literature-triage-matrix

name	literature-triage-matrix
description	Turn a list of papers (Zotero collection, Obsidian cluster, manual list) into a compact comparison matrix written to .research/literature_matrix.md, instead of generic per-paper summaries. Use when the user asks to "make a literature matrix", "compare these papers by method/data/limitations", or "decide which papers are central to my review". If the user says "extract the claims from these papers": cross-paper comparison matrix → this skill; claims from their own manuscript draft → `paper-memory-builder`; per-cited-paper Key Findings → `paper-summarize`. This skill compares a KNOWN paper set; to turn a research area into a go/no-go decision on a candidate thesis/proposal topic, use `gap-to-topic`.
compatibility	Pure agentskills.io-spec skill. Verified loaded by NousResearch/hermes-agent 0.13.0 via `hermes skills install <github-raw-url>` (security scan: SAFE, status: enabled). Should work on any agentskills.io-compliant host without modification.

literature-triage-matrix

Produce a single comparison table over a set of papers, optimized for review-writing decisions. The matrix lets the user (or a downstream writing skill) see at a glance which papers cluster together by method, which carry the load on a particular claim, and which can be cited in passing vs deeply engaged.

This skill avoids the common AI failure mode of "give me a summary of each paper" — which produces N independent bullet lists that the user then has to manually compare.

When to use

Trigger phrases:

• "Make a literature matrix for these papers."
• "Compare these papers by method, data, claims, and limitations."
• "Help me decide which papers are central to my review."
• "Build a triage table over my Zotero collection / Obsidian cluster."

Not for:

• A 5-page narrative literature review — that's a writing task.
• Citation formatting — Zotero and the writing skill handle that.
• Single-paper deep dive — use paper-memory-builder.

Inputs

In priority order (cheapest to most expensive):

0. Manual paper list — a Markdown bullet list of titles + DOIs (or arXiv IDs) the user pastes directly into the chat. Lowest-friction entry; works without any other research-hub setup. Treat each line as one row in the matrix; fill cells from your own knowledge

   • DOI lookup if the title is famous, otherwise mark ? and ask the user.

   Example minimal input:

   - "Memory enables ToM-like behaviour in LLM poker agents", arXiv:2604.04157
   - "Multi-agent LLM social learning", arXiv:2604.02677
   - "Triadic Loop alignment framework", arXiv:2604.18850
   

1. .research/literature_matrix.md — if it already exists, parse it first. Append-only by convention; only re-emit a row if the underlying paper changed materially.

2. Obsidian cluster notes under raw/<cluster>/*.md — these have structured frontmatter (title, authors, year, doi) plus research-hub-generated Summary / Key Findings / Methodology / Relevance sections. Read these first; they're cheaper than PDFs.

3. Zotero collection metadata via local API (fast) — add child note contents only if Obsidian doesn't have the paper.

4. NotebookLM downloaded briefs under .research_hub/artifacts/ — if the user has already generated a brief on the cluster, mine it for cross-paper comparisons.

5. Raw PDFs — only as last resort, and only the abstract + first 2 pages + conclusion. PDFs are token-expensive.

Output

Write to <project-root>/.research/literature_matrix.md. Append by default; rewrite the whole table only if the user explicitly says "regenerate from scratch".

Markdown table columns (customize per request, but default is):

| Citation | Question | Method | Data / study area | Main claim | Evidence | Limitation | Relevance | Use as |
|---|---|---|---|---|---|---|---|---|
| Smith 2024 | How does adaptation affect flood risk? | mesa-based ABM, 10k agents | Houston synthetic | Adaptation cuts loss 18% | simulation | single basin, no validation | High — direct precedent | Lit review §2 |
| Jones 2023 | Hydraulic coupling in flood ABMs | Coupled ABM-2D | Galveston | Coupling reduces RMSE 22% | empirical | calibration window narrow | Medium — methods | Methods §3 |

Column meanings:

• Citation — short reference (e.g., Smith 2024); leaving full citation to Zotero.
• Question — the paper's research question, one sentence.
• Method — model class + key technical detail.
• Data / study area — datasets used + geographic / temporal scope.
• Main claim — single most-cited finding, one sentence.
• Evidence — what kind: simulation / empirical / theoretical / review.
• Limitation — most important caveat, one sentence.
• Relevance — High / Medium / Low + one-phrase justification.
• Use as — where in the user's manuscript or review this paper fits (Lit review, Methods, Discussion, citation-only).

Token-saving behavior

• Read existing .research/literature_matrix.md first; only emit rows for papers not already covered or whose underlying note changed.
• Prefer Obsidian frontmatter + the Summary section over re-reading the PDF.
• Use stable paper identifiers (DOI or arXiv ID) so future requests can reference rows by Smith 2024 (10.1234/abcd) instead of pasting paragraphs.
• Cap PDF reads at 3 per session; tell the user if more are needed.

Output format for the user

After writing the matrix, print:

[literature-triage-matrix]
  Wrote/appended: .research/literature_matrix.md
  Papers in matrix: 12 (4 new this run)
  Skipped (already in matrix, unchanged): 8
  Read full PDF for: 1 (Smith 2024 — no Obsidian note found)
  Suggested next: review High-relevance rows in .research/literature_matrix.md

What NOT to do

• Don't write to .paper/ — that's paper-memory-builder's territory.
• Don't fabricate findings if a paper's note is sparse. Emit the row with ? in unknown columns and a note: (Obsidian note incomplete — run research-hub auto with --max-papers 1 to enrich, or fill manually).
• Don't re-summarize already-summarized papers unless explicitly asked.
• Don't include columns the user didn't ask for. The default 9 columns are a starting point; trim if requested.