Official agent skill

ONNX Runtime CI Management

by microsoft in microsoft/onnxruntime

Triggers, re-runs and unblocks the CI checks on an ONNX Runtime pull request, after diagnosing whether a failure is transient or needs a code change.

OfficialMITAuto-check passedDevOps & Cloud

Install ONNX Runtime CI Management

skills CLI
$ npx skills add microsoft/onnxruntime --skill ort-ci -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install microsoft/onnxruntime ort-ci --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/microsoft/onnxruntime.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/ort-ci .claude/skills/ort-ci && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ort-ci
GitHub stars
22k
Token cost
~4.1k tokens
SKILL.md length
1,531 words
Files
1
Skills in repo
14
Repo updated
First seen
Licence
MIT

At a glance

Triggers, re-runs and unblocks the CI checks on an ONNX Runtime pull request, after diagnosing whether a failure is transient or needs a code change.

  • Works in 2 steps: Download the failed log → Classify the failure and act
  • A required check on an ONNX Runtime PR is stuck, missing or failed
  • SKILL.md covers Gather Context, Triage: Diagnose Before…, 1. Re-run Failed GitHub… and 2. Unblock license/cla (CLA bot), plus 6 more sections
  • Calls gh, git and python

What it does

Work happens on pull requests in `microsoft/onnxruntime`, where almost all CI runs as GitHub Actions workflows. Only the `Linux Android Emulator QNN CI Pipeline` still runs on Azure Pipelines, alongside the bot-driven `license/cla` status check. The agent starts by inspecting the PR's `statusCheckRollup` with `gh` so it does not queue duplicates, and tells GitHub Actions checks from Azure ones by their workflow name and details URL.

Failures are diagnosed before anything is re-run: most need a code change, and only genuinely transient problems such as network or disk errors deserve a manual re-run, and only while the PR head is unchanged. Checks that are already queued, running or successful are left alone, and after pushing a fix the agent stops because the push starts fresh CI. Triggers include stuck or missing checks, Python format failures, Doc Gen CI failures and `/azp run`.

When your agent uses it

  • A required check on an ONNX Runtime PR is stuck, missing or failed
  • Deciding whether a failing check is transient or needs a code change
  • Fixing a failing Python format or operator-docs check
  • Re-running the Azure Pipelines QNN check

Example prompts

  • “My ONNX Runtime PR has a required check that never started; figure out why and retrigger it.”
  • “The Python format check failed on my PR, so tell me whether to re-run it or fix the code.”
  • “List all failed and pending checks on my onnxruntime pull request without re-triggering anything.”

Requirements

  • GitHub CLI `gh`, signed in with access to the pull request

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Download the failed log
  2. Classify the failure and act

What it can do on your machine

Read from SKILL.md and the folder at commit a571b72. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh
    • git
    • python
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh, git and pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

ONNX Runtime CI Management loads about 4.1k tokens when it runs. Until then it costs about 136 tokens; SKILL.md has 1,531 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~136
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from microsoft/onnxruntime at commit a571b72, republished under its MIT licence (© microsoft). 1,531 words, ~4,113 tokens.

Download SKILL.mdSave it as .claude/skills/ort-ci/SKILL.md (or your agent's skills folder).
name
ort-ci
description
Trigger, re-run, and unblock ONNX Runtime CI checks on a GitHub pull request. Use this skill when a required check is stuck, missing, failed, or needs re-running. Nearly all ORT CI runs as GitHub Actions workflows; only the "Linux Android Emulator QNN CI Pipeline" remains on Azure Pipelines, plus the bot-driven "license/cla" and "Python format" lint checks. Triggers on: rerun CI, retrigger checks, stuck check, missing pipeline, failed CI, license/cla, Python format failure, Doc Gen CI failure, operator docs out of date, /azp run.

ONNX Runtime CI Management

Workflows for triggering, re-running, and unblocking CI checks on an ONNX Runtime PR. The repository is microsoft/onnxruntime. As of 2026-07, nearly all CI runs as GitHub Actions workflows (~80+ checks per PR). Only one required check still runs on Azure Pipelines — Linux Android Emulator QNN CI Pipeline (host aiinfra.visualstudio.com). There is also the bot-driven license/cla status check.

Failures are not all the same. Before touching anything, diagnose each failure (see Triage: Diagnose Before Re-running): most failures need a code change and re-running them just fails again; only genuinely transient (network/disk) failures should be re-run via §1. §4 (Azure Pipelines) applies only to the single QNN pipeline.

Before doing anything, inspect current state so you do not queue duplicate runs.

If you make any change, commit and push it, then stop. A push updates the PR head SHA and automatically starts CI for the new commit. Do not manually re-run failures from the old SHA after pushing a fix; that only queues redundant runs against stale code. Manual re-runs are only for transient failures when the PR head has not changed.

Classify a check's provider

GitHub Actions checks have a non-empty workflowName and a detailsUrl on github.com; the Azure Pipelines check has an empty workflowName and a detailsUrl on aiinfra.visualstudio.com:

bash
gh pr view <number> --repo microsoft/onnxruntime --json statusCheckRollup \
  --jq '[.statusCheckRollup[] | {name, host:(.detailsUrl|split("/")[2])}]
        | group_by(.host) | map({host:.[0].host, count:length})'

Gather Context

bash
# PR metadata + all checks grouped by state
gh pr view <number> --repo microsoft/onnxruntime \
  --json number,title,url,state,isDraft,headRefName,headRefOid,baseRefName,statusCheckRollup

# Head / merge SHAs for external CI
gh api repos/microsoft/onnxruntime/pulls/<number> --jq '{head:.head.sha, merge:.merge_commit_sha}'

Inspect statusCheckRollup and note, for each requested check, whether it is missing, queued, in progress, failed, canceled, skipped, or already successful. Do not re-trigger a check that is already queued/in_progress/SUCCESS unless the user explicitly asks.

Quickly list just the failed/pending checks:

bash
gh pr view <number> --repo microsoft/onnxruntime --json statusCheckRollup \
  --jq '.statusCheckRollup[]
        | {name:(.name//.context), status:(.status//.state), conclusion:.conclusion}
        | select(.conclusion!="SUCCESS" and .status!="SUCCESS")'

Triage: Diagnose Before Re-running

Never blindly re-run failed CI. Most failures need a code change and will fail again identically on re-run. Only transient failures should be re-run. The process is: download the failed job's log, read the actual error, classify it, then fix or re-run case by case.

Step 1 — Download the failed log

For a GitHub Actions check (the vast majority), get the run and read only the failed steps:

bash
HEAD_SHA=$(gh api repos/microsoft/onnxruntime/pulls/<number> --jq .head.sha)

# Map failed checks to their workflow run IDs
gh run list --repo microsoft/onnxruntime --commit "$HEAD_SHA" --limit 100 \
  --json databaseId,workflowName,status,conclusion,url \
  --jq '.[] | select(.conclusion=="failure" or .conclusion=="cancelled")'  # cspell:ignore cancelled -- literal GitHub API value

# Dump just the failed steps of a run (grep for the real error)
gh run view <run_id> --repo microsoft/onnxruntime --log-failed > /tmp/ci_<run_id>.log
grep -nE "error:|FAILED|warning:|Traceback|fatal error|No space left|Could not resolve|timed out" \
  /tmp/ci_<run_id>.log | head -50

For the Azure Pipelines QNN check, open its detailsUrl (a dev.azure.com / aiinfra.visualstudio.com build page) and download the job log, or use the Azure DevOps .../builds/<buildId>/timeline + log APIs (see the ci-failure-retrieval skill for the exact requests).

Step 2 — Classify the failure and act
#Failure classHow to recognize it in the logAction — re-run or fix?
1C/C++ warning-as-errorerror: on a -Werror//WX line — e.g. implicit type-cast/narrowing (-Werror=conversion), unused variable/unused parameter (-Werror=unused-*), sign-compare, maybe-uninitializedFix code. Re-run will not help. Remove/[[maybe_unused]] the unused symbol, add an explicit static_cast<T>() / gsl::narrow_cast<T>() for the cast, or fix the real logic. Rebuild locally to confirm the warning is gone.
2Test failure[ FAILED ] Suite.Case (gtest) or FAILED test_*.py::... - AssertionError (pytest); often only on some EPsFix code/test. If a newly added op test fails only on EPs that don't support the op, restrict the test to supported EPs (e.g. gtest OpTester::Run(..., {kCpuExecutionProvider, kCudaExecutionProvider}) / excluded_provider_types, or skip via SetUp), or fix the kernel. Don't re-run unchanged. See the ort-test skill.
3Transient / infra failureCould not resolve host, Connection timed out, 429 Too Many Requests, No space left on device, package/download 5xx, agent lost, submodule clone timeout — with no compile/test errorRe-run (§1). This is the one class that a plain re-run fixes. If it recurs 2–3×, escalate — it may be a real infra/proxy issue, not noise.
4Lint / Python formatPython format check fails; lintrunner reports diffsFix code with lintrunner -a, commit, push (§3). Re-run alone won't fix it.

Rules of thumb:

  • A compile error: or a [ FAILED ]/FAILED line means fix the code — re-running reruns the same failing commit and fails identically.
  • Only re-run when the log shows a network/disk/agent problem and no compile or assertion error.
  • When unsure, download the log and read it; do not guess from the check name alone.
  • After any code, test, lint, or generated-file fix, commit and push it. CI starts automatically for the new head SHA. Do not use §1 after pushing a change; the failed runs belong to the old SHA and re-running them would test stale code.

1. Re-run Failed GitHub Actions (transient failures only)

The repo ships a helper that re-runs only the GitHub Actions workflows whose latest run for the PR's current head commit failed/canceled — and skips any workflow that already has a newer run queued or in progress. Use it only after triage confirms the failures are transient (network/disk/agent) — see Triage. It is the safest way to retry those without piling on duplicates. Do not use this helper if you changed anything and pushed a new commit; the push already starts CI for the new head SHA.

Script: tools/scripts/rerun_failed_ci.sh

bash
# Dry run first — shows what would be re-run, triggers nothing
./tools/scripts/rerun_failed_ci.sh <number> --dry-run

# Actually re-run the failed/canceled workflows for the PR's head commit
./tools/scripts/rerun_failed_ci.sh <number>

# Explicit repo (auto-detected from cwd when omitted)
./tools/scripts/rerun_failed_ci.sh <number> microsoft/onnxruntime

It prefers gh run rerun <id> --failed (retry only failed jobs) and falls back to a full rerun for fully canceled runs that have no discrete failed jobs. Requires an authenticated gh. Always run --dry-run first and confirm the list looks right before the real run.

To re-run one specific workflow manually:

bash
gh run list --repo microsoft/onnxruntime --commit <head_sha> --limit 100 \
  --json databaseId,workflowName,status,conclusion,url
gh run rerun <run_id> --repo microsoft/onnxruntime --failed   # only failed jobs
gh run rerun <run_id> --repo microsoft/onnxruntime            # full rerun

2. Unblock license/cla (CLA bot)

The license/cla check is posted by Microsoft's CLA bot, independent of the CI pipelines. When it is stuck as "Expected — Waiting for status to be reported", re-trigger only the bot — no CI jobs are re-run — by posting this comment on the PR:

bash
gh pr comment <number> --repo microsoft/onnxruntime \
  --body "@microsoft-github-policy-service rerun"

Then verify it flips to success:

bash
gh pr view <number> --repo microsoft/onnxruntime --json statusCheckRollup \
  --jq '.statusCheckRollup[] | select((.name//.context)=="license/cla")
        | {status, conclusion}'

Expect {"status":"COMPLETED","conclusion":"SUCCESS"}.

Show full SKILL.md (658 more words)Show less

3. Fix the "Python format" required check

The required Python format check (job lint-python-format in .github/workflows/lint.yml) runs lintrunner --all-files and fails on any formatting/lint violation. Re-running it will not help — you must fix the code, commit, and push. See docs/Coding_Conventions_and_Standards.md and the ort-lint skill.

bash
# One-time setup (in an activated Python venv)
pip install -r requirements-lintrunner.txt
lintrunner init

# Auto-fix. Prefer changed files; use --all-files to match CI exactly.
lintrunner -a                    # changed files only
lintrunner -a --all-files        # everything (what CI checks)

# Verify clean (no changes reported == pass)
lintrunner --all-files

Then commit and push the formatting fixes; the check re-runs automatically on the new commit:

bash
git add -u && git commit -m "Fix lint" && git push

Notes:

  • CI runs lintrunner --all-files, so a local lintrunner -a on only changed files can miss a pre-existing violation the CI reports. If the check still fails, run --all-files locally.
  • The same job also covers C++ clang-format and other adapters; the fix is the same (lintrunner -a).

4. Trigger the Azure Pipelines check (QNN Android Emulator only)

As of 2026-07, the only ORT check still on Azure Pipelines is Linux Android Emulator QNN CI Pipeline. Everything else is GitHub Actions (use §1). Trigger it through the PR comment integration:

bash
gh pr comment <number> --repo microsoft/onnxruntime \
  --body "/azp run Linux Android Emulator QNN CI Pipeline"

Then wait briefly and check for a reply from azure-pipelines[bot]:

bash
# Note: through the GraphQL `comments` field (what `gh pr view --json comments`
# uses), the bot's author.login is `azure-pipelines` (no `[bot]` suffix), even
# though it surfaces as azure-pipelines[bot] in the UI and the REST API.
gh pr view <number> --repo microsoft/onnxruntime --json comments \
  --jq '.comments[] | select(.author.login=="azure-pipelines")
        | {createdAt, body}' | tail
  • If the bot replies "No pipelines are associated with this pull request", the pipeline is not wired to the comment app — use the direct Azure DevOps API fallback (see the Trigger CI Pipelines section of the private gh-pr-management skill for the dev.azure.com project/definition discovery and POST .../runs payload using refName: refs/pull/<number>/merge and the PR merge_commit_sha).
  • Keep it to one batch /azp run comment per attempt; do not spam repeated comments.

5. Fix the "Windows GPU Doc Gen CI" check (operator docs out of date)

The ONNX Runtime Windows GPU Doc Gen CI check (workflow .github/workflows/windows_gpu_doc_gen.yml) builds ORT and runs build.py --gen_doc validate. It fails when the generated operator docs no longer match what's committed — typically after you add/modify an operator or its kernel registrations but forget to regenerate docs/ContribOperators.md / docs/OperatorKernels.md. Re-running will not help; you must update the docs.

The easiest fix is to download the regenerated docs the failed job already produced: on failure the workflow uploads a single artifact named updated-docs that contains both OperatorKernels.md and ContribOperators.md at its top level, so you can replace the committed copies without building locally.

bash
HEAD_SHA=$(gh api repos/microsoft/onnxruntime/pulls/<number> --jq .head.sha)

# Find the failed Doc Gen run
run_id=$(gh run list --repo microsoft/onnxruntime --commit "$HEAD_SHA" --limit 100 \
  --json databaseId,workflowName,conclusion \
  --jq '.[] | select(.workflowName|test("Doc Gen")) | select(.conclusion=="failure") | .databaseId' | head -1)

# Confirm the artifact is present (expect: updated-docs)
gh api repos/microsoft/onnxruntime/actions/runs/$run_id/artifacts --jq '.artifacts[].name'

# gh run download does not overwrite existing files. Remove both stale generated docs first,
# then extract their replacements (the artifact always holds both files).
rm docs/OperatorKernels.md docs/ContribOperators.md
gh run download "$run_id" --repo microsoft/onnxruntime -n updated-docs --dir docs/

Then review, commit, and push — the check re-runs on the new commit:

bash
git diff --stat docs/ContribOperators.md docs/OperatorKernels.md
git add docs/ContribOperators.md docs/OperatorKernels.md
git commit -m "Update operator docs" && git push

Notes:

  • The updated-docs artifact always contains both OperatorKernels.md and ContribOperators.md at its top level. Remove both committed files before downloading because gh run download refuses to overwrite them; --dir docs/ then recreates both. Only the file(s) whose generated content changed will show up in git diff after extraction.
  • Equivalent local alternative: python tools/ci_build/build.py --config Release --build_dir build/Linux --gen_doc after a build, then commit the regenerated files. Downloading the artifact is faster since it avoids a full build.

Special-Case Check Handling

Check / job nameOwnerHow to unblock
license/claCLA botComment @microsoft-github-policy-service rerun (§2)
Python format (lint-python-format)GitHub ActionsFix with lintrunner -a, commit, push (§3) — rerun alone won't fix
ONNX Runtime Windows GPU Doc Gen CIGitHub ActionsDownload the updated-docs artifact into docs/, commit, push (§5) — rerun alone won't fix
Optional Lint, Optional Lint C++GitHub ActionsNon-required reviewdog checks; fix warnings or ignore
Most CI (Linux/Windows/Mac/CUDA/TensorRT/WebGPU/Web/Android/iOS, windows_x64_*, Builds, PR Checks)GitHub Actionsrerun_failed_ci.sh <number> (§1)
Linux Android Emulator QNN CI PipelineAzure DevOps/azp run Linux Android Emulator QNN CI Pipeline (§4), then API fallback

Safety Rules

  • Never trigger release, publish, official, nightly, signing (ESRP), deployment, or package-upload pipelines unless the user explicitly asks for that class of pipeline. Treat names containing release, publish, official, nightly, sign, ESRP, production, or deploy as high-risk and confirm first.
  • Always --dry-run the rerun script before the real run.
  • Do not re-run green checks or dispatch unrelated workflows.
  • Prefer PR merge refs (refs/pull/<number>/merge) over branch refs for external CI so the run validates the merge result.
  • Do not claim success until the provider returns a queued/in-progress/completed run ID or URL, or the check flips state in statusCheckRollup.

Verify After Triggering

bash
gh pr view <number> --repo microsoft/onnxruntime --json statusCheckRollup \
  --jq '.statusCheckRollup | group_by(.status//.state)
        | map({state:(.[0].status//.[0].state), count:length})'

Confirm the previously stuck/failed check moved to queued/in_progress (or SUCCESS for the CLA bot), and that no duplicate runs were created for the same head SHA.

© microsoft, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .github/skills/ort-ci of microsoft/onnxruntime.

Open the folder on GitHubat commit a571b72

Compare with similar skills

ONNX Runtime CI Management next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

ONNX Runtime CI Management compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
ONNX Runtime CI Management this skillmicrosoft/onnxruntime22k—~4.1kAutomated safety check: PassMIT
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CI Watchdoglatitude-dev/latitude-llm4.7k—~1.6kAutomated safety check: PassMIT
Migrate To TeamcityJetBrains/teamcity-cli125—~1.3kAutomated safety check: PassApache-2.0
Megatron-LM CI Failure TriageNVIDIA/Megatron-LM18k—~1.6kAutomated safety check: PassApache-2.0
CI/CD Failure Troubleshootingruby-git/ruby-git1.8k—~1.9kAutomated safety check: PassMIT

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  • ONNX Runtime Test Runner

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Categories

Questions about ONNX Runtime CI Management

What does ONNX Runtime CI Management do?

Triggers, re-runs and unblocks the CI checks on an ONNX Runtime pull request, after diagnosing whether a failure is transient or needs a code change. Work happens on pull requests in `microsoft/onnxruntime`, where almost all CI runs as GitHub Actions workflows. Only the `Linux Android Emulator QNN CI Pipeline` still runs on Azure Pipelines, alongside the bot-driven `license/cla` status check.

When should I use ONNX Runtime CI Management?

ONNX Runtime CI Management fits situations like: A required check on an ONNX Runtime PR is stuck, missing or failed; deciding whether a failing check is transient or needs a code change; fixing a failing Python format or operator-docs check; re-running the Azure Pipelines QNN check.

How do I install ONNX Runtime CI Management in Claude Code?

Run `npx skills add microsoft/onnxruntime --skill ort-ci -a claude-code`. Or copy the skill folder (.github/skills/ort-ci in microsoft/onnxruntime) into .claude/skills/ort-ci in your project. Claude Code loads it when a task matches its description.

How do I install ONNX Runtime CI Management in Codex?

Run `npx skills add microsoft/onnxruntime --skill ort-ci -a codex`. Or copy the skill folder (.github/skills/ort-ci in microsoft/onnxruntime) into .agents/skills/ort-ci in your project. Codex loads it when a task matches its description.

Can I use ONNX Runtime CI Management in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add microsoft/onnxruntime --skill ort-ci -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ort-ci, .gemini/skills/ort-ci, .github/skills/ort-ci and .opencode/skills/ort-ci in your project.

What does ONNX Runtime CI Management need to run?

Going by SKILL.md and its folder, ONNX Runtime CI Management needs the command-line tools its instructions call (gh, git, python and pip). Our summary lists: GitHub CLI `gh`, signed in with access to the pull request.

Does ONNX Runtime CI Management access the network?

SKILL.md contains no URLs. Its commands use gh, git and pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is ONNX Runtime CI Management safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does ONNX Runtime CI Management use?

ONNX Runtime CI Management is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does ONNX Runtime CI Management use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to ONNX Runtime CI Management?

Skills that share tags, products or a category with ONNX Runtime CI Management: Azure Pipelines Log Downloader (ansible/ansible, 71k stars), CI Watchdog (latitude-dev/latitude-llm, 4.7k stars), Migrate To Teamcity (JetBrains/teamcity-cli, 125 stars) and Megatron-LM CI Failure Triage (NVIDIA/Megatron-LM, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains ONNX Runtime CI Management?

microsoft (a GitHub organization, an official publisher) maintains it in microsoft/onnxruntime, which has 22,035 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 8, 2026.

Source: microsoft/onnxruntime on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.