Citation Manager

---
name: citation-manager
description: Manage citations and references — collecting sources, organizing libraries, formatting bibliographies, and keeping reference integrity. Use when writing anything that cites sources.
category: ai-research
---

# Citation Manager

Citations are the paper's supply chain: every claim traceable to its source. Managing them means 
collecting sources systematically, organizing them so you can find anything, formatting 
consistently, and — critically — verifying that every citation is real and says what you claim.

## Overview

The workflow: capture sources as you read (never "I'll find it later"), store them with notes on 
why they matter, cite-while-you-write with keys instead of hand-typed references, and generate the 
bibliography from the library. The integrity rule: every in-text citation matches a real source 
you've read, and every bibliography entry is cited. Reference managers automate formatting; they 
don't automate honesty.

## When to use

- Writing papers, theses, reports, or any document with references.
- Building a personal research library over time.
- Collaborating: shared libraries keep everyone's citations consistent.
- Cleaning up a manuscript's references before submission.

## Core concepts

- **Capture at reading time**: save the full citation + PDF + your notes the moment you read 
something useful. "I'll relocate it later" fails reliably.
- **Cite keys**: stable keys (`smith2024attention`) used in drafts; the formatted reference is 
generated, never hand-typed. Renumbering-proof.
- **Annotated library**: each entry with a one-line note — what it claims, why you cited it. 
Future you will thank present you.
- **Style consistency**: one citation style per document (APA, IEEE, Chicago…), applied by the 
tool, not by hand. Switching styles should be one setting.
- **Reference integrity**: every citation verified — the source exists, you read the relevant 
part, it supports your claim. AI-generated citations must be checked individually.
- **Deduplication**: the same source saved three ways creates triple entries. Merge duplicates; 
keep one canonical record.

## Practical workflow

1. Set up the library before writing: folder structure or tags by project/topic.
2. For each source read: save citation metadata, the file, and a 2-line note (claim + relevance).
3. Write with cite keys; never type a formatted reference by hand.
4. Before submission: run the integrity check — every key resolves, every entry is cited, no 
duplicates, style consistent.
5. Verify AI-suggested citations individually: confirm the source exists and supports the claim.
6. Archive the library with the manuscript version so the reference set is reproducible.

```text
Source note template:
KEY:   smith2024attention
CLAIM: <what the paper shows, in your words>
USE:   <why you're citing it — which argument it supports>
READ:  <which sections you actually read>
LIMIT: <caveats relevant to your use>
```

## Common pitfalls

- **Phantom citations**: references that don't exist — the classic AI failure mode. Verify every 
single one.
- **Citation without reading**: citing the abstract or a citation-of-a-citation. Read the part you 
rely on.
- **Hand-formatted references**: typed bibliographies that break on every edit. Generate from keys.
- **No notes**: a library of 500 PDFs with no memory of why each mattered. Annotate at capture time.
- **Style drift**: mixed formats in one document. Set the style once; let the tool enforce it.
- **Uncited bibliography entries**: sources in the list that nothing cites (or claims nothing 
supports). Audit both directions.