audit-tickets

name	audit-tickets
description	Audit open GitHub issues against the codebase to find tickets that have already been implemented. Use when the user wants to triage, audit, or clean up GitHub issues.
disable-model-invocation	true
allowed-tools	Bash(gh *), Read, Grep, Glob, Agent

Audit GitHub Tickets

Scan open GitHub issues and check whether each one has already been addressed in the codebase. Post a comment on tickets that appear complete.

Arguments

The user may provide:

• A repo name (e.g., TangleML/tangle-ui, TangleML/tangle). If not specified, audit both TangleML/tangle-ui and TangleML/tangle.
• A limit (e.g., --limit 20). Default: all open issues.
• Specific issue numbers to audit (e.g., #1641 #575).

Process

Step 1: Fetch open issues

gh issue list -R <repo> --state open --limit 100 --json number,title,labels,body,author,assignees,comments

Step 2: Filter candidates

Skip issues that are:

• Epics (title contains "epic" or "[Epic]")
• Design/brainstorm issues (title starts with "design:" or "Brainstorm:")
• Issues created in the current session (issues you just created in this conversation)
• Recently audited — issues that already have a comment containing 🤖 This is an AI-generated audit posted within the last 14 days. Check comments when fetching issues and skip any that match.

Audit everything else, including feature requests and vague tickets. The goal is to close as many tickets as possible.

Step 3: Verify each candidate against the codebase

For each candidate issue, use the Explore agent to search the codebase:

• Search for the specific files, components, functions, or patterns described in the issue
• Read the relevant code to confirm the feature/fix exists
• Determine one of these statuses:
  • Done — The described feature/fix is fully implemented
  • Partial — Some aspects are done, others remain
  • Open — Not implemented, clear what needs to be done
  • Vague — Cannot determine if implemented because the issue lacks clear acceptance criteria or sufficient detail

Be thorough but efficient. Use targeted searches (Grep/Glob) for quick checks, and Explore agents for deeper investigation when needed. Run multiple checks in parallel where possible.

Step 4: Report findings

Present a summary table to the user:

Issue	Title	Status	Evidence

Group by status: Done first, then Partial, then Vague, then Open.

Step 5: Post comments (with user approval)

Ask the user which issues they'd like to comment on. Then for each approved issue:

1. Look up the author and any assignees/commenters on the issue
2. Post a comment using the appropriate template based on status:

For Done issues:

> 🤖 This is an AI-generated audit of this ticket.

@<author> [and @<assignees/commenters>] — What is the status of this issue? It appears to already be complete.

<Evidence from the codebase with specific file paths>

Can this be closed?

For Vague issues (cannot determine implementation status):

> 🤖 This is an AI-generated audit of this ticket.

@<author> [and @<assignees/commenters>] — This ticket doesn't have enough detail to determine whether it's been addressed. Could you add more context — specific acceptance criteria, expected behavior, or details on what "done" looks like? Otherwise, can this be closed?

For Partial issues:

> 🤖 This is an AI-generated audit of this ticket.

@<author> [and @<assignees/commenters>] — This issue appears to be partially addressed. Here's what we found:

**Implemented:**
<what's done, with file paths>

**Still missing:**
<what remains>

Can the scope be updated, or should this be closed and a new issue opened for the remaining work?

Codebase locations

This skill runs from within the frontend repository. Use the current working directory as the frontend root.

• Frontend (tangle-ui): the current working directory (this repo)
• Backend (tangle): a sibling repository containing the backend. It is typically named tangle, but the user may have cloned it under a different name. To locate it, first look for a directory named tangle adjacent to the current working directory; if not found, look for any sibling whose name suggests a backend (e.g. contains backend, server, or api). If still ambiguous, ask the user to confirm the path before searching.

Important

• Never close issues directly — always comment and ask
• Always prefix comments with the AI audit disclaimer
• Ping the issue author and any assignees/commenters
• Include specific file paths as evidence
• For "Partial" issues, clearly state what's done and what's remaining
• Do not audit issues that were just created in the current conversation