Agent SkillsNeverSight/learn-skills.dev › reference-corpus-analyzer

reference-corpus-analyzer

GitHub

将多篇文献聚合为结构化对比矩阵,用于方法差异分析、性能差距评估及趋势识别。适用于撰写相关工作、定位研究空白或选择基线模型,需基于已有源卡片进行分层深度阅读与比较。

data/skills-md/a-green-hand-jack/ml-research-skills/reference-corpus-analyzer/SKILL.md NeverSight/learn-skills.dev

Trigger Scenarios

需要多论文横向对比以撰写相关工作章节 识别最接近的3-5篇论文并理解其差异 在多个源卡片基础上生成比较表格而非单独摘要

Install

npx skills add NeverSight/learn-skills.dev --skill reference-corpus-analyzer -g -y
More Options

Non-standard path

npx skills add https://github.com/NeverSight/learn-skills.dev/tree/main/data/skills-md/a-green-hand-jack/ml-research-skills/reference-corpus-analyzer -g -y

Use without installing

npx skills use NeverSight/learn-skills.dev@reference-corpus-analyzer

指定 Agent (Claude Code)

npx skills add NeverSight/learn-skills.dev --skill reference-corpus-analyzer -a claude-code -g -y

安装 repo 全部 skill

npx skills add NeverSight/learn-skills.dev --all -g -y

预览 repo 内 skill

npx skills add NeverSight/learn-skills.dev --list

SKILL.md

Frontmatter
{
    "name": "reference-corpus-analyzer",
    "description": "Produce a multi-paper comparison matrix across a literature corpus with tiered read depth. Use when multiple papers need to be compared side-by-side for method differences, performance gaps, closest-work ranking, or trend identification — distinct from per-paper source cards (reference-reading-summarizer) and single-paper project linking (reference-project-synthesizer).",
    "allowed-tools": "Read, Write, Edit, Bash, Glob, WebSearch, WebFetch",
    "argument-hint": "[project-dir] [--corpus <path>] [--top-n <N>] [--mode compare|rank|trend|gap]"
}

Reference Corpus Analyzer

Synthesize a literature corpus into a structured comparison matrix. This skill answers: across these N papers, who does what, how do they differ, and where is the open space?

Use this skill when:

  • related-work writing requires a side-by-side method comparison across multiple papers
  • you want to identify the closest 3–5 papers and understand exactly how they differ from each other
  • a literature survey needs trend identification across publication years or venues
  • you want to map open gaps across a set of existing approaches before writing the related-work section
  • you have 5+ source cards and want a comparison table rather than individual summaries

Do not use this skill to create per-paper source cards — use reference-reading-summarizer for that. Do not use this skill to link a paper to your project's claims — use reference-project-synthesizer for that. Use this skill after source cards exist; avoid re-reading raw PDFs unless a card is insufficient.

Pair this skill with:

  • reference-reading-summarizer upstream: produce source cards before running corpus analysis
  • reference-project-synthesizer upstream or downstream: link individual papers to project memory before or after comparison
  • related-work-positioning-writer downstream: use the comparison matrix to write novelty-boundary paragraphs
  • baseline-selection-audit downstream: use the ranking to identify must-have baselines
  • literature-review-sprint when the corpus is not yet assembled and a broader topic survey is needed first

Skill Directory Layout

<installed-skill-dir>/
├── SKILL.md
└── templates/
    └── comparison-matrix.md

Progressive Loading

  • Read reference/cards/ to find available source cards before reading raw sources.
  • Read reference/.agent/source-index.md or reference/.agent/reference-index.md to get the corpus inventory.
  • Read memory/claim-board.md when the comparison should be anchored to specific project claims.
  • Read templates/comparison-matrix.md before writing the output matrix.

Core Principles

  • Tiered depth: read deeply only the top-N closest papers; skim the rest for placement.
  • The comparison matrix is a project artifact, not a free-text essay — it should be queryable.
  • Ranking closest work requires a clear criterion: task overlap, method overlap, or claim overlap.
  • Gaps should be stated specifically: "no paper does X under constraint Y" beats "X is underexplored".
  • Do not invent paper properties not stated in the source card or the paper itself.

Step 1 — Assemble the Corpus

Read reference/.agent/source-index.md (or reference-index.md) to list available sources.

For each source, record:

  • source ID and title
  • card availability: has-card, no-card, partial-card
  • initial relevance estimate: core, related, background, tangential

If cards are missing for sources that appear highly relevant, route to reference-reading-summarizer first.

Step 2 — Select Tiered Read Depth

Assign read depth to each source:

Tier Sources Read depth
Deep Top 3–5 closest by task + method overlap Full source card; re-read raw source if card is insufficient
Standard Next 5–10 related works Source card only
Survey Remaining background papers Title + abstract + card summary

Criterion for "closest": same task, same claim type, overlapping method family, or shared benchmark.

Step 3 — Build the Comparison Matrix

Read templates/comparison-matrix.md.

Dimensions to compare (select those relevant to the project):

  • Task / problem: what problem does the paper address?
  • Method family: what is the core mechanism (attention, diffusion, RL, prompting, etc.)?
  • Key innovation: what is the single thing this paper claims to contribute?
  • Benchmark / dataset: what is it evaluated on?
  • Primary metric: what metric is reported?
  • Best reported result: the headline number (with venue and year for context)
  • Limitations acknowledged: what does the paper say it cannot do?
  • Relationship to our work: closer / complementary / orthogonal / superseded by ours

For each tier-1 (deep) paper, also add:

  • Closest claim to ours: the specific claim that most overlaps with our paper's contribution
  • Key differentiator: in one sentence, how our work differs from this paper

Save to reference/corpus-analysis-<date>.md.

Step 4 — Rank Closest Work

Produce a ranked list of the top-5 closest papers with:

Rank: 1
Paper: [title] ([venue year])
Overlap: task=high / method=medium / claim=high
Closest claim: [their specific claim that overlaps ours]
Differentiator: [one sentence: how we differ]
Novelty risk: high / medium / low
Reviewer action: cite as closest work / cite as baseline / cite as background

A paper with novelty risk: high and method=high overlap is the paper whose related-work paragraph needs the clearest boundary statement.

Step 5 — Identify Gaps and Trends

Gaps: what combinations of (task, method, constraint, benchmark) are not yet addressed by any paper in the corpus?

Gap: no paper addresses [X] under [constraint Y] with [method family Z]
Evidence: papers A, B, C address X but not under Y; papers D, E address Y but not X
Opportunity: our work fills this gap by [brief description]

Trends (optional, for survey mode):

  • Which method families are gaining / losing papers over the last 3 years?
  • Which benchmarks are becoming standard vs. falling out of use?
  • What claims were controversial 2 years ago and are now accepted?

Step 6 — Write Memory Writeback

  • Update reference/.agent/source-index.md with read-depth assignments
  • Update memory/risk-board.md for any high-novelty-risk closest-work findings
  • Update memory/claim-board.md if the comparison changes the novelty framing of a claim
  • The comparison matrix itself is saved in reference/ — do not copy it into memory/

Final Sanity Check

Before finishing:

  • tier-1 papers have been read at full card depth
  • the comparison matrix has consistent dimensions across all papers
  • top-5 closest-work ranking has differentiators in one sentence each
  • gaps are stated specifically (not as vague "future work" language)
  • novelty-risk papers are flagged for related-work-positioning-writer

Version History

  • e0220ca Current 2026-07-05 21:36

Same Skill Collection

data/skills-md/00prabalk00/claude-skills/knowledge-base-gap-finder/SKILL.md
data/skills-md/01000001-01001110/agent-jira-skills/jira-agile/SKILL.md
data/skills-md/01000001-01001110/agent-jira-skills/jira-auth/SKILL.md
data/skills-md/01000001-01001110/agent-jira-skills/jira-issues/SKILL.md
data/skills-md/01000001-01001110/agent-jira-skills/jira-project-management/SKILL.md
data/skills-md/01000001-01001110/agent-jira-skills/jira-projects/SKILL.md
data/skills-md/01000001-01001110/agent-jira-skills/jira-safe/SKILL.md
data/skills-md/01000001-01001110/agent-jira-skills/jira-search/SKILL.md
data/skills-md/01000001-01001110/agent-jira-skills/jira-spaces/SKILL.md
data/skills-md/01000001-01001110/agent-jira-skills/jira-transitions/SKILL.md
data/skills-md/0731coderlee-sudo/wechat-publisher/wechat-publisher/SKILL.md
data/skills-md/0froq/skills/conventionalcommits/SKILL.md
data/skills-md/0froq/skills/nuxt/SKILL.md
data/skills-md/0froq/skills/oq/SKILL.md
data/skills-md/0froq/skills/pinia/SKILL.md
data/skills-md/0froq/skills/pnpm/SKILL.md
data/skills-md/0froq/skills/slidev/SKILL.md
data/skills-md/0froq/skills/tsdown/SKILL.md
data/skills-md/0froq/skills/turborepo/SKILL.md
data/skills-md/0froq/skills/unocss/SKILL.md
data/skills-md/0froq/skills/vitepress/SKILL.md
data/skills-md/0froq/skills/vitest/SKILL.md
data/skills-md/0froq/skills/vue-best-practices/SKILL.md
data/skills-md/0froq/skills/vue-router-best-practices/SKILL.md
data/skills-md/0froq/skills/vue-testing-best-practices/SKILL.md
data/skills-md/0froq/skills/vue/SKILL.md
data/skills-md/0froq/skills/vueuse-functions/SKILL.md
data/skills-md/0froq/skills/web-design-guidelines/SKILL.md
data/skills-md/0juano/agent-skills/bondterminal-x402/SKILL.md
data/skills-md/0juano/agent-skills/edgeone-pages-deploy/SKILL.md
data/skills-md/0juano/agent-skills/ley-ar/SKILL.md
data/skills-md/0juano/agent-skills/ticktick/SKILL.md
data/skills-md/0juano/agent-skills/x-image-cards/SKILL.md
data/skills-md/0juano/x-image-cards/x-image-cards/SKILL.md
data/skills-md/0x0funky/agent-sprite-forge/generate2dsprite/SKILL.md
data/skills-md/0x0funky/agent-sprite-forge/video2dsprite/SKILL.md
data/skills-md/0x2e/superpowers/brainstorming/SKILL.md
data/skills-md/0x2e/superpowers/dispatching-parallel-agents/SKILL.md
data/skills-md/0x2e/superpowers/executing-plans/SKILL.md
data/skills-md/0x2e/superpowers/finishing-a-development-branch/SKILL.md
data/skills-md/0x2e/superpowers/receiving-code-review/SKILL.md
data/skills-md/0x2e/superpowers/requesting-code-review/SKILL.md
data/skills-md/0x2e/superpowers/subagent-driven-development/SKILL.md
data/skills-md/0x2e/superpowers/systematic-debugging/SKILL.md
data/skills-md/0x2e/superpowers/test-driven-development/SKILL.md
data/skills-md/0x2e/superpowers/using-git-worktrees/SKILL.md
data/skills-md/0x2e/superpowers/using-superpowers/SKILL.md
data/skills-md/0x2e/superpowers/verification-before-completion/SKILL.md
data/skills-md/0x2e/superpowers/writing-plans/SKILL.md
data/skills-md/0x2e/superpowers/writing-skills/SKILL.md

Metadata

Files
0
Version
f4b4cf1
Hash
8a71f40e
Indexed
2026-07-05 21:36

Accueil - Wiki
Copyright © 2011-2026 iteam. Current version is 2.155.2. UTC+08:00, 2026-08-08 06:35
浙ICP备14020137号-1 $Carte des visiteurs$