Agent skill

Research

by notque in notque/vexjoy-agent

Research: structured investigation, fact-checking, explanation traces.

MITAuto-check: notesResearch & Science

Install Research

skills CLI
$ npx skills add notque/vexjoy-agent --skill research -a claude-code

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

GitHub CLI
$ gh skill install notque/vexjoy-agent 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/notque/vexjoy-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/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
435
Token cost
~1.7k tokens
SKILL.md length
645 words
Files
4 (incl. references)
Skills in repo
61
Repo updated
First seen
Licence
MIT

At a glance

Research: structured investigation, fact-checking, explanation traces.

  • Works in 7 steps: EXTRACT → VERIFY → ADJUDICATE → …
  • Tasks that involve Fact-checking and source verification
  • SKILL.md covers Mode A: Research Pipeline, Mode B: Fact-Check, Mode C: Explanation Traces and Deep References
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Research is an agent skill from notque/vexjoy-agent. Research: structured investigation, fact-checking, explanation traces.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/error-handling.md`, `references/preferred-patterns.md` and `references/trace-schema.md`).

It sits in Research & Science, covering Fact-checking and source verification. The repository describes itself as: VexJoy AI Agent with Jev Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop. The licence is MIT.

When your agent uses it

  • Tasks that involve Fact-checking and source verification

Example prompts

  • “/research”

Requirements

  • Pre-approved tools (allowed-tools): Read, Bash, Glob, Grep, Agent, Write, WebFetch, WebSearch

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. EXTRACT
  2. VERIFY
  3. ADJUDICATE
  4. REPORT
  5. LOCATE
  6. PARSE
  7. PRESENT

What it can do on your machine

Read from SKILL.md and the folder at commit 5218674. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash
    • Glob
    • Grep
    • Agent
    • Write
    • WebFetch
    • WebSearch

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    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

Research loads about 1.7k tokens when it runs, and up to ~7.1k if it reads all its reference files. Until then it costs about 20 tokens; SKILL.md has 645 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~20
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.1k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash, Glob, Grep, Agent, Write, WebFetch, WebSearch

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 notque/vexjoy-agent at commit 5218674, republished under its MIT licence (© notque). 645 words, ~1,687 tokens.

Download SKILL.mdSave it as .claude/skills/research/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
research
description
Research: structured investigation, fact-checking, explanation traces.
allowed-tools
Read, Bash, Glob, Grep, Agent, Write, WebFetch, WebSearch
user-invocable
true
argument-hint
<research topic or claim to verify>
agent
research-coordinator-engineer
context
fork
routing.force_route
true
routing.not_for
code review (use review), security audit (use security)
routing.triggers
research-pipeline, research, formal research, research with artifacts, systematic investigation, research report, gather evidence, fact check, fact-check…
routing.category
research
routing.pairs_with
review, writing

Research Skill

Three modes. Select by request signal:

SignalMode
Formal research, investigation, sourced report, gather evidenceResearch Pipeline
Fact check, verify claims, check facts, is this accurate, verify quoteFact-Check
Why did you, explain routing, show trace, decision log, why that agentExplanation Traces

Default: Research Pipeline.


Mode A: Research Pipeline


Mode B: Fact-Check

Verify every factual claim in a draft before publish. Burden of proof sits on the claim, not the checker. Works standalone or as a pre-publish gate. Non-blocking: the report warns; the caller decides whether to publish.

Phase 1: EXTRACT

List every checkable claim: statistics, prices, dates, quotes, attributions, titles, event facts, rankings, causal assertions. Opinions and speculation stay out.

For each claim, record: ID, verbatim text, type (stat/quote/attribution/event/title/price/causal), location. Extract quotes verbatim for exact-words comparison.

Gate: Every checkable assertion has a claim ID. Sweep the document twice.

Phase 2: VERIFY

Work claim by claim.

  1. Check provided sources first. Search caller-supplied documents before external sources. Record exact passages.
  2. Read laterally. Judge a source by what other sources say about it, not by its own presentation.
  3. Climb the source tier. Follow citations upward: primary (study, filing, transcript) > direct secondary (interviews, primary reading) > derived (aggregators, rewrites). A broken citation chain caps the claim at Unverifiable.
  4. Triangulate contested claims. Two independent sources (separate origins, not wire rewrites). One source suffices for routine facts from a primary document.
  5. Verify quotes on three axes. All must hold: exact words match, attributed speaker confirmed, original context supports the meaning used.
  6. Check staleness. Time-sensitive claims expire. Prices/rates: 1 day-1 week. Counts: 1-3 months. Titles/roles: 3-6 months. Event status: until event date. Surveys: 6-12 months. Science: 1-3 years. Laws: 6-12 months. Records/superlatives: re-check every use. Stable history: none.

Gate: Every claim has an evidence record.

Phase 3: ADJUDICATE

Assign each claim one label:

LabelAssign when
VerifiedEvidence supports; current within staleness window; sufficient source tier
DisputedEvidence contradicts; newer source supersedes; quote fails any axis
UnverifiableSources engage the claim but settle nothing
Missing-sourceNo available source addresses it

Rules: contradiction beats support. Partial verification gets the weakest label. Stale figure superseded by newer = Disputed; merely old with no newer figure = Unverifiable.

Gate: Every claim carries one label and a one-line justification.

Show full SKILL.md (270 more words)Show less
Phase 4: REPORT
# Fact-Check Report: [document]
## Summary
Claims: N | Verified: n | Disputed: n | Unverifiable: n | Missing-source: n
Unchecked: n (reason)
## Per-Claim Findings
### C1 -- [label]
Claim: [text] | Evidence: [source + passage] | Reasoning: [why this label]
## Warnings
[Every Disputed/Missing-source claim with correction]
## Publish Recommendation
[Hold / fix-then-publish / clear]

Every time-sensitive Verified claim carries its as-of date.

Gate: Report covers every claim ID. Warnings lists every Disputed and Missing-source finding.


Mode C: Explanation Traces

Read the per-dispatch route event log and present routing decisions as a human-readable timeline. Answer "why did I get routed here?" from recorded events only -- never from reconstruction or rationalization.

Log path: ${CLAUDE_LEARNING_DIR:-$HOME/.claude/learning}/route-events.jsonl (append-only JSONL).

Phase 1: LOCATE
bash
LOG="${CLAUDE_LEARNING_DIR:-$HOME/.claude/learning}/route-events.jsonl"
wc -l "$LOG"

If absent or empty, report the path and the producing hook (hooks/routing-decision-recorder.py). Do not reconstruct from memory.

Phase 2: PARSE

Parse each JSON line. Two event types: DECISION (one per /do-routed dispatch) and OUTCOME (one per finalized dispatch). See references/trace-schema.md for full field semantics.

Filter to the user's query:

User signalFilter
Names an agent or skillDECISION/OUTCOME events matching that name
"Why routed here" / latestMost recent DECISION, current session first
Outcome questionOUTCOME events, joined to decisions
No specific targetChronological timeline, most recent session

Join OUTCOME to DECISION on same session AND key == "{agent}:{skill}". File adjacency is unreliable.

Phase 3: PRESENT

Sort by ts. For each decision, show: time, agent+skill, complexity, request snippet, health at decision (three states: numeric, no-weight-row, legacy), alternates, outcome.

Lead with the answer to the user's specific question, then offer surrounding context. Flag gaps honestly: pre-instrumentation entries, unmatched outcomes.

request_snippet is private session data: show to the session's user, keep out of PR bodies, issues, exports.


Deep References

SignalReferenceContent
Event field schema, health states, join rulesreferences/trace-schema.mdDECISION/OUTCOME field semantics
Diagnosing thin trace data, consumer mistakesreferences/preferred-patterns.mdFailure mode catalog for log reading
Parse/read errors, missing log, unmatched outcomesreferences/error-handling.mdError-fix mappings for trace reading

© notque, 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 3 other files (references) in skills/research/research of notque/vexjoy-agent.

  • SKILL.md
  • references/error-handling.md
  • references/preferred-patterns.md
  • references/trace-schema.md

Open the folder on GitHubat commit 5218674

Compare with similar skills

Research 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.

Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Research this skillnotque/vexjoy-agent435—~1.7kAutomated safety check: NotesMIT
Perplexity Web Searchdavila7/claude-code-templates32k12 repos~3.5kAutomated safety check: NotesMIT
Citation Verification GuideGalaxy-Dawn/claude-scholar5.7k3 repos~1.9kAutomated safety check: PassMIT
Article Fact Checkerdigoal/blog8.6k—~939Automated safety check: PassGPL-2.0
Deep Research Agent TeamImbad0202/academic-research-skills51k—~13kAutomated safety check: PassCustom licence
Docs Grounding Verifiermicrosoft/apm4k—~1.9kAutomated safety check: PassMIT

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  • Perplexity Web Search

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    Research & ScienceAuto-check passed
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Questions about Research

What does Research do?

Research: structured investigation, fact-checking, explanation traces. Research is an agent skill from notque/vexjoy-agent. Research: structured investigation, fact-checking, explanation traces.

When should I use Research?

Research fits situations like: tasks that involve Fact-checking and source verification.

How do I install Research in Claude Code?

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

How do I install Research in Codex?

Run `npx skills add notque/vexjoy-agent --skill research -a codex`. Or copy the skill folder (skills/research/research in notque/vexjoy-agent) into .agents/skills/research in your project. Codex loads it when a task matches its description.

Can I use Research 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 notque/vexjoy-agent --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 Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Research is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Bash, Glob, Grep, Agent, Write, WebFetch, WebSearch.

Does Research 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 Research safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Research use?

Research 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 Research use?

About 1.7k tokens (SKILL.md is roughly 6.7k 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 5.5k tokens, read only when the agent opens those files.

What are the alternatives to Research?

Skills that share tags, products or a category with Research: Perplexity Web Search (davila7/claude-code-templates, 32k stars), Citation Verification Guide (Galaxy-Dawn/claude-scholar, 5.7k stars), Article Fact Checker (digoal/blog, 8.6k stars) and Deep Research Agent Team (Imbad0202/academic-research-skills, 51k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Research?

notque (a GitHub user) maintains it in notque/vexjoy-agent, which has 435 GitHub stars. The repository holds 61 skills in this directory. The repository was last updated on October 3, 2026.

Source: notque/vexjoy-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.