
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)
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| Installs | 56 |
|---|---|
| repo stars | ★ 354 |
| Last updated | July 3, 2026 |
| Repository | fcakyon/phd-skills ↗ |
What it does
Helps with ai & agent building tasks.
Files
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