Agent skill

Investigate CI Reliability

by openshift-eng in openshift-eng/ai-helpers

Find and independently validate actionable reliability defects across OpenShift release jobs and presubmits, then export portable issue handoffs.

Apache-2.0Auto-check passed

Install Investigate CI Reliability

skills CLI
$ npx skills add openshift-eng/ai-helpers --skill investigate-ci-reliability -a claude-code

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

GitHub CLI
$ gh skill install openshift-eng/ai-helpers investigate-ci-reliability --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/openshift-eng/ai-helpers.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ci-extras/skills/investigate-ci-reliability .claude/skills/investigate-ci-reliability && 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
investigate-ci-reliability
GitHub stars
120
Token cost
~1.9k tokens
SKILL.md length
865 words
Files
11 (incl. scripts, references)
Skills in repo
118
Repo updated
First seen
Licence
Apache-2.0

At a glance

Find and independently validate actionable reliability defects across OpenShift release jobs and presubmits, then export portable issue handoffs.

  • Asked to investigate reliability across a bounded CI run population and deliver proven fixes
  • SKILL.md covers Inputs and defaults, Start and collect, Investigate bounded candidates and Record evidence and…, plus 1 more section
  • Runs Python scripts from its folder; calls python3

What it does

Investigate CI Reliability is an agent skill from openshift-eng/ai-helpers. Find and independently validate actionable reliability defects across OpenShift release jobs and presubmits, then export portable issue handoffs. Use when asked to investigate reliability across a bounded CI run population and deliver proven fixes.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `references/artifact-helpers.md`, `references/collection.md` and `references/evidence-contract.md`).

The repository describes itself as: Developer productivity tools for Claude Code & other AI assistants. The licence is Apache-2.0.

When your agent uses it

  • Asked to investigate reliability across a bounded CI run population and deliver proven fixes

Example prompts

  • “/investigate-ci-reliability”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit a627176. 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

    Ships 6 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

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

  • Network

    No URLs in SKILL.md.

    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

Investigate CI Reliability loads about 1.9k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 865 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.6k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from openshift-eng/ai-helpers at commit a627176, republished under its Apache-2.0 licence (© openshift-eng). 865 words, ~1,861 tokens.

Download SKILL.mdSave it as .claude/skills/investigate-ci-reliability/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
investigate-ci-reliability
description
Find and independently validate actionable reliability defects across OpenShift release jobs and presubmits, then export portable issue handoffs. Use when asked to investigate reliability across a bounded CI run population and deliver proven fixes.

CI reliability investigation

Investigate actual failed job runs, demonstrate the underlying defects, and deliver an issues/ directory containing only independently validated, currently applicable fixes. This package contains its own collectors, review contract, and exporter. Use the prow-job-analysis skill from the ci plugin for failed-job debugging; no prior investigation repository is required.

Inputs and defaults

  • release: required OpenShift release, inferred from the request when stated.
  • Window: last 24 hours, frozen at collection start; explicit UTC start/end supported.
  • Scope: all jobs in the release plus presubmits. Presubmits are the separate Sippy Presubmits population, not implicitly restricted to merged PRs or that release.
  • Optional scope: release-only, presubmit-only, or verified payload-blocking runs. Optional exact job, job substring, and variant filters narrow either population.
  • max-issues: 10 distinct validated mechanisms, a ceiling rather than a quota.
  • max-candidates: 100; time-budget-minutes: 120; max-agents: 4. These bound exploration, including review. Honor smaller user limits.
  • Workspace/output: user-selected paths, otherwise ~/tmp/ci-reliability/<UTC timestamp> and its validated/ subdirectory. Scratch defaults to ~/tmp/ci-reliability. Keep raw downloads bounded and separate from curated handoff evidence.

Invoke /investigate-ci-reliability 5.1 --max-issues 10 directly. The skill accepts these inputs in natural language or flags. Resolve bundled script paths relative to this SKILL.md, regardless of cwd. The bundled scripts need Python 3.10+ and public HTTPS access. JUnit parsing requires Expat 2.7.2+ in that Python runtime. The investigation also requires the prow-job-analysis skill from the ci plugin.

Start and collect

Read collection controls for exact collector flags, verified blocking selection, pagination behavior, and completeness limits.

bash
python3 scripts/reliability.py init --workspace WORK --max-issues 10
python3 scripts/collect_runs.py --release 5.1 --output WORK

Here WORK is the chosen workspace, not a literal required directory name. Run commands using the resolved script paths. The default collector includes successful controls, running jobs, and every failure code; preserve these distinctions in all rate calculations. The manifest records the frozen interval and any truncation or missing evidence.

Group failures by job, phase, and recurring signature. Use the selected release and presubmit populations as independent cohorts and deduplicate shared run IDs. Ordinary unmerged PR defects are not automatically reliability issues: establish that a defect in reusable infrastructure, product recovery, or test behavior causes the failures. A selected PR SHA does not freeze base revision, batch membership, configuration, or images.

Investigate bounded candidates

Invoke $prow-job-analysis for every failed Prow job being investigated, including aggregate component jobs. Provide the exact run URL, frozen scope, and selected scratch location. Follow that skill's metadata, artifact, OS-evidence, and domain-specific investigation procedure. If it is unavailable, report the missing skill dependency; do not substitute a second debugging workflow here.

Use the bundled artifact helpers for bounded acquisition, caching, and retained evidence. Preserve the chosen scratch location and budgets when invoking prow-job-analysis. The proof-review stage below determines whether the resulting causal finding is ready for a validated handoff.

For aggregated jobs, retain the parent verdict and follow recorded component URLs. Verify which children belong to the tested payload versus baseline/control cohorts. Investigate causal component failures and sample/discovery shortfalls. Unavailable children are explicit coverage gaps. Parent retries sharing children are not independent failures.

If delegation is available, assign disjoint candidates and reserve independent review capacity within max-agents. Give each worker the frozen scope, shared budgets, scratch location, the requirement to use prow-job-analysis, and the evidence contract. Otherwise investigate serially and request a separate review context before promotion; self-review cannot satisfy the gate. Do not change the agent installation's concurrency configuration.

Stop new discovery when the validated ceiling, candidate ceiling, or elapsed time budget is reached. Finish review and export within the remaining budget; export fewer issues when proof is incomplete. Do not lower the standard to fill max-issues.

Show full SKILL.md (279 more words)Show less

Record evidence and independent review

Write one candidate JSON per mechanism under WORK/candidates/ using the evidence contract. Curate text artifacts under WORK/evidence/ with source URLs, SHA256 hashes, and exact line ranges. Distinguish observed cofailures from sole blockers; source defects can be demonstrated without claiming every matching run would recover. A timeout, quota rejection, or hypothesis is not yet a demonstrated incorrect behavior with a justified fix.

Perform the independent proof-review stage in a separate reviewer context. Give the reviewer the raw evidence, candidate, and contract. Require counterarguments, current-fix verification, and the exact scope of the proposed repair. A later candidate edit invalidates its review digest. Already-fixed incidents and unresolved causes stay outside issues/, even when their historical impact is large.

Export the deliverable

bash
python3 scripts/reliability.py validate --workspace WORK
python3 scripts/reliability.py publish --workspace WORK --output WORK/validated

The exporter requires matching investigator and independent PROVEN_FIX verdicts, valid evidence hashes/ranges, and distinct investigator/reviewer identities. It deduplicates by mechanism, ranks by priority, and enforces max-issues. It checks the evidence contract, not the truth of an agent's claim; substantive review remains essential.

Output is a fresh, portable snapshot:

  • issues/<slug>/README.md: impact, root cause, owner, source state, proposed change, acceptance criteria, review reasoning, limits, and linked retained evidence.
  • Each issue includes evidence files and machine-readable finding/review records.
  • index.html and README.md: ranked validated handoffs.
  • manifest.json: limits and published issue inventory.
  • unresolved.json: rejected, unresolved, already-fixed, duplicate, unreviewed, and validated-over-limit records; excluded from the fix directory.

Publishing refuses an existing destination to protect hand edits and prevent stale approvals from surviving a new run. Choose a new snapshot path to publish again. Report the actual validated count and collection/proof gaps. Collection and export are local/read-only with respect to CI: opening external issues, triggering jobs, or implementing fixes are separate user requests.

© openshift-eng, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts, references) in plugins/ci-extras/skills/investigate-ci-reliability of openshift-eng/ai-helpers.

  • SKILL.md
  • references/artifact-helpers.md
  • references/collection.md
  • references/evidence-contract.md
  • references/proof-review.md
  • scripts/collect_runs.py
  • scripts/prow_artifacts.py
  • scripts/reliability.py
  • scripts/test_collect_runs.py
  • scripts/test_prow_artifacts.py
  • scripts/test_reliability.py

Open the folder on GitHubat commit a627176

Compare with similar skills

Investigate CI Reliability 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.

Investigate CI Reliability compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Investigate CI Reliability this skillopenshift-eng/ai-helpers120—~1.9kAutomated safety check: PassApache-2.0
Openshiftsickn33/agentic-awesome-skills47k1 repos~2.4kAutomated safety check: PassMIT
ActionsJetBrains/intellij-community21k—~341Automated safety check: PassCustom licence
Root Cause Investigationgarrytan/gstack136k—~12kAutomated safety check: NotesMIT
Osint Investigationaffaan-m/ECC276k—~5.7kAutomated safety check: PassCC-BY-SA-4.0
Laravel Actionscoollabsio/coolify63k—~2.4kAutomated safety check: PassApache-2.0

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Questions about Investigate CI Reliability

What does Investigate CI Reliability do?

Find and independently validate actionable reliability defects across OpenShift release jobs and presubmits, then export portable issue handoffs. Investigate CI Reliability is an agent skill from openshift-eng/ai-helpers. Find and independently validate actionable reliability defects across OpenShift release jobs and presubmits, then export portable issue handoffs.

When should I use Investigate CI Reliability?

Investigate CI Reliability fits situations like: asked to investigate reliability across a bounded CI run population and deliver proven fixes.

How do I install Investigate CI Reliability in Claude Code?

Run `npx skills add openshift-eng/ai-helpers --skill investigate-ci-reliability -a claude-code`. Or copy the skill folder (plugins/ci-extras/skills/investigate-ci-reliability in openshift-eng/ai-helpers) into .claude/skills/investigate-ci-reliability in your project. Claude Code loads it when a task matches its description.

How do I install Investigate CI Reliability in Codex?

Run `npx skills add openshift-eng/ai-helpers --skill investigate-ci-reliability -a codex`. Or copy the skill folder (plugins/ci-extras/skills/investigate-ci-reliability in openshift-eng/ai-helpers) into .agents/skills/investigate-ci-reliability in your project. Codex loads it when a task matches its description.

Can I use Investigate CI Reliability 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 openshift-eng/ai-helpers --skill investigate-ci-reliability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/investigate-ci-reliability, .gemini/skills/investigate-ci-reliability, .github/skills/investigate-ci-reliability and .opencode/skills/investigate-ci-reliability in your project.

What does Investigate CI Reliability need to run?

Going by SKILL.md and its folder, Investigate CI Reliability needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Investigate CI Reliability access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Investigate CI Reliability 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Investigate CI Reliability use?

Investigate CI Reliability is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Investigate CI Reliability use?

About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.7k tokens, read only when the agent opens those files.

What are the alternatives to Investigate CI Reliability?

Skills that share tags, products or a category with Investigate CI Reliability: Openshift (sickn33/agentic-awesome-skills, 47k stars), Actions (JetBrains/intellij-community, 21k stars), Root Cause Investigation (garrytan/gstack, 136k stars) and Osint Investigation (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Investigate CI Reliability?

openshift-eng (a GitHub organization) maintains it in openshift-eng/ai-helpers, which has 120 GitHub stars. The repository holds 118 skills in this directory. The repository was last updated on October 6, 2026.

Source: openshift-eng/ai-helpers on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.