IronClaw Workflow Orchestrator

Overview

Use this skill to install and maintain a complete project workflow as routines, not core code changes. It maps GitHub webhook events plus scheduled checks into plan/update/implement/review/merge loops with explicit staging-batch analysis.

Workflow

1. Gather workflow parameters.
2. Verify runtime prerequisites.
3. Install or update routine set from templates.
4. Run a dry test with event_emit.
5. Monitor outcomes and tune prompts/filters.

Parameters

Collect these values before creating routines:

• repository: owner/repo (required)
• maintainers: GitHub handles allowed to trigger implement/replan actions
• staging_branch: default staging
• main_branch: default main
• batch_interval_hours: default 8
• implementation_label: default autonomous-impl

Prerequisites

Before installing routines, verify:

• Routines system enabled.
• GitHub tool authenticated (for issue/PR/comment/status operations).
• GitHub webhook delivery configured to POST /webhook/tools/github.
• Webhook HMAC secret configured in the secrets store as github_webhook_secret (required for GitHub webhook delivery).
• Events can also be emitted via event_emit tool calls for testing or when webhook ingestion is not yet configured.

Install Procedure

1. Open workflow-routines.md.
2. For each template block:

• replace placeholders ({{repository}}, {{maintainers}}, branch names)
• call routine_create

3. If a routine already exists:

• use routine_update instead of creating duplicates
• keep names stable so long-lived metrics/history stay intact

4. Confirm install with routine_list and routine_history.

Routine Set

Install these routines:

• wf-issue-plan: on issue.opened or issue.reopened, generate implementation plan comment/checklist.
• wf-maintainer-comment-gate: on maintainer comments, decide update-plan vs start implementation.
• wf-pr-monitor-loop: on PR open/sync/review-comment/review, address feedback and refresh branch.
• wf-ci-fix-loop: on CI status/check failures, apply fixes and push updates.
• wf-staging-batch-review: every 8h, review ready PRs, merge into staging, run deep batch correctness analysis, fix findings, then merge staging -> main.
• wf-learning-memory: on merged PRs, extract mistakes/lessons and write to shared memory.

Event Filters

Prefer top-level filters for stability:

• repository_name (string, e.g. owner/repo)
• sender_login (string)
• issue_number / pr_number
• ci_status, ci_conclusion
• review_state, comment_author

Use narrow filters to avoid accidental triggers across repos.

Operating Rules

• All implementation work must occur on non-main branches.
• PR loop must resolve both human and AI review comments.
• On conflicts with origin/main, refresh branch before continuing.
• Staging-batch routine is the only path for bulk correctness verification before mainline merge.
• Memory update routine runs only after successful merge.

Validation

After install, run:

1. event_emit with a synthetic issue.opened payload for the target repo.
2. Confirm at least one routine fired.
3. Check corresponding routine_history entries.
4. Confirm no unrelated routines fired.

When To Update Templates

Update this skill when:

• GitHub event names/payload fields change.
• Team review policy changes (e.g., staging cadence, maintainer gates).
• New CI policy requires different failure routing.