Trigger Pipelines For Copilot Pr Agent Skill
Trigger ADO pipelines for a Copilot-created PR by posting /azp run comments. Use when the user asks to trigger CI pipelines for a specific PR.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/microsoft/FluidFramework --skill trigger-pipelines-for-copilot-prWhat it tells the agent to use
found in the instruction textThe instruction itself
as written by the authorPost the following two comments to the PR specified by the user ($ARGUMENTS), in the microsoft/FluidFramework repository on GitHub.
They need to be posted separately because otherwise it gets too long and
fails to trigger correctly.
First comment:
/azp run Build - protocol-definitions,Build - test-tools,server-gitrest,server-gitssh,server-historian,server-routerlicious,Build - client packages,repo-policy-check
Second comment:
/azp run Build - api-markdown-documenter,Build - benchmark-tool,Build - build-common,Build - build-tools,Build - common-utils,Build - eslint-config-fluid,Build - eslint-plugin-fluid
Posting those comments will trigger all our pipelines, which is necessary for PRs that are created by Copilot.
To post the comments first check if the GitHub CLI is available,
and if so use MSYS_NO_PATHCONV=1 gh pr comment <PULL_REQUEST_NUMBER> --repo microsoft/FluidFramework --body "<COMMENT_TEXT>".
Note: MSYS_NO_PATHCONV=1 is required on Windows (Git Bash) to prevent /azp from being expanded to C:/Program Files/Git/azp.
If gh is not available but $GITHUB_TOKEN is, you can try the GitHub REST API directly, e.g.:
curl -L \
-X POST \
-H "Accept: application/vnd.github+json" \
-H "Authorization: Bearer $GITHUB_TOKEN" \
-H "X-GitHub-Api-Version: 2022-11-28" \
https://api.github.com/repos/microsoft/FluidFramework/issues/PULL_REQUEST_NUMBER/comments \
-d '{"body":"<COMMENT_TEXT>"}'
If neither is available, don't do anything and tell the user you can't complete the request and why.
Other skills for the same job
different authors, same section of the catalogue
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
How to use it
Take microsoft/trigger-pipelines-for-copilot-pr from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.