GitHub Actions Retry Recovery Audit for Openclaw

Analyze GitHub Actions run history to identify and quantify the cost of flaky fail-then-success retry patterns.

daniellummis
v1.0.0
Mar 7, 2026
0
795
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install github-actions-retry-recovery-audit

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install github-actions-retry-recovery-audit using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is GitHub Actions Retry Recovery Audit?

The GitHub Actions Retry Recovery Audit is a specialized tool within the Openclaw Skills ecosystem designed to tackle the flaky test problem. It specifically targets workflow slices that exhibit a recovery pattern—where initial failures are followed by an eventual success on the same commit.

This skill helps DevOps teams and software engineers identify exactly where time and money are being wasted on redundant CI reruns, providing the data needed to justify stabilization efforts. By integrating this into your developer workflow via Openclaw Skills, you can transform raw GitHub Actions JSON exports into actionable insights that improve pipeline reliability and reduce compute costs.

GitHub Actions Retry Recovery Audit Use Cases

  • Quantifying time lost to flaky GitHub Actions workflows across large repositories.
  • Identifying the most expensive CI failures to prioritize fixing the flakiest tests.
  • Implementing a CI fail gate that blocks builds if flaky rerun waste exceeds a specific minute threshold.
  • Generating audit reports for engineering leadership to demonstrate the impact of CI instability.

How GitHub Actions Retry Recovery Audit Works

  1. Collect GitHub Actions workflow run data in JSON format using the GitHub CLI tool.
  2. Group the collected attempts by repository, workflow name, branch, and specific commit SHA.
  3. Analyze the sequence of attempts to identify recovery patterns where failure-like states precede a success.
  4. Calculate the total wasted minutes consumed by all failed attempts prior to the first successful run in each group.
  5. Generate a prioritized report or JSON payload highlighting the top waste contributors for triage and remediation.

GitHub Actions Retry Recovery Audit Setup

To use this skill, ensure you have bash and python3 installed. First, export your GitHub Actions run data using the following command:

gh run view <run-id> --json databaseId,workflowName,headBranch,headSha,conclusion,createdAt,updatedAt,url,repository > artifacts/github-actions/run-<run-id>.json

Then, execute the audit script using Openclaw Skills patterns:

RUN_GLOB='artifacts/github-actions/*.json' \
WARN_WASTE_MINUTES=20 \
CRITICAL_WASTE_MINUTES=60 \
bash skills/github-actions-retry-recovery-audit/scripts/retry-recovery-audit.sh

GitHub Actions Retry Recovery Audit Data Schema & Taxonomy

The skill processes GitHub Actions JSON exports and produces structured data as follows:

Attribute Description
summary Aggregated statistics of total wasted minutes and recovery counts.
recoveries A ranked list of workflow groups that experienced fail-then-success sequences.
critical_recoveries A filtered list of sequences exceeding the CRITICAL_WASTE_MINUTES threshold.

Configuration is handled via environment variables like RUN_GLOB, TOP_N, and regex filters for workflows and branches.

GitHub Actions Retry Recovery Audit Advanced Features

  • Configurable fail gates using FAIL_ON_CRITICAL to enforce CI quality standards.
  • Granular filtering using regex for repositories, branches, and specific workflows to focus on high-priority areas.
  • Dual-format output supporting both human-readable text reports and machine-readable JSON for dashboard integration.
  • Intelligent grouping by headSha to ensure accurate tracking of reruns on the same code state.

SKILL.md


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