From research-workspace
Converts a paper draft manuscript plus figures and Zotero metadata into structured .paper/claims.yml and .paper/figures.yml files for use by academic-writing-skills. For the user's own manuscript only, not for summarizing cited papers.
How this skill is triggered — by the user, by Claude, or both
Slash command
/research-workspace:paper-memory-builderThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Bridge between research-hub and `academic-writing-skills`. Reads a
Bridge between research-hub and academic-writing-skills. Reads a
manuscript draft (Word, LaTeX, Markdown, Obsidian paper folder) plus the
figures it references, and writes structured .paper/claims.yml +
.paper/figures.yml so the writing skill can reason about the paper
without parsing the manuscript on every call.
This skill does not edit the manuscript itself. That's the writing skill's job. We just produce the memory layer it consumes.
Trigger phrases:
Not for:
academic-writing-skills.research-project-orienter.literature-triage-matrix.notebooklm-brief-verifier.In priority order:
.paper/ existing files — if claims.yml already exists, parse
it; this run should refresh, not replace human edits.paper/manuscript.docx / manuscript.tex / manuscript.mdraw/<paper-cluster>/manuscript.mdfigures/ or outputs/figures/ — read
filenames + captions; don't OCR images..research/project_manifest.yml — for project context (research
question, datasets, current_stage). Use to anchor claims in the
project's overall question..research/literature_matrix.md — for citation key lookup if the
manuscript references "Smith 2024" but you need to disambiguate.Beyond the manuscript + figures, real research repos typically contain
sibling artifacts that populate claims[].evidence_artifacts for
non-figure evidence. When scanning, look for:
*.csv, *.parquet, *.npz,
*.h5, *.json log files under outputs/, results/, or
repo-root sibling dirs. Common patterns: <experiment_id>_log.csv,
run_log.csv, adaptation_log.csv. These back claims like
"χ²=891, p<0.0001 across 50 runs" (the chi-squared output came from
the per-agent decision log, not from a figure).*.py, *.R, *.jl, *.ipynb files that
produce evidence numbers. The script itself is rarely the evidence
artifact, but its OUTPUT path is. Trace the script → output pairing
if the manuscript cites a number computed by a script.*.drawio, *.svg sources for
conceptual figures (e.g. framework architecture, decision-flow
diagrams). These often back claim text like "Figure 1 shows the
module architecture" even when the embedded image in the .docx is
rendered from the drawio source.Review Response-YYYYMMDD.docx
files (operator-side response prep). Not consumed by
paper-memory-builder directly but listed here as a typical sibling
artifact; academic-writing-skills reviewer-response workflow uses
these.Populate claims[].evidence_artifacts with the artifact PATH (relative
to the paper repo root), not the artifact contents. Example:
- id: C8
text: "Chi-squared test on coping-appraisal keywords yields χ²=891, p<0.0001..."
evidence_artifacts:
- "outputs/llm-abm_decision_log.csv" # raw per-agent text used in chi-sq
- "scripts/analyze_appraisal_keywords.py" # script that ran the test
- "Result section §4.2 (chi-squared paragraph)" # manuscript anchor
figure_or_table: ["TabS5"]
status: draft
This makes the audit trail traceable end-to-end: claim → manuscript
sentence → figure/table reference → underlying data file → analysis
code. Cross-plugin consumers like academic-writing-skills (claim-
evidence audit) can then verify the chain is intact before accepting
the claim as supported.
Write to <project-root>/.paper/:
claims.yml — every paper-level claim, with evidence pointers + status. Schema: references/yaml-schemas.md.figures.yml — every figure inventory, with key numbers + supported claims. Schema: references/yaml-schemas.md.revision_history.yml — append-only log of revision rounds. Schema + append-vs-overwrite rules: references/revision_history_schema.md.Do not touch journal_format.md, reviewer_comments.md, or style_overrides.md — those belong to academic-writing-skills.
.paper/*.yml in subsequent writing turns.academic-writing-skills next for any
writing/revision/audit pass."[paper-memory-builder]
Read manuscript: paper/manuscript.docx (8 pages, 12 figures)
Wrote: .paper/claims.yml (9 claims; 2 marked at risk)
Wrote: .paper/figures.yml (12 figures; 8 mapped to claims, 4 are context)
Suggested next: load academic-writing-skills for revision/audit passes.
status: draft — see the
anti-leakage rule below..paper/journal_format.md, reviewer_comments.md, or
style_overrides.md. Those are owned by academic-writing-skills..research/ — that's the workspace layer, not the
paper layer.A claim with empty or absent
evidence_artifactsMUST havestatus: gapplus a one-linegap_reason. Never emit such a claim asstatus: draftorstatus: supported.
This is the contract that prevents an unsupported claim from leaking into the downstream writing/audit pipeline as if it were evidenced.
How it manifests in normal use:
status: gap with a gap_reason like
"intro claim with no matching E-run output". The writing skill then
surfaces it as [MATERIAL GAP] rather than treating it as evidenced
prose.status: draft; document the doubt in risk: rather
than emptying evidence_artifacts.status: rejected; do not delete (audit trail).The contract is enforced by scripts/check_claims_schema.py against
the JSON Schema at references/claims.schema.json. Run the validator
manually after editing .paper/claims.yml by hand:
python scripts/check_claims_schema.py <path-to-claims.yml>
references/yaml-schemas.md — .paper/claims.yml and .paper/figures.yml schemasreferences/revision_history_schema.md — .paper/revision_history.yml schema + append-only audit-trail rulesnpx claudepluginhub wenyuchiou/ai-research-skills --plugin research-workspaceAudits academic or technical manuscripts with a section-level refactoring report covering argument architecture, narrative flow, citation hygiene, and submission-readiness.
Orchestrates multi-agent writing of academic papers or proposals from research artifacts, with evidence-grounded prose, MAGI cross-review, and quality validation.
Extracts propositional claims from research paper notes.md into claims.yaml using propstore schema. Supports parameter and equation claims; uses concepts.yaml for canonical concept names.