# Batch Workflows

> Provide the shared fan-out engine used by provider-specific Jira and GitHub batch presets to enqueue child workflows with inherited runtime and stable evidence.

- Skill: `moonladderstudios/batch-workflows` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add moonladderstudios/batch-workflows`
- Raw SKILL.md: https://api.skillmd.com/api/skills/moonladderstudios/batch-workflows/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: MoonLadderStudios (https://skillmd.com/u/moonladderstudios)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/moonladderstudios/batch-workflows

---


# Batch Workflows Fan-out Skill

## Purpose

Provide the internal fan-out engine used by the product-facing Batch Jira
Workflows and Batch GitHub Workflows presets. Each provider-specific preset owns
its fixed source model and curated destination list; this shared skill handles
queueing, runtime inheritance, publishing, idempotency, and evidence. Every child inherits the parent runtime
(`runtimeInheritance="caller"`) and a single shared publish policy. The parent
records a summary artifact that links every queued child workflow.

This parent batch skill does not publish repository changes itself. It records
child workflow queueing evidence in `artifacts/batch-workflows-result.json`;
each queued child workflow owns its configured publish outcome.

This replaces one-off prompts like "Queue Jira Verify for every MM issue in In
Progress" with a single batch run.

## Inputs (preset inputs / skill args)

- `jira_project_key` (string, required): Jira project key, for example `MM`.
- `jira_status` (string, required): Jira status name, for example `In Progress`.
- `run_ref` (string, required): child capability to run per target. Default
  `skill:jira-verify`. The Jira preset supports `skill:jira-verify`,
  `preset:jira-implement`, and `preset:jira-orchestrate`. The separate GitHub
  preset supports `preset:github-issue-implement` and
  `preset:github-issue-orchestrate` through the same helper.
- `max_workflows` (number, optional): hard cap on queued children. Default `25`.
- `constraints` (string, optional): shared input copied to every child.
- `run_verify` (boolean, optional): shared verification toggle copied to child
  implement presets. Default `true`.
- `additional_jql` (string, optional): advanced JQL AND-clause appended to the
  project/status query.
- `issue_range` (string, GitHub preset only): inclusive `START-END` search
  criteria. Numeric members are targets only when GitHub returns an open Issue.
- `repository` (string, optional): repository override when workflow context
  cannot infer it.
- `publish_mode` (string, optional): advanced child publish override, `none`,
  `branch`, `pr`, or `pr_with_merge_automation`. Default `none`.

## Workflow

1. **Resolve provider targets** into the canonical resolved-target shape and
   write them to `artifacts/batch-workflows-targets.json` before queueing.

   For Jira, use the trusted Jira tool surface to search and preview the list:

   ```text
   project = "<jira_project_key>" AND status = "<jira_status>"
   ```

   Append `additional_jql` only when provided. Each target is:

   ```json
   {
     "provider": "jira",
     "ref": "MM-123",
     "jiraIssue": {"key": "MM-123", "summary": "...", "description": "...",
                    "url": "...", "status": "In Progress", "assignee": "..."},
     "repository": "MoonLadderStudios/MoonMind"
   }
   ```

   Never use raw Jira credentials, web scraping, or guessed issue content to
   build the target list.

   For GitHub, the inclusive number range is search criteria, not a target list.
   GitHub issues and pull requests share numbers, and numbers may be absent. Use
   the helper's `--github-issue-range` plus `--github-repository` inputs so its
   trusted GraphQL `issue(number:)` lookup returns only open Issue objects.
   Closed issues, pull requests, absent numbers, and responses without an
   explicit open state are omitted normally and never become targets. The helper
   rejects numeric spans wider than 1,000 numbers before querying GitHub and
   writes the resolved target artifact before queueing.

2. **Queue child workflows** by running the helper:

   ```bash
   python3 "$MOONMIND_ACTIVE_SKILLS_DIR/batch-workflows/bin/batch_workflows.py" \
     --targets artifacts/batch-workflows-targets.json \
     --run-ref <curated provider-specific run ref> \
     --publish-mode <none|branch|pr|pr_with_merge_automation> \
     --constraints-file <optional path to shared constraints> \
     --run-verify | --no-run-verify \
     --update-status | --no-update-status \
     --max-workflows <cap>
   ```

   For the GitHub preset, replace `--targets` with:

   ```bash
   --github-issue-range <START-END> \
   --github-repository <owner/repository>
   ```

   For each resolved target the helper:
   - Auto-binds Jira issues into `skill:jira-verify` inputs (`jira_issue`,
     `jira_issue_key`, `repository`, `verification_mode`, `update_status`, and
     `constraints`).
   - Auto-binds Jira issues into `preset:jira-implement` inputs
     (`jira_issue`, `jira_issue_key`, `constraints`, and `run_verify`).
   - Auto-binds the same Jira inputs into `preset:jira-orchestrate`.
   - Auto-binds GitHub issue targets into the Implement and Orchestrate presets,
     including `run_verify`.
   - Stamps `runtimeInheritance="caller"` plus a fallback copy of the parent's
     effective runtime (mode/model/effort/provider profile) so children reuse the
     caller runtime even on deployments that do not honour the inheritance
     contract.
   - Applies the chosen `publish.mode` once to every child.
   - Assigns a stable idempotency key per `(batch scope, provider, target kind,
     target slug, target ref)` so rerunning the same batch does not create
     duplicate child workflows.
   - Submits via the internal Temporal execution API (`POST /api/executions`);
     `MOONMIND_URL` must point at the MoonMind API from the managed session.
   - When MoonMind supplies `MOONMIND_EXECUTION_FANOUT_BEARER_TOKEN`, forwards
     it as the execution-scoped bearer and marks create and describe calls as
     fan-out v1.

   If provider-specific input validation fails before a trustworthy target list
   can be written, still invoke the same helper exactly once with
   `--preflight-error <actionable message>` and
   `--requested-count <requested target count>`. The helper records the
   current execution's authoritative failure in the managed artifact spool and
   does not read targets or queue children. Do not handcraft or reuse a
   repo-local result artifact.

3. **Record the summary**: the helper writes `artifacts/batch-workflows-result.json`
   linking every queued child workflow id together with the resolved targets,
   skips, and errors, and prints a short `queued/skipped/errors` count summary.

## Safety constraints

- Require `MOONMIND_URL` to reach the MoonMind API; the legacy direct-DB queue is
  not supported.
- Never re-select provider/model/effort in the batch form; children inherit the
  caller runtime.
- Cap the resolved target list at `max_workflows`; a GitHub range's numeric
  width is not the target count. Bound GitHub discovery independently to a
  maximum numeric span of 1,000.
- Targets whose selected run capability is not auto-bindable are skipped with a
  clear `unsupported_target` reason rather than queued blindly.