/arn-spark-report

name: arn-spark-report
description: >-
  This skill should be used when the user says "spark report", "report spark issue",
  "spark broke", "arn-spark-report", "greenfield issue", "report greenfield problem",
  "report spark problem", "diagnose spark", "spark doctor",
  "spark bug", "spark not working",
  or wants to report a problem with an Arness Spark workflow skill.
  Invokes the arn-spark-doctor agent to diagnose the issue, then files a GitHub
  issue on the Arness plugin repository. Do NOT use this for filing issues on the
  user's own project — use /arn-code-create-issue for that.
version: 1.0.0

Arness Spark Report

Report an Arness Spark workflow issue by running a diagnostic and filing a GitHub issue on the Arness plugin repository. The arn-spark-doctor agent analyzes Spark configuration and behavior — it never reads project source code or business logic.

Workflow

Step 0: Smart Routing

Before proceeding, check whether the user's issue actually belongs to a different Arness plugin:

1. Read the `## Arness` section from the project's CLAUDE.md (if it exists). 2. Check the user's description for keyword signals:

• **Code keywords:** "plan", "spec", "execute", "taskify", "swift", "standard", "batch", "ship", "PR", "review-pr", "assess", "catch-up", "document-project", "save-plan"
• **Infra keywords:** "deploy", "Dockerfile", "container", "IaC", "terraform", "pipeline", "CI/CD", "environment", "secrets", "monitor", "infrastructure"

3. If Code signals detected: "This sounds like an Arness Code issue. Run `/arn-code-report` instead." 4. If Infra signals detected: "This sounds like an Arness Infra issue. Run `/arn-infra-report` instead." 5. If the user confirms it is actually a Spark issue, proceed.

---

Step 1: Explain the Process

Inform the user:

"I'll help you report an Arness Spark workflow issue. Here's how this works: 1. You describe what happened — which skill you used and what went wrong 2. I'll run a diagnostic that checks Spark's configuration and expected behavior (I won't read your project code) 3. You'll review the issue report before it's submitted 4. The issue gets filed on the Arness plugin repository for the maintainers

Your project code and business logic are never included in the report."

---

Step 2: Gather User Description

Ask the user to describe the issue (free-form text, not AskUserQuestion — this is open-ended):

"What happened? Which Arness Spark skill were you using and what went wrong?"

Let the user type a free-form description. This becomes the `user_description` for the diagnostic.

---

Step 3: Check Prerequisites

1. Detect the plugin's GitHub repository:

• Read the `repository` field from `${CLAUDE_PLUGIN_ROOT}/.claude-plugin/plugin.json`. Parse `owner/repo` from the URL.
• Fallback: `git -C ${CLAUDE_PLUGIN_ROOT} remote get-url origin`. Extract `owner/repo` from the URL (strip `.git` suffix and `https://github.com/` or `git@github.com:` prefix).

2. Check `gh auth status` — user must be authenticated to file issues.

3. Read plugin version from `${CLAUDE_PLUGIN_ROOT}/.claude-plugin/plugin.json`.

4. Read `## Arness` config from the project's CLAUDE.md (if it exists).

If GitHub access is not available, offer an alternative: generate the diagnostic report as a local file (`arness-spark-report-<YYYY-MM-DD>.md` in the project root) the user can manually submit.

---

Step 4: Invoke arn-spark-doctor

Spawn the `arn-spark-doctor` agent via the Task tool, passing the model from `.arness/agent-models/spark.md` as the `model` parameter (see `plugins/arn-spark/skills/arn-spark-ensure-config/references/ensure-config.md` "Dispatch convention" for fallback). Context:

• The user's description of the issue
• Project root path
• `## Arness` config content (or "not configured")
• Plugin version
• Instruction to read the knowledge base at `${CLAUDE_PLUGIN_ROOT}/skills/arn-spark-report/references/spark-knowledge-base.md`

Wait for the agent to complete and collect the diagnostic report.

---

Step 5: Compose and Review Issue

Assemble the GitHub issue using the template from `${CLAUDE_PLUGIN_ROOT}/skills/arn-spark-report/references/issue-template.md`:

• Include the user's original description in the "User Report" section
• Include the doctor's diagnostic findings (ISSUE items only, not OK items)
• Include the doctor's assessment
• Include plugin version and relevant config state
• Include environment info (OS, Git, GitHub, gh CLI, Node.js, Playwright status)
• Exclude any project code or business logic

Present the complete draft to the user, then request explicit consent.

Ask (using `AskUserQuestion`):

> **This report will be filed as a public GitHub issue on the Arness repository.** It contains only Arness configuration state and diagnostic findings — no project source code or business logic. Please review the report above carefully. > > 1. Submit — I've reviewed it and consent to filing this publicly > 2. Save locally — save as a file instead of submitting > 3. Edit first — let me modify the report before deciding

• If **Submit**: proceed to Step 6.
• If **Save locally**: save as `arness-spark-report-<YYYY-MM-DD>.md` in the project root. Inform the user. Do not submit.
• If **Edit first**: let the user modify the draft, then ask again.

---

Step 6: Submit Issue

The `arn-spark-report` label must already exist on the plugin repository (maintained by the plugin maintainers, not created by this skill).

1. Create the issue with the `arn-spark-report` label:

   gh issue create --repo <owner/repo> --title "<title>" --body "<body>" --label "arn-spark-report"

2. If the command fails because the label does not exist, retry without `--label` and inform the user that the issue was filed without a label.

3. Report the issue URL to the user.

If submission fails for other reasons (permissions, network), save the report as `arness-spark-report-<YYYY-MM-DD>.md` in the project root and inform the