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Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step…
Use when the user wants to find related work, survey a research area, identify literature gaps, or discover open-source implementations. Triggers on phrases like "find papers on", "related work", "literature review", "what papers exist", "open source implementation", or "papers
$ npx -y skills add fcakyon/phd-skills --skill literature-research --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/literature-researchContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when the user wants to find related work, survey a research area, identify literature gaps, or discover open-source implementations. Triggers on phrases like "find papers on", "related work", "literature review", "what papers exist", "open source implementation", or "papers
name: literature-research description: > Use when the user wants to find related work, survey a research area, identify literature gaps, or discover open-source implementations. Triggers on phrases like "find papers on", "related work", "literature review", "what papers exist", "open source implementation", or "papers with code".
You are helping a researcher conduct systematic literature research. Follow this methodology to ensure thorough, accurate coverage.
Before searching:
Use multiple search strategies in order:
Use [Paper Search CLI](https://github.com/fcakyon/paper-search-cli); follow [setup](../../commands/setup.md#paper-search-when-needed) if it is missing.
paper-search search 'object detection' -s fast -n 5 paper-search search 'object detection' -s arxiv,crossref -n 5
Inspect the JSON `errors` field and retain each paper's source ID and URL. Report unavailable providers rather than treating errors as an empty literature search. Vary search terms to catch different framings of the same concept. Use web search for proceedings and sources the CLI cannot access.
From seed papers or initial results:
Organize found papers into a structured taxonomy:
| Paper | Year | Venue | Approach | Key Result | Code? | Relevance | |-------|------|-------|----------|-----------|-------|-----------|
Group by methodology or approach type, not chronologically.
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:
For each identified gap, verify it's real:
Rate confidence: HIGH (extensively searched, clearly missing), MEDIUM (searched but might have missed niche work), LOW (limited search, gap may exist elsewhere).
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)
Every paper mentioned must have verified metadata:
Catch AI mistakes before they cost weeks of compute. Reproduce papers from arxiv. Debug runs evidence-first. Compare experiments at the right epoch. Launch with discipline.
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