Codex

research-cite

Generate properly formatted citation from research corpus

104 stars

Best use case

research-cite is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

It is a strong fit for teams already working in Codex.

Generate properly formatted citation from research corpus

Teams using research-cite should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/research-cite/SKILL.md --create-dirs "https://raw.githubusercontent.com/jmagly/aiwg/main/.agents/skills/research-cite/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/research-cite/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How research-cite Compares

Feature / Agentresearch-citeStandard Approach
Platform SupportCodexLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Generate properly formatted citation from research corpus

Which AI agents support this skill?

This skill is designed for Codex.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Research Cite Command

Generate a properly formatted, policy-compliant citation from the research corpus.

## Instructions

When invoked, generate a correct citation:

1. **Locate Source**
   - Search `.aiwg/research/sources/` and `.aiwg/research/findings/` for the specified reference
   - Match by REF-XXX identifier, author name, or keyword
   - If multiple matches, present options for user selection

2. **Load Metadata**
   - Extract frontmatter: ref_id, title, authors, year, DOI, source_type
   - Load quality assessment if available
   - Determine GRADE level

3. **Generate Citation**
   - Format @-mention citation with full path
   - Include page numbers if specified
   - Apply GRADE-appropriate hedging language
   - Include quality level annotation

4. **Output Formats**
   - `inline` - Ready to paste into markdown
   - `bibtex` - BibTeX format
   - `reference` - Full reference section entry

## Arguments

- `[ref-id or keyword]` - REF-XXX identifier or search keyword (required)
- `--format [inline|bibtex|reference]` - Output format (default: inline)
- `--page [n]` - Include page reference
- `--quote "[text]"` - Include direct quote

## Example

```
/research-cite REF-020 --page 4

Output:
According to @.aiwg/research/findings/REF-020-tree-of-thoughts.md (p. 4),
Tree of Thoughts enables "deliberate decision making" through systematic
exploration of reasoning paths.

GRADE: HIGH (peer-reviewed conference, NeurIPS 2023)
Hedging: "demonstrates" / "shows" appropriate for HIGH quality evidence
```

## References

- @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/rules/citation-policy.md - Citation enforcement rules
- @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/schemas/research/frontmatter-schema.yaml - Source metadata
- @.aiwg/research/docs/grade-assessment-guide.md - GRADE levels

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