Agent skill

Evidence-Based Research Lane

by Yeachan-Heo in Yeachan-Heo/oh-my-claudecode

Answers an open question with sourced findings, searching the repo and official docs first, separating verified facts from inference and not implementing anything.

MITAuto-check passedAgent Workflows

Install Evidence-Based Research Lane

skills CLI
$ npx skills add Yeachan-Heo/oh-my-claudecode --skill research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Yeachan-Heo/oh-my-claudecode research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Yeachan-Heo/oh-my-claudecode.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research .claude/skills/research && rm -rf skills-src

Use ~/.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/

Facts

Skill name
research
GitHub stars
40k
Token cost
~350 tokens
SKILL.md length
187 words
Files
1
Skills in repo
47
Repo updated
First seen
Licence
MIT

At a glance

Answers an open question with sourced findings, searching the repo and official docs first, separating verified facts from inference and not implementing anything.

  • Works in 5 steps: State the question precisely enough to… → Search the repo and its docs first —… → For external SDKs, frameworks, or APIs,… → …
  • Finding out how an unfamiliar part of a codebase actually behaves before changing it
  • SKILL.md covers Goal, Workflow, Scale and Rules, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

This skill is for moments when the next step depends on something not yet known. It answers questions and does not implement. The agent states the question precisely enough to know when it is answered, searches the repository and its docs first because local evidence beats recollection, consults official documentation for external SDKs, frameworks and APIs, and sweeps by file, symbol, caller and history when an answer could hide.

Effort scales with the question: a narrow lookup is answered directly, independent questions are investigated in parallel, and open-ended discovery continues until further passes find nothing new. Findings must cite a file and line or the document consulted, mark what was verified versus inferred, and report contradicting evidence instead of picking the tidier story. The output lists the question, sourced findings, what remains unknown and a next step if one follows. Other research-style skills in the same collection route here.

When your agent uses it

  • Finding out how an unfamiliar part of a codebase actually behaves before changing it
  • Checking an SDK or API's documented behavior before relying on it
  • Getting a sourced answer that lists what is still unknown

Example prompts

  • “Research how the retry logic in the upload service works and cite the files.”
  • “Find out from the official docs whether this framework supports streaming responses.”
  • “Investigate why the cache invalidation looks inconsistent, but do not change any code yet.”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. State the question precisely enough to know when it is answered.
  2. Search the repo and its docs first — local evidence outranks recollection.
  3. For external SDKs, frameworks, or APIs, consult official documentation.
  4. Sweep more than one way when the answer could hide: by file, by symbol, by caller, by history.
  5. Synthesize into findings, each tied to where it came from.

What it can do on your machine

Read from SKILL.md and the folder at commit 454bae0. 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

Evidence-Based Research Lane loads about 350 tokens when it runs. Until then it costs about 19 tokens; SKILL.md has 187 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~19
When it runs · the whole SKILL.md, loaded when a task matches
~350

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 Yeachan-Heo/oh-my-claudecode at commit 454bae0, republished under its MIT licence (© Yeachan-Heo). 187 words, ~350 tokens.

Download SKILL.mdSave it as .claude/skills/research/SKILL.md (or your agent's skills folder).
name
research
description
Investigate an open question and return grounded, sourced findings

Research

Use this skill when the next step depends on something not yet known. Research answers questions; it does not implement.

This is the canonical research lane. deep-dive, sciomc, and autoresearch route here.

Goal

Replace assumptions with evidence, and say plainly what remains uncertain.

Workflow

  1. State the question precisely enough to know when it is answered.
  2. Search the repo and its docs first — local evidence outranks recollection.
  3. For external SDKs, frameworks, or APIs, consult official documentation.
  4. Sweep more than one way when the answer could hide: by file, by symbol, by caller, by history.
  5. Synthesize into findings, each tied to where it came from.

Scale

  • Narrow lookup — answer it directly.
  • Multiple independent questions — investigate in parallel.
  • Unknown-size discovery — keep going until additional passes surface nothing new.

Rules

  • Cite the source: file and line, or the document consulted.
  • Distinguish what was verified from what was inferred.
  • Report contradicting evidence rather than picking the tidier story.
  • Do not implement as a side effect of researching.

Output

  • The question
  • Findings, each with its source
  • What remains unknown or unverifiable
  • Recommended next step, if one follows

© Yeachan-Heo, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/research of Yeachan-Heo/oh-my-claudecode.

Open the folder on GitHubat commit 454bae0

Compare with similar skills

Evidence-Based Research Lane 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.

Evidence-Based Research Lane compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Evidence-Based Research Lane this skillYeachan-Heo/oh-my-claudecode40k—~350Automated safety check: PassMIT
AI RAG PipelineNeverSight/learn-skills.dev2171 repos~2kAutomated safety check: PassNone
Tavily Web Searchallenpeng0705/EnvoyMesh3.1k3 repos~2.5kAutomated safety check: NotesNone
Deep Research Agent TeamImbad0202/academic-research-skills51k—~13kAutomated safety check: PassCustom licence
AnythingAtlasLiuziyu77/AnythingAtlas195—~3.6kAutomated safety check: PassApache-2.0
Deep Research PlanMagicCube/helixent680—~2.1kAutomated safety check: PassNone

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Questions about Evidence-Based Research Lane

What does Evidence-Based Research Lane do?

Answers an open question with sourced findings, searching the repo and official docs first, separating verified facts from inference and not implementing anything. This skill is for moments when the next step depends on something not yet known. It answers questions and does not implement.

When should I use Evidence-Based Research Lane?

Evidence-Based Research Lane fits situations like: finding out how an unfamiliar part of a codebase actually behaves before changing it; checking an SDK or API's documented behavior before relying on it; getting a sourced answer that lists what is still unknown.

How do I install Evidence-Based Research Lane in Claude Code?

Run `npx skills add Yeachan-Heo/oh-my-claudecode --skill research -a claude-code`. Or copy the skill folder (skills/research in Yeachan-Heo/oh-my-claudecode) into .claude/skills/research in your project. Claude Code loads it when a task matches its description.

How do I install Evidence-Based Research Lane in Codex?

Run `npx skills add Yeachan-Heo/oh-my-claudecode --skill research -a codex`. Or copy the skill folder (skills/research in Yeachan-Heo/oh-my-claudecode) into .agents/skills/research in your project. Codex loads it when a task matches its description.

Can I use Evidence-Based Research Lane 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 Yeachan-Heo/oh-my-claudecode --skill research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/research, .gemini/skills/research, .github/skills/research and .opencode/skills/research in your project.

What does Evidence-Based Research Lane need to run?

SKILL.md names no scripts, command-line tools or credentials: Evidence-Based Research Lane is instructions for the agent only.

Does Evidence-Based Research Lane 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 Evidence-Based Research Lane 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 Evidence-Based Research Lane use?

Evidence-Based Research Lane 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 Evidence-Based Research Lane use?

About 350 tokens (SKILL.md is roughly 1.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Evidence-Based Research Lane?

Skills that share tags, products or a category with Evidence-Based Research Lane: AI RAG Pipeline (NeverSight/learn-skills.dev, 217 stars), Tavily Web Search (allenpeng0705/EnvoyMesh, 3.1k stars), Deep Research Agent Team (Imbad0202/academic-research-skills, 51k stars) and AnythingAtlas (Liuziyu77/AnythingAtlas, 195 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Evidence-Based Research Lane?

Yeachan-Heo (a GitHub user) maintains it in Yeachan-Heo/oh-my-claudecode, which has 39,751 GitHub stars. The repository holds 47 skills in this directory. The repository was last updated on October 8, 2026.

Source: Yeachan-Heo/oh-my-claudecode on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.