| 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):
-
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
-
.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.
-
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.
-
Zotero collection metadata via local API (fast) — add child note
contents only if Obsidian doesn't have the paper.
-
NotebookLM downloaded briefs under .research_hub/artifacts/ —
if the user has already generated a brief on the cluster, mine it
for cross-paper comparisons.
-
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.
