groups from touched paths or capability flags. The helper only prints commands; it does not execute them. - Escalate to full unit, notebook, docs, or CI-matrix coverage only when the touched files affect those surfaces or when preparing a broad change.
- Use
references/troubleshooting.mdfor Python/NumPy marker conflicts, pre-commit or Ruff failures, notebook timeouts, docs build failures, import confusion, and release/publish limits.
Safety rules
- Treat commands as checkout-maintenance commands, not runtime user guidance.
- Do not run
scripts/format.shunless the user explicitly wants formatting or automatic fixes; it mutates source files. - Do not use
scripts/check_precommit.shas a routine verification shortcut; it installs hooks before running all files. Prefer direct non-mutating Ruff or pre-commit commands when verification is enough. - Never provide credentialed publishing steps. PyPI publishing is release-only and guarded by repository secrets.
04 同仓库更多 Skills
passnet-triton-opt
Make a single PassNet Triton kernel fast AND numerically faithful: performance model (when a replacement can win at all), block/grid/warp tuning, autotune policy, launch overhead, and per-op numeric recipes to pass the dtype baseline tolerances. Use when a pass matches and is correct but speedup below expected, or when correctness fails by small numeric margins.
passnet-solve
END-TO-END playbook for solving one PassNet sample: analyze the computation graphs, decide the optimization strategy (what to fuse, what to replace, what to leave alone), drive the iteration loop, and maximize the sample score. This is the ENTRY skill — invoke it first for any PassNet optimization task; it tells you when to use passnet-pattern-fusion, passnet-triton-opt and passnet-feedback.
passnet-skill
PassNet GPU kernel optimization via compiler passes. Design and implement Triton-based optimization passes, create pass files under ./pass_dir/, self-evaluate with pass_evaluator, and iterate to maximize GPU speedup.
passnet-pattern-fusion
Author PassNet pass files: write patterns that actually MATCH the FX graph, pick fusion regions (which ops to absorb into one Triton kernel), and structure multi-pass submissions with the mandatory shared-dispatch architecture. Use when creating or fixing pattern()/replacement_args()/replacement_func() files, when a pass "failed to match", or when deciding how to fuse multiple kernels/ops.
passnet-orchestrate
Round/batch planning and decision support for PassNet graph optimization. Use for multi-sample triage, worker allocation, eval-budget policy, and post-eval keep/revert decisions. For solving one concrete sample end to end, use passnet-solve as the entry skill; consult this skill only when you need broader planning or a specific decision gate.
passnet-feedback
Fast, deterministic feedback for PassNet work: pre-flight pattern/match verification WITHOUT burning a GPU evaluation, per-node bottleneck analysis of a sample's graphs, and eval-log parsing into per-variant status + estimated score + failure classification. Use BEFORE every GPU evaluation (check_pattern), at the START of a sample (analyze_graph), and AFTER every evaluation (parse_eval_log).