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Literature Research

  • 56 installs
  • 354 repo stars
  • Updated July 3, 2026
  • fcakyon/phd-skills

Helps with ai & agent building tasks.

About

literature-research is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • literature-research
  • AI & Agent Building
  • AI-coding skill

Literature Research by the numbers

  • 56 all-time installs (skills.sh)
  • Ranked #6,604 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
npx skills add https://github.com/fcakyon/phd-skills --skill literature-research

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Listed on Skillselion
Installs56
repo stars354
Last updatedJuly 3, 2026
Repositoryfcakyon/phd-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Literature Research Methodology

You are helping a researcher conduct systematic literature research. Follow this methodology to ensure thorough, accurate coverage.

Step 1: Scope Definition

Before searching:

  • Clarify the exact research question or topic boundary
  • Identify key terms and their synonyms (e.g., "content moderation" = "safety filtering" = "NSFW detection")
  • Define inclusion/exclusion criteria (year range, venue type, methodology type)
  • Ask if the user has seed papers to start from

Step 2: Systematic Search

Use multiple search strategies in order:

2a. Direct Search

  • Search for the topic using key terms via web search
  • Target: Google Scholar, Semantic Scholar, arXiv, DBLP
  • Vary search terms to catch different framings of the same concept

2b. Citation Chaining

From seed papers or initial results:

  • Forward chaining: who cited this paper? (find via Semantic Scholar or Google Scholar)
  • Backward chaining: what does this paper cite? (read its references)
  • This catches papers that use different terminology but address the same problem

2c. Venue Mining

  • Identify top venues for the topic (conferences, journals, workshops)
  • Check recent proceedings of these venues for relevant papers
  • Workshop papers often contain early-stage work not yet in main conferences

2d. Open Source Discovery

  • Search GitHub for implementations related to the topic
  • Check Papers With Code for the specific task/dataset
  • Look for "awesome-X" lists curated by the community

Step 3: Categorization

Organize found papers into a structured taxonomy:

| Paper | Year | Venue | Approach | Key Result | Code? | Relevance |
|-------|------|-------|----------|-----------|-------|-----------|

Group by methodology or approach type, not chronologically.

Step 4: Gap Identification

Map what exists vs. what's missing:

1. Coverage matrix: rows = problem aspects, columns = existing approaches 2. Empty cells = potential gaps 3. For each candidate gap:

  • Search specifically for work filling this gap (it may exist under different terms)
  • Check very recent papers (last 6 months) that might have addressed it
  • Assess whether the gap is meaningful (would filling it advance the field?)

Step 5: Gap Validation

For each identified gap, verify it's real:

  • [ ] Searched with at least 3 different phrasings
  • [ ] Checked proceedings of top-3 venues from last 2 years
  • [ ] No preprint on arXiv addressing this gap
  • [ ] The gap is technically feasible to address
  • [ ] Filling the gap would be a meaningful contribution

Rate confidence: HIGH (extensively searched, clearly missing), MEDIUM (searched but might have missed niche work), LOW (limited search, gap may exist elsewhere).

Step 6: Output Format

Produce a structured research landscape:

1. Topic summary — 2-3 sentence overview of the research area 2. Paper table — categorized list with year, venue, approach, results, code availability 3. Gap table — identified gaps with confidence levels and evidence 4. Recommended readings — top 5-10 most relevant papers for the user's specific work 5. Candidate citations — BibTeX entries for papers worth citing (MUST verify against DBLP before presenting)

Citation Integrity

Every paper mentioned must have verified metadata:

  • Author names: cross-check with DBLP or the paper's official page
  • Venue and year: confirm against the published version (not preprint)
  • Claims about results: only include numbers you can trace to a specific table/figure in the paper
  • If uncertain about any detail, flag it explicitly rather than guessing

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