Why Automate: Manual Updates Do Not Scale
Manual rule updates: a ruleset is updated in the dashboard. Someone needs to: notify all affected teams, each team runs rulesync pull, reviews the changes, and commits. At 5 projects: manageable. At 50 projects: someone will forget. At 200 projects: guaranteed that 20% of repos are running outdated rules at any given time. Automation: ensures every repo gets the update without human intervention. The update: happens automatically, consistently, and traceably.
Automation levels: Level 1 — Scheduled pull (a cron job runs rulesync pull weekly). Level 2 — Webhook-triggered pull (the update happens immediately when a ruleset changes). Level 3 — Automated PR (the system opens a PR in each repo with the updated rules, like Dependabot does for dependencies). Each level: more immediate and more reliable than the previous. Most teams: start at Level 1 and progress to Level 3 as they scale.
The automation goal: when a ruleset is updated in the dashboard, every affected repository has the updated rules within 1 hour (Level 2) or within 1 day (Level 1). No developer needs to remember to pull. No repo falls behind. The dashboard: shows which repos are current and which are pending update. AI rule: 'Automation replaces the weakest link in the distribution chain: human memory. Machines do not forget to run rulesync pull.'
Step 1: Scheduled Pull (Level 1 — Easiest)
GitHub Actions schedule: add a scheduled workflow to each repository. In .github/workflows/sync-rules.yml: on: schedule: - cron: '0 9 * * 1' (every Monday at 9 AM). The job: runs rulesync pull, checks if the files changed (git diff), and if changed: creates a commit and pushes (or opens a PR). The schedule: ensures rules are checked weekly. If a ruleset was updated during the week: the next Monday's run picks it up.
GitLab CI schedule: create a scheduled pipeline (Settings → CI/CD → Pipeline Schedules). Cron: 0 9 * * 1 (weekly). The pipeline: runs rulesync pull and commits if changed. GitLab: supports pipeline schedules natively — no workflow file modification needed. The schedule: runs in the project's CI environment with the configured RULESYNC_API_KEY variable.
Limitations: the update: delayed by up to 7 days (weekly schedule) or 1 day (daily schedule). For most teams: weekly is sufficient (ruleset updates are infrequent). For teams that update rules frequently: daily schedule or Level 2 (webhook) is better. AI rule: 'Weekly schedule: sufficient for 80% of teams. Daily: for teams that update rules more than once per week. If rule updates are urgent (security): use webhooks (Level 2) for immediate propagation.'
Most teams update rulesets: once or twice per month. A weekly schedule: catches every update within 7 days. The 7-day delay: acceptable because rule updates are not urgent (the old rules are still valid — the new rules are better but the old ones are not wrong). For the 20% of teams with frequent updates (weekly or more): daily schedule or webhooks are better. Do not over-engineer: start with the simplest automation that meets your update frequency.
Step 2: Webhook-Triggered Pull (Level 2 — Immediate)
RuleSync webhooks: the dashboard sends a webhook when a ruleset is published or updated. Configure: Settings → Webhooks → Add Webhook. URL: your CI/CD endpoint that triggers the rule pull. Events: ruleset.published, ruleset.updated. When the webhook fires: the CI system triggers the rulesync pull for all affected projects. The update: propagates within minutes of the ruleset change.
GitHub Actions webhook: use a repository_dispatch event. The RuleSync webhook: sends a POST to the GitHub API (POST /repos/{owner}/{repo}/dispatches with event_type: 'rule-update'). The workflow: triggers on repository_dispatch with type 'rule-update'. The job: runs rulesync pull and creates a PR with the changes. Webhook fires → GitHub Actions triggers → PR opened → team merges. Total time: 2-5 minutes from ruleset update to PR.
For multiple repos: the webhook: sends to each affected repo's CI endpoint. Or: sends to a central orchestrator that fans out to affected repos. The fan-out approach: scales better (one webhook endpoint, the orchestrator handles distribution). AI rule: 'Webhooks + fan-out: the most scalable pattern. One webhook from RuleSync. One orchestrator. N repositories updated. The orchestrator: determines which repos are affected based on the ruleset-to-project mapping.'
Developers already understand the Dependabot model: a PR appears, shows what changed, and the team merges when ready. Automated rule PRs: the same model. The ruleset changes → a PR appears → the team reviews the rule diff → merges when ready. No new workflow to learn. No new tool to understand. The Dependabot analogy: the most effective way to explain automated rule PRs to a team that already uses Dependabot for dependency updates.
Step 3: Automated PRs (Level 3 — Dependabot-Style)
The Dependabot model for rules: when a ruleset updates, the automation: opens a PR in each affected repo with the updated rule files, a descriptive title ('Update AI rules to v2.5.0'), a body with the changelog (what changed and why), and passing CI checks (the PR triggers the normal CI pipeline, verifying the updated rules do not break anything). The team: reviews and merges the PR. This is the most complete automation: the update is visible, reviewable, and controllable.
Implementation: the central sync workflow (from the 'Sync Rules in GitHub Actions' tutorial) with webhook triggers instead of scheduled triggers. When a ruleset is updated: the webhook fires, the orchestrator identifies affected repos, the workflow clones each repo, runs rulesync pull, and opens a PR. The PR: one per repo. If a PR already exists: updated instead of duplicated. The team: sees one PR per rule update, not a stream of PRs.
Team autonomy: the automated PR model preserves team autonomy. The PR: is opened automatically, but merging is the team's decision. They can: merge immediately (trust the update), review the diff (verify the changes make sense for their project), decline (if the update conflicts with a team-specific exception), or delay (merge after the current sprint to avoid mid-sprint changes). AI rule: 'Automated PRs: the best balance of automation and human control. The system proposes. The team disposes. No force-push, no silent updates.'
The orchestrator: pushes updated rules directly to main in all repos. No PR. No review. No notification. A developer: wakes up to AI generating different code. Confusion. Frustration. Trust erosion. Automated updates must be visible: a PR that the team can review. Even if 95% of PRs are merged immediately: the 5% where the team catches an issue or delays for a sprint boundary justify the PR step. Automation: proposes. Humans: decide.
Rule Update Automation Summary
Summary of automating AI rule updates.
- Manual does not scale: at 50+ repos, guaranteed some repos are running outdated rules
- Level 1 — Scheduled: cron job runs rulesync pull weekly. Easiest setup. Up to 7-day delay
- Level 2 — Webhook: RuleSync sends webhook on ruleset update. CI triggers immediately. 2-5 min delay
- Level 3 — Automated PR: Dependabot-style PRs for rule updates. Visible, reviewable, controllable
- Fan-out: one webhook → orchestrator → N repos. Scales better than per-repo webhooks
- Team autonomy: automated PRs propose changes. Teams decide when to merge. No force-push
- Progression: start Level 1. Move to Level 2 when updates are frequent. Level 3 for full governance
- Goal: every repo updated within 1 hour (webhook) or 1 day (schedule) of a ruleset change