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novelty-matrix

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Mis à jour14 avril 2026 à 11:50

Use when `paper-review` needs overlap/delta positioning against provided related work. **Trigger**: novelty matrix, prior-work matrix, overlap/delta, 相关工作对比, 新颖性矩阵. **Use when**: `paper-review` 中评估 novelty/positioning，需要把贡献与相关工作逐项对齐并写出差异点证据。 **Skip if**: 缺少 claims（先跑 `claims-extractor`）或你不打算做新颖性定位分析。 **Network**: none (retrieval of additional related work is out-of-scope unless provided). **Guardrail**: 明确 overlap 与 delta；尽量给出可追溯证据来源（来自稿件/引用/作者陈述）。

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

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Source

WILLOSCAR

WILLOSCAR/research-units-pipeline-skills

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Analystes des systèmes informatiquesProfessions informatiques et mathématiques·SOC 15-1211

Développeurs de logicielsSOC 15-1252

Explorateur de fichiers

2 fichiers

SKILL.md

readonly

name

novelty-matrix

description

Novelty Matrix

Transforms a claim ledger plus related-work surface into a novelty positioning table for paper-review.

Input

Required:

output/CLAIMS.md

Optional:

manuscript reference list
user-provided related work list

Output

output/NOVELTY_MATRIX.md

Contract

Each matrix row must expose:

claim
closest related work
overlap
delta
evidence note

Script boundary

scripts/run.py should:

extract candidate related works from available sources
generate stable overlap/delta rows
write a table-shaped artifact

Keep matching heuristics and markdown rendering in shared tooling.

Acceptance

output exists
includes at least one row per claim
if related works are unavailable, the artifact says so explicitly

Non-goals

final novelty judgment prose
retrieval of a new literature corpus

Plus depuis ce dépôt

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agent-survey-corpus

WILLOSCAR/research-units-pipeline-skills

Download a small corpus of open-access arXiv survey/review PDFs about agentic systems and extract text for style learning. **Trigger**: agent survey corpus, ref corpus, download surveys, 学习综述写法, 下载 survey. **Use when**: you want to study how real agent surveys structure sections (6–8 H2), size subsections, and write evidence-backed comparisons. **Skip if**: you cannot download PDFs (no network) or you don't want local PDF files. **Network**: required. **Guardrail**: only download arXiv PDFs; store under `ref/` and keep large files out of git.

2026-05-30472

global-reviewer

WILLOSCAR/research-units-pipeline-skills

Global consistency review for survey drafts: terminology, cross-section coherence, and scope/citation hygiene. Writes `output/GLOBAL_REVIEW.md` and (optionally) applies safe edits to `output/DRAFT.md`. **Trigger**: global review, consistency check, coherence audit, 术语一致性, 全局回看, 章节呼应, 拷打 writer. **Use when**: Draft exists and you want a final evidence-first coherence pass before LaTeX/PDF. **Skip if**: You are still changing the outline/mapping/notes (do those first), or prose writing is not approved. **Network**: none. **Guardrail**: Do not invent facts or citations; do not add new citation keys; treat missing evidence as a failure signal.

2026-05-30472

literature-engineer

WILLOSCAR/research-units-pipeline-skills

Multi-route literature expansion + metadata normalization for evidence-first surveys. Produces a large candidate pool (`papers/papers_raw.jsonl`, target ≥1200) with stable IDs and provenance, ready for dedupe/rank + citation generation. **Trigger**: evidence collector, literature engineer, 文献扩充, 多路召回, snowballing, cited by, references, 元信息增强, provenance. **Use when**: 需要把候选文献扩充到 ≥1200 篇并补齐可追溯 meta（survey pipeline 的 Stage C1，写作前置 evidence）。 **Skip if**: 已经有高质量 `papers/papers_raw.jsonl`（≥1200 且每条都有稳定标识+来源记录）。 **Network**: 可离线（靠 imports）；雪崩/在线检索需要网络。 **Guardrail**: 不允许编造论文；每条记录必须带稳定标识（arXiv id / DOI / 可信 URL）和 provenance；不写 output/ prose。

2026-05-30472

pdf-text-extractor

WILLOSCAR/research-units-pipeline-skills

Download PDFs (when available) and extract plain text to support full-text evidence, writing `papers/fulltext_index.jsonl` and `papers/fulltext/*.txt`. **Trigger**: PDF download, fulltext, extract text, papers/pdfs, 全文抽取, 下载PDF. **Use when**: `queries.md` 设置 `evidence_mode: fulltext`（或你明确需要全文证据）并希望为 paper notes/claims 提供更强 evidence。 **Skip if**: `evidence_mode: abstract`（默认）；或你不希望进行下载/抽取（成本/权限/时间）。 **Network**: fulltext 下载通常需要网络（除非你手工提供 PDF 缓存在 `papers/pdfs/`）。 **Guardrail**: 缓存下载到 `papers/pdfs/`；默认不覆盖已有抽取文本（除非显式要求重抽）。

2026-05-30472

prose-writer

WILLOSCAR/research-units-pipeline-skills

Write `output/DRAFT.md` (or `output/SNAPSHOT.md`) from an approved outline and evidence packs, using only verified citation keys from `citations/ref.bib`. **Trigger**: write draft, prose writer, snapshot, survey writing, 写综述, 生成草稿, section-by-section drafting. **Use when**: structure is approved (`DECISIONS.md` has `Approve C2`) and evidence packs exist (`outline/subsection_briefs.jsonl`, `outline/evidence_drafts.jsonl`). **Skip if**: approvals are missing, or evidence packs are incomplete / scaffolded (missing-fields, TODO markers). **Network**: none. **Guardrail**: do not invent facts or citations; only cite keys present in `citations/ref.bib`; avoid pipeline-jargon leakage in final prose.

2026-05-30472

schema-normalizer

WILLOSCAR/research-units-pipeline-skills

Normalize cross-skill JSONL interfaces (ids + titles + citation key formats) so downstream skills do not rely on best-effort joins. **Trigger**: schema normalize, jsonl contract, interface drift, join drift, 字段不一致, schema 规范化. **Use when**: you have generated C2-C4 JSONL artifacts (outline/briefs/bindings/packs/anchors) and want deterministic, stable fields before self-loops/writing. **Skip if**: you are not using the survey pipelines, or the workspace already has a fresh PASS `output/SCHEMA_NORMALIZATION_REPORT.md` for the current artifacts. **Network**: none. **Guardrail**: NO PROSE; deterministic transforms only; do not invent evidence/claims; only fill missing ids/titles from `outline/outline.yml`.

2026-05-30472

name

novelty-matrix

description

Novelty Matrix

Transforms a claim ledger plus related-work surface into a novelty positioning table for paper-review.

Input

Required:

output/CLAIMS.md

Optional:

manuscript reference list
user-provided related work list

Output

output/NOVELTY_MATRIX.md

Contract

Each matrix row must expose:

claim
closest related work
overlap
delta
evidence note

Script boundary

scripts/run.py should:

extract candidate related works from available sources
generate stable overlap/delta rows
write a table-shaped artifact

Keep matching heuristics and markdown rendering in shared tooling.

Acceptance

output exists
includes at least one row per claim
if related works are unavailable, the artifact says so explicitly

Non-goals

final novelty judgment prose
retrieval of a new literature corpus