Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
Systematically discover and shortlist academic literature for a research project.
Install Literature Discovery
$ npx skills add scholay/skills --skill literature-discovery -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install scholay/skills literature-discovery --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/scholay/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/literature-discovery .claude/skills/literature-discovery && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "literature-discovery" agent skill from https://github.com/scholay/skills/tree/main/literature-discovery into .claude/skills/literature-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "literature-discovery", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/scholay/skills/tree/main/literature-discoveryType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add scholay/skills --skill literature-discovery -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install scholay/skills literature-discovery --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/scholay/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/literature-discovery .agents/skills/literature-discovery && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "literature-discovery" agent skill from https://github.com/scholay/skills/tree/main/literature-discovery into .agents/skills/literature-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "literature-discovery", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add scholay/skills --skill literature-discovery -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install scholay/skills literature-discovery --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/scholay/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/literature-discovery .cursor/skills/literature-discovery && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "literature-discovery" agent skill from https://github.com/scholay/skills/tree/main/literature-discovery into .cursor/skills/literature-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "literature-discovery", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/scholay/skills.git --path literature-discovery--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add scholay/skills --skill literature-discovery -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install scholay/skills literature-discovery --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/scholay/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/literature-discovery .gemini/skills/literature-discovery && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "literature-discovery" agent skill from https://github.com/scholay/skills/tree/main/literature-discovery into .gemini/skills/literature-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "literature-discovery", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install scholay/skills literature-discoveryInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add scholay/skills --skill literature-discovery -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/scholay/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/literature-discovery .github/skills/literature-discovery && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "literature-discovery" agent skill from https://github.com/scholay/skills/tree/main/literature-discovery into .github/skills/literature-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "literature-discovery", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add scholay/skills --skill literature-discovery -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install scholay/skills literature-discovery --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/scholay/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/literature-discovery .opencode/skills/literature-discovery && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "literature-discovery" agent skill from https://github.com/scholay/skills/tree/main/literature-discovery into .opencode/skills/literature-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "literature-discovery", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
literature-discovery- GitHub stars
- 141
- Token cost
- ~864 tokens
- SKILL.md length
- 462 words
- Files
- 2 (incl. references)
- Skills in repo
- 5
- Repo updated
- First seen
- Licence
- MIT
At a glance
Systematically discover and shortlist academic literature for a research project.
- Works in 6 steps: Define scope before searching → Start from a seed set, not a broad search → Expand in three directions from each seed → …
- Building a reading corpus from scratch
- SKILL.md covers The core problem, Workflow, Output expectations and Rules, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Literature Discovery is an agent skill from scholay/skills. Systematically discover and shortlist academic literature for a research project. Use when building a reading corpus from scratch, expanding an existing one, filling topic or language gaps, or turning search results into a prioritized intake queue.
Its SKILL.md is about 860 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/search-matrix.md`).
It sits in Research & Science. The repository describes itself as: Open academic AI skills maintained by Scholay. The licence is MIT.
When your agent uses it
- Building a reading corpus from scratch
- Expanding an existing one
- Turning search results into a prioritized intake queue
Example prompts
- “/literature-discovery”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit cd61bd9. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Literature Discovery loads about 864 tokens when it runs, and up to ~1.4k if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 462 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from scholay/skills at commit cd61bd9, republished under its MIT licence (© scholay). 462 words, ~864 tokens.
Download SKILL.mdSave it as
.claude/skills/literature-discovery/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- literature-discovery
- description
- Systematically discover and shortlist academic literature for a research project. Use when building a reading corpus from scratch, expanding an existing one, filling topic or language gaps, or turning search results into a prioritized intake queue.
Literature Discovery
The core problem
Most researchers start by downloading everything that looks relevant. This creates an unmanageable pile with no structure and no sense of what's missing. The alternative: build the corpus in deliberate waves, starting from a small seed set and expanding outward in three directions.
Workflow
1. Define scope before searching
Write one sentence: what is this research about, and what is it not about? This sentence is your filter for every candidate you encounter.
Then build a search matrix:
| Axis | Examples |
|---|---|
| Topic terms | main concept + synonyms + older names |
| Region/context | geographic or institutional scope |
| Object type | what specific thing you're studying |
| Time period | historical range |
| Language | all relevant languages, not just English |
| Method | field methods you're comparing against |
2. Start from a seed set, not a broad search
Pick 3–5 items you already know are strong:
- One overview or survey paper
- One foundational monograph or book
- One case-study cluster
- One methods paper
Do not start by downloading 200 results from a keyword search.
3. Expand in three directions from each seed
Backward — who does this paper cite? Follow references to earlier foundational work.
Forward — who cites this paper? Use Google Scholar, Semantic Scholar, or OpenAlex to find later work building on it.
Lateral — what related terms or adjacent topics does this paper not cover? Search those gaps explicitly.
4. Record every candidate before annotating
Keep a running candidate list. Add a row for every item you encounter, even if you don't have the PDF yet. Minimum fields per row:
- title, authors, year
- source (where you found it)
- language
- status:
candidate / keep / exclude / needs_pdf / needs_translation - reason (one line)
Do not annotate or read deeply at this stage.
Show full SKILL.md (183 more words)Show less
5. Check for coverage bias
Before promoting items to intake, ask:
- Am I over-representing one language, one region, one time period?
- Are there practitioner sources, grey literature, or non-English materials I haven't touched?
- What would a critic say is missing from this list?
6. Promote only strong items
Only keep-status items move to the next stage (source intake). Everything else stays in the candidate list with a reason recorded.
Output expectations
- A reusable search matrix
- A candidate list with statuses and one-line reasons
- A gap list: what's under-covered and why
- A
keepqueue ready for source intake
Rules
- Search in waves. One wave = one seed's full expansion before starting the next seed.
- Never download everything from a keyword search in one pass.
- De-duplicate before promoting: DOI first, then title + year, then manual check.
- Keep translated titles and original titles together.
- Record the search URL and date for every batch — you will need to verify later.
- A candidate list with 200 items and no statuses is not useful. Triage as you go.
References
- Read
references/search-matrix.mdfor a reusable matrix template and project-specific axis examples.
© scholay, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file (references) in literature-discovery of scholay/skills.
- SKILL.md
- references/search-matrix.md
Open the folder on GitHubat commit cd61bd9
Compare with similar skills
Literature Discovery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Literature Discovery this skillscholay/skills | 141 | — | ~864 | Automated safety check: Pass | MIT | |
| Hypothesis Generationspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Last30daysmvanhorn/last30days-skill | 64k | — | ~7.9k | Automated safety check: Notes | MIT |
Similar skills
GitHub Deep Research
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
Nature Paper Card
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
Content Research Writer
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
Last30days
mvanhorn/last30days-skill
Research what people actually say about any topic in the last 30 days.
Peer Review
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
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Related
Categories
Questions about Literature Discovery
What does Literature Discovery do?
Systematically discover and shortlist academic literature for a research project. Literature Discovery is an agent skill from scholay/skills. Systematically discover and shortlist academic literature for a research project.
When should I use Literature Discovery?
Literature Discovery fits situations like: building a reading corpus from scratch; expanding an existing one; turning search results into a prioritized intake queue.
How do I install Literature Discovery in Claude Code?
Run `npx skills add scholay/skills --skill literature-discovery -a claude-code`. Or copy the skill folder (literature-discovery in scholay/skills) into .claude/skills/literature-discovery in your project. Claude Code loads it when a task matches its description.
How do I install Literature Discovery in Codex?
Run `npx skills add scholay/skills --skill literature-discovery -a codex`. Or copy the skill folder (literature-discovery in scholay/skills) into .agents/skills/literature-discovery in your project. Codex loads it when a task matches its description.
Can I use Literature Discovery in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add scholay/skills --skill literature-discovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/literature-discovery, .gemini/skills/literature-discovery, .github/skills/literature-discovery and .opencode/skills/literature-discovery in your project.
What does Literature Discovery need to run?
SKILL.md names no scripts, command-line tools or credentials: Literature Discovery is instructions for the agent only.
Does Literature Discovery access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Literature Discovery safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Literature Discovery use?
Literature Discovery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Literature Discovery use?
About 864 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 577 tokens, read only when the agent opens those files.
What are the alternatives to Literature Discovery?
Skills that share tags, products or a category with Literature Discovery: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Literature Discovery?
scholay (a GitHub user) maintains it in scholay/skills, which has 141 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on June 17, 2026.
Source: scholay/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.