MLflow is an open-source platform for building, testing, monitoring, and managing applications that use machine-learning models, large language models, or AI agents. Teams use it to inspect application behavior, evaluate results, manage prompts, and control access to models and data in production. Catalogue add-ons provide coding-agent workflows for MLflow.
Borrowing it
Nothing to install: this file belongs to mlflow/mlflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mlflow/mlflow/master/.claude/skills/analyze-ci/SKILL.mdgit clone --depth 1 https://github.com/mlflow/mlflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mlflow/mlflow/analyze-ci)<a href="https://agentmods.dev/skills/mlflow/mlflow/analyze-ci"><img src="https://agentmods.dev/badge/skills/mlflow/mlflow/analyze-ci.svg" alt="Measured on agentmods" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.00647 |
| Opus 5 | $0.00019 | $0.00324 |
| Sonnet 5 | $0.00008 | $0.00129 |
| Haiku 4.5 | $0.00004 | $0.00065 |
Grade A, and why
analyze-ci scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze CI Failures
Fetch logs from failed GitHub Action jobs and produce a focused per-job failure summary.
URLs provided: $ARGUMENTS
If no URLs are listed above, ask for a job, run, or PR URL and stop. fetch-logs requires at least one and exits 2 without.
Prerequisites
- GitHub Token: Auto-detected via
gh auth token, or setGH_TOKEN.
Steps
-
Fetch logs. Run, single-quoting each URL so the shell does not interpret
?or other special characters in it:uv run --package skills skills fetch-logs '<url>' ['<url>' ...]The command prints one block per failed job containing the workflow/job name, URL, failed step, and paths to the cached raw log, failed-step log, and (optional) package versions file.
-
Read each failed-step log and summarize it. For every block, Read the file at its
Failed step log:path, then identify:- The root cause.
- Specific error messages (assertion errors, exceptions, stack traces).
- Full pytest test names where applicable (e.g.
tests/test_foo.py::test_bar). - A short log snippet showing the error context.
-
Format each summary with these fields, then a blank line, then the 1-2 paragraph summary.
Failed job: <workflow name> / <job name>Failed step: <step name>URL: <job_url>Raw log: <raw_log_path>Failed step log: <failed_step_log_path>Package versions: <package_versions_path>(if present)
Preserve the
Raw log:,Failed step log:, andPackage versions:paths verbatim from step 1 so downstream agents can grep deeper.
Invocation examples
# All failed jobs on a PR
/analyze-ci https://github.com/mlflow/mlflow/pull/19601
# All failed jobs in one workflow run
/analyze-ci https://github.com/mlflow/mlflow/actions/runs/22626454465
# Specific job by URL
/analyze-ci https://github.com/mlflow/mlflow/actions/runs/12345/job/67890
# Multiple URLs at once
/analyze-ci https://github.com/mlflow/mlflow/actions/runs/123/job/456 https://github.com/mlflow/mlflow/actions/runs/789/job/012
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 63 lines · 38 tokens per session scan A fcba00982884
analyze-ci is a skill published in the GitHub repository mlflow/mlflow (27,837 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 647 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
fix-ci
Fix CI failures and performance regressions for a Julia package PR by iterating - triage the latest CI results, fix one root cause, verify locally, push. Use when a PR's GitHub Actions or Buildkite CI is failing, or when CI jobs run slower than they do on the main branch. Verifies CPU and GPU compilation locally…
developing-with-streamlit
Use for ALL Streamlit tasks: creating, editing, debugging, beautifying, styling, theming, optimizing, or deploying Streamlit apps. Also custom components, st.components.v2, HTML/JS/CSS work. Discovers and loads version-matched reference docs from the user's installed Streamlit (>=1.57). Triggers: streamlit, st.…
mcore-create-issue
Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.
debug-task
Diagnose and fix moon tasks that are broken, misconfigured, or behaving unexpectedly. Use this skill when a moon task is failing, not running, skipped, hanging, producing stale or wrong output, cached when it shouldn't be, re-running every time when it should be cached, or when outputs are empty or missing after a…
trulens-instrumentation
Instrument LLM apps with TruLens OTEL-based tracing - from setup to debugging and optimization.
github-ci-fix
Fix failing GitHub CI / Actions checks via fixgithubprci and push to the existing PR head, or fix a branch's failing CI via a linked repair worktree.