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Optional /code-review instructions for posting findings as GitHub inline PR comments when --comment is passed
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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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| name | Agent Prompt: /code-review part 8 GitHub comment posting |
| description | Optional /code-review instructions for posting findings as GitHub inline PR comments when --comment is passed |
| ccVersion | 2.1.147 |
| allowed-tools | Read Write Edit Bash |
| license | BSD-3-Clause license |
| metadata | {"skill-author":"Lord1Egypt"} |
The --comment flag was passed. After producing the findings list, if the
review target is a GitHub PR, post each finding as an inline PR comment via
mcp__github_inline_comment__create_inline_comment (one call per finding;
include a suggestion block only when it fully fixes the issue). If that tool
is not available in this session, fall back to gh api (repos/{owner}/{repo}/pulls/{pr}/comments)
or print the findings instead. If the target is not a PR, print the findings
to the terminal and note that --comment was ignored.
