Maintaining accuracy and proper attribution for factual content is a constant task for anyone managing information-rich web pages. Broken links, inconsistent formatting, or missing details in citations can undermine credibility. This is where citation-fixer provides assistance. This AI agent skill is designed to audit pages and automatically repair common citation issues, helping content creators, researchers, and editors maintain high standards of factual integrity.

The citation-fixer skill focuses on ensuring that all factual claims on a page carry inline [Source: ...] citations in a standardized format. It's built to address the practical challenges of citation management without requiring manual, page-by-page review for every potential error. Instead, it offers an automated approach to identifying and rectifying problems, streamlining the process of maintaining a reliable knowledge base.

How citation-fixer Operates

The operation of citation-fixer is structured into five distinct phases, moving from a broad assessment to granular repairs and, finally, a comprehensive report. The skill utilizes core tools such as search, get_page, put_page, and list_pages to interact with content effectively.

1. List and Scan Pages: The process begins by listing all specified pages and then scanning each for compliance with the established citation standard. This initial pass identifies the scope of the audit.

2. Flagging Issues: During the scan, the skill flags various types of citation problems. These include facts presented without any inline citation, existing citations that are missing critical details such as dates or source types, and citations that simply do not adhere to the canonical formatting standard.

3. Rewriting Malformed Citations: Once identified, citations with incorrect formatting are rewritten to the standard. For example, a citation like [Source: Blog Post on AI, January 2023] might be standardized to [Source: AI Trends Blog, 2023-01-15] if that's the canonical format expected, assuming the necessary data can be extracted. The skill does not invent citation data; it only reconfigures what is present or identifies what is missing.

4. Resolving Broken Tweet References: A common issue is broken links to social media posts, particularly tweets (now X posts). The skill addresses this by resolving broken tweet references via the X API. For instance, if a page contains a malformed or outdated reference to a tweet, citation-fixer extracts key information like the author's handle, the quoted text, and the date of the post. Using this data, it then constructs a deterministic URL in the format https://x.com/<handle>/status/<id>. This is a critical point: all links generated by the skill are deterministic, meaning they are directly built from API-provided data, not composed by an LLM guessing an ID. If the tweet cannot be resolved via the API due to deletion or an unrecoverable reference, it will be flagged as an unfixable issue, rather than attempting to guess a tweet ID or URL.

5. Reporting Counts: Upon completion, citation-fixer provides a summary report. This report details the number of pages scanned, the total citations found, how many issues were fixed, the number of tweet references successfully resolved, and any remaining gaps or unresolved problems that require manual attention.

Triggering and Important Considerations

The citation-fixer skill can be activated using straightforward triggers: 'fix citations', 'citation audit', or 'check citations'. These commands initiate the full five-phase process, providing flexibility depending on whether a full repair or just an assessment is needed.

It is important to understand the scope and limitations of the tool. The skill is not designed for inventing citations for unsourced facts; instead, it flags these instances, indicating where human intervention is needed to find or add a source. It also does not delete uncitable content, nor does it attempt to guess tweet IDs for unresolvable references. Its focus is on auditing existing citations and repairing issues based on verifiable data and predefined formatting rules, ensuring that all constructed links are deterministic and reliable.

Frequently Asked Questions

Q: Can citation-fixer add citations to facts that currently have none? A: No. The skill flags facts that lack any citation. It does not invent sources or add new citations to unsourced content; that requires human input and research.

Q: What if a referenced tweet has been deleted? A: If a tweet cannot be resolved via the X API, perhaps due to deletion, the skill will flag the reference as an unresolved issue. It will not attempt to guess or create a non-existent URL.

Q: How does the skill handle different citation styles (e.g., APA, MLA)? A: The skill operates on a single canonical standard format. While it rewrites malformed citations to that standard, it's not a multi-style citation manager. The expected standard [Source: ...] format must be defined for its operation.

Utilizing citation-fixer helps maintain the integrity of published information by automating a critical aspect of content maintenance. This can significantly reduce the overhead associated with manual citation checks, allowing teams to focus on content creation and verification rather than formatting minutiae.