Resolve an identified GitHub issue through investigation, duplicate-work checks, implementation, and regression testing. Open a pull request, address CI and review feedback, and track the outcome while keeping the issue updated with concise progress comments.
中文简介正在准备中。当前显示英语简介。
Implement and land a fix for an explicitly identified marin-community/marin GitHub issue.
安装前可以查看交给 AI 代理的指令内容。
Read AGENTS.md and the issue guidance in writing-style. Keep issue comments
terse: omit filler and repetition, use annotated links, and keep each prose
section to three or four sentences. Complete the workflow in order. If blocked,
comment on the issue with the last completed state and the blocker.
Use gh to fetch the issue. Read the codebase for all relevant source files.
Post one concise comment with Research and Proposed Fix sections.
Before writing code, run a duplicate-work preflight:
gh pr list --state open --search "<issue-id or keyword>").gh issue list --state open --search "<keyword>").In Research, state the cause in two or three sentences and link at most five
relevant code locations with short annotations. In Proposed Fix, name the
smallest fix and include the failing call chain or a snippet only when needed.
Implement on agent/{YYYYMMDD}-fix-{issue-id}.
Follow write-tests. Prefer a regression test that fails before
the fix, extend an existing file, run the narrow test and affected safe suite,
then run ./infra/pre-commit.py --all-files --fix. Do not override the configured
marker expression.
When all tests pass, upload your branch and open a PR following
.agents/skills/commit/SKILL.md exactly — use the plain-text format it
specifies (no markdown headers, bullet lists, or ## Summary sections;
violations are rejected). Attach a comment to the Github issue summarizing the fix.
After opening the PR, follow step 9 of .agents/skills/commit/SKILL.md
exactly. Its wait_for.py loop owns CI, review feedback, and lifecycle events.
Do not start a separate gh pr view or gh pr checks polling loop. Investigate
failures, push fixes, and re-arm the wait as that skill specifies.
Post a final issue comment with the landed PR or the last terminal state.
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Compare Zephyr changes using the same prepared dataset and matching execution settings. Analyze Finelog metrics for each processing stage to distinguish CPU efficiency, elapsed time, and peak memory changes, then report improvements, regressions, or tradeoffs.
中文简介正在准备中。当前显示英语简介。
Register Hugging Face datasets for Marin by inspecting their structure without downloading the full dataset. Identify text fields, configurations, and data splits, then add a dataset module and validate the mapping with a sample.
中文简介正在准备中。当前显示英语简介。
Turn a specified GCP access request into a reviewable Pulumi change and pull request. Select the narrowest suitable role and encrypt personal email identities, covering resource permissions and Cloud Run viewer access through IAP, an access gateway.
中文简介正在准备中。当前显示英语简介。
Add or update Pallas/Mosaic kernels, specialized computation routines for JAX. The skill covers reference implementations, output and gradient checks, public wrappers, fallback behavior, performance benchmarks, and hardware-specific tuning when requested.
中文简介正在准备中。当前显示英语简介。
Preserve old experiment scripts with a dated Git tag, a named snapshot of the code, and add comments linking related GitHub issues to the archived files and cleanup pull request.
中文简介正在准备中。当前显示英语简介。
Launch and continuously monitor a specified Zephyr pipeline on Iris when asked. It checks coordinator and worker health, tracks stage progress, identifies stalled or repeated failures, and requires explicit approval before stopping or restarting jobs.
中文简介正在准备中。当前显示英语简介。