agentic/code/frameworks/research-complete/skills/research-cite/SKILL.md
Format citations and generate bibliographies
npx skillsauth add jmagly/aiwg research-citeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Generate properly formatted, policy-compliant citations from the research corpus.
When invoked, generate correct citations:
Locate Source
.aiwg/research/sources/ and .aiwg/research/findings/ for specified referenceLoad Metadata
Generate Citation
Output Formats
inline - Ready to paste into markdown with GRADE-appropriate languagebibtex - BibTeX format for reference managementreference - Full reference section entryapa - APA 7th edition formatchicago - Chicago Manual of Style format[ref-id or keyword] - REF-XXX identifier or search keyword (required)--format [inline|bibtex|reference|apa|chicago] - Output format (default: inline)--page [n] - Include page reference--quote "[text]" - Include direct quote with citation--hedging [suggest|none] - Include hedging language suggestions (default: suggest)# Basic inline citation
/research-cite REF-022
# Citation with page reference
/research-cite REF-022 --page 4
# Citation with quote
/research-cite REF-022 --page 4 --quote "deliberate decision making"
# BibTeX format for bibliography
/research-cite REF-022 --format bibtex
# Search by keyword
/research-cite "autogen multi-agent" --format inline
# Multiple citations for bibliography
/research-cite REF-022 REF-001 REF-013 --format reference
/research-cite REF-022 --page 4
Output:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Citation (inline format):
According to @.aiwg/research/findings/REF-022-autogen.md (p. 4),
AutoGen enables "flexible conversation patterns" through multi-agent
collaboration.
GRADE Assessment: MODERATE
Source: arXiv preprint (peer-reviewed conference version available)
Quality: Moderate (single study, not systematic review)
Recommended Hedging Language:
✓ APPROPRIATE: "Research suggests...", "Evidence indicates..."
✗ TOO STRONG: "Research proves...", "Evidence demonstrates..."
✗ TOO WEAK: "Some sources claim...", "Anecdotal reports suggest..."
Usage Example:
Multi-agent conversation patterns suggest flexible agent orchestration
is feasible for complex workflows (@.aiwg/research/findings/REF-022-autogen.md,
p. 4), though scalability limits require further investigation (GRADE: MODERATE).
/research-cite REF-022 --format bibtex
Output:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
@article{wu2023autogen,
title={AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation},
author={Wu, Qingyun and Bansal, Gagan and Zhang, Jieyu and Wu, Yiran and Zhang, Shaokun and Zhu, Erkang and Li, Beibin and Jiang, Li and Zhang, Xiaoyun and Wang, Chi},
journal={arXiv preprint arXiv:2308.08155},
year={2023},
doi={10.48550/arXiv.2308.08155},
note={GRADE: MODERATE - arXiv preprint}
}
/research-cite REF-022 --format reference
Output:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., Li, B.,
Jiang, L., Zhang, X., & Wang, C. (2023). AutoGen: Enabling Next-Gen
LLM Applications via Multi-Agent Conversation. *arXiv preprint*
arXiv:2308.08155. https://doi.org/10.48550/arXiv.2308.08155
GRADE: MODERATE
Internal Reference: @.aiwg/research/findings/REF-022-autogen.md
PDF: @.aiwg/research/sources/REF-022.pdf
Citations automatically include hedging guidance based on evidence quality:
| GRADE Level | Appropriate Language | Avoid | |-------------|---------------------|-------| | HIGH | "demonstrates", "shows", "confirms" | Uncertainty language | | MODERATE | "suggests", "indicates", "supports" | Definitive claims | | LOW | "limited evidence", "preliminary findings" | Strong claims | | VERY LOW | "anecdotal", "exploratory", "reports suggest" | Confident claims |
Generate bibliography for multiple sources:
/research-cite REF-001 REF-013 REF-022 REF-057 --format reference
Output:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## References
Feng, Y., et al. (2024). The Landscape of Emerging AI Agent Architectures...
GRADE: MODERATE - @.aiwg/research/findings/REF-001-production-agentic.md
Hong, S., et al. (2023). MetaGPT: Meta Programming for Multi-Agent Systems...
GRADE: HIGH - @.aiwg/research/findings/REF-013-metagpt.md
Wu, Q., et al. (2023). AutoGen: Enabling Next-Gen LLM Applications...
GRADE: MODERATE - @.aiwg/research/findings/REF-022-autogen.md
Schmidgall, S., et al. (2024). Agent Laboratory: Using LLM Agents...
GRADE: HIGH - @.aiwg/research/findings/REF-057-agent-laboratory.md
All citations are validated against:
.aiwg/research/When writing documentation:
/research-cite REF-XXX to get citationdata-ai
Report which research-corpus radar sidecars are overdue for refresh. Computes staleness (days since last refresh vs the cadence window) for every radar, sorted most-overdue-first. Runs via `aiwg corpus radar-status`.
data-ai
Aggregate research-corpus radar sidecars into a corpus or per-cluster freshness report — totals, overdue count, per-cluster / per-GRADE / per-trajectory breakdowns, an overdue table, and per-radar rationale snippets. Runs via `aiwg corpus radar-report`.
testing
Scaffold radar/freshness sidecars for research-corpus REFs. Pulls title/authors from the citation sidecar and GRADE from the analysis doc, defaults the refresh cadence from GRADE and the cluster from a corpus-local map, and stamps documentation/radar/REF-XXX-radar.md. Runs via `aiwg corpus radar-init`.
data-ai
Compute an entity's publication trajectory — per-year paper counts, topic drift, hot-streak detection (≥3 consecutive A-grade years), and career phase. Runs via `aiwg corpus profile-temporal`.