From wiki-page-author
Runs an ultra review of the proposed tickets in a pull request for clarity, implementability, and ticket-author compliance, optionally posting findings as PR comments. Use ONLY when the user explicitly asks for an ultra review of the tickets, e.g. "ultra-review the tickets", "ticket-ultra-review this PR", or "run an ultra review on the proposed tickets". A generic request to review or lint the tickets does NOT qualify — the user must ask for the ultra review by name. Do NOT use for code review, for general review-this-PR requests, or when a PR is merely mentioned.
How this skill is triggered — by the user, by Claude, or both
Slash command
/wiki-page-author:ticket-ultra-reviewThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Provide an ultra review of the tickets in the given pull request.
Provide an ultra review of the tickets in the given pull request.
ODD tickets are out of scope. Any proposed-tickets/*.md file whose labels include type::ODD is excluded from the whole review — no step or agent below reviews or flags it.
Agent assumptions (applies to all agents and subagents):
Every comment posted to the PR — inline, summary, no-issues note — leads with 🤖 Generated with Claude Code on its own line.
To do this, follow these steps precisely:
Launch a haiku agent to check if any of the following are true:
gh pr view <PR> --comments for comments left by claude)If any condition is true, stop and do not proceed.
Note: Still review Claude-generated PRs.
Launch the following agents in parallel to independently review the changes. Each agent should return the list of issues, where each issue includes a description and the reason it was flagged (e.g. "unclear ticket", "not implementable", "ticket-author rule violation"). The agents should do the following:
Agent 1: ticket-reviewer agent, one per ticket (parallel subagents with the others)
For each changed non-ODD ticket file, launch the ticket-reviewer agent once, passing it that one ticket as its target. These per-ticket runs cover the per-ticket rules and cannot run the cross-ticket checks (the whole-batch run does those), so ignore any cross-ticket UNVERIFIED they report. Each violation is an issue.
Agent 2: ticket-reviewer agent, whole batch (parallel subagent with the others)
Launch the ticket-reviewer agent once over the whole non-ODD batch — every non-ODD ticket in proposed-tickets/, not only the changed ones — so it sees the whole batch at once. Its unique contribution is the cross-ticket checks — at most one epic per batch, epic: references resolving; keep those findings as issues.
Agent 3: sonnet implementability agent, whole batch (parallel subagent with the others) Read every changed non-ODD ticket in one pass and judge it at two levels.
type: epic files): following the ticket's links, could an LLM coding agent with the wiki and codebase in context deliver this ticket without inventing an undecided choice, and could it tell when the work is done?CRITICAL: We only want HIGH SIGNAL issues. Flag issues where:
Do NOT flag:
If you are not certain an issue is real, do not flag it. False positives erode trust and waste reviewer time.
In addition to the above, each subagent should be told the PR title and description. This will help provide context regarding the author's intent.
Once every agent above has returned, deduplicate the whole-batch reviewer's per-ticket findings against the per-ticket runs on file + rule + verbatim offending text, keeping its cross-ticket findings. The parallel agents cannot see each other's output, so this dedup happens here, after they all return — not inside any agent.
For each issue found in the previous step, launch parallel subagents to validate the issue. These subagents should get the PR title and description along with a description of the issue. The agent's job is to review the issue to validate that the stated issue is truly an issue with high confidence. For a ticket-author rule violation, the agent should validate that the rule that was violated is scoped for this file and is actually violated. For an implementability issue, the agent should validate that the problem holds when the ticket and the context it links are read together. Use sonnet subagents to validate.
Filter out any issues that were not validated in step 3. This step will give us our list of high signal issues for our review.
Output a summary of the review findings to the terminal:
If --comment argument was NOT provided, stop here. Do not post any GitHub comments.
If --comment argument IS provided and NO issues were found, post a summary comment using gh pr comment and stop.
If --comment argument IS provided and issues were found, continue to step 6.
Create a list of all comments that you plan on leaving. This is only for you to make sure you are comfortable with the comments. Do not post this list anywhere.
Post inline comments for each issue using mcp__github_inline_comment__create_inline_comment with confirmed: true. For each comment:
IMPORTANT: Only post ONE comment per unique issue. Do not post duplicate comments.
Post a summary comment on the pull request using gh pr comment. Lead with the whole-batch ticket-reviewer run's N of M tickets ready. line, then list the issues you could not post inline (whole-file, structural, or cross-ticket findings with no diff line) with their file, rule, and why.
Use this list when evaluating issues in Steps 2 and 3 (these are false positives, do NOT flag):
skills/ticket-author/SKILL.md allows — a ticket may defer spec detail to a linked source, name behaviours instead of enumerating test cases, and leave technical decisions to the wiki or codeNotes:
--comment argument is provided, post a comment with the following format:No issues found. Checked for ticket-author compliance, clarity, and implementability.
https://github.com/owner/repo/blob/$(git rev-parse HEAD)/foo/bar will not work, since your comment will be directly rendered in Markdown.L4-7)npx claudepluginhub sheriff-stuff/scribe-skills --plugin ticket-authorFetches up-to-date documentation from Context7 for libraries and frameworks like React, Next.js, Prisma. Use for setup questions, API references, and code examples.
Applies a firm's KYC/AML rules grid to parsed onboarding records: assigns risk rating, checks required documents, outputs rule outcomes with citations, and routes for escalation.
Generates daily or weekly digests of activity from connected sources (chat, email, docs, tasks, CRM), highlighting action items, decisions, mentions, and project updates.