research
Research: structured investigation, fact-checking, explanation traces.
Install / Use
npx skills add notque/vexjoy-agent --skill researchInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
SecuritySupported Platforms
Our assessment of research
research scores 85/100 on our quality scale, 825th of 1,122 Security skills we index.
Its SKILL.md is 6.6 KB long, well organised into 18 sections with 2 code examples: a thorough specification that gives an agent plenty to work with.
It has 425 GitHub stars, a meaningful sign that others use it.
Maintenance, license and trust
- The repository was last updated 12 days ago, so research is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-10-15. Automated pattern scan on 2026-10-15. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
research compared with similar skills
All 4 of these similar skills score higher than research; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| research (this skill)by notque | 85 | 425 | 12d ago | SKILL.md |
| algorithmic-artby anthropics | 100 | 177.9k | 22d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 22d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 133.6k | 11d ago | SKILL.md |
| ui-ux-pro-maxby nextlevelbuilder | 100 | 133.6k | 11d ago | SKILL.md |
Frequently asked questions
- How do I install research?
- Run
npx skills add notque/vexjoy-agent --skill research. The install tabs above show the steps for each supported agent. - Which AI agents does research work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is research safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is research still maintained?
- The repository was last updated 12 days ago, so research is actively maintained.
Skill content
View source on GitHubname: research description: "Research: structured investigation, fact-checking, explanation traces." user-invocable: true argument-hint: "<research topic or claim to verify>" agent: research-coordinator-engineer context: fork allowed-tools:
- Read
- Bash
- Glob
- Grep
- Agent
- Write
- WebFetch
- WebSearch
routing:
force_route: true
not_for: "code review (use review), security audit (use security)"
triggers:
- "research-pipeline"
- "research"
- "formal research"
- "research with artifacts"
- "systematic investigation"
- "research report"
- "gather evidence"
- "fact check"
- "fact-check"
- "verify claims"
- "check facts"
- "verify this quote"
- "is this accurate"
- "is this true"
- "check this claim"
- "verify this"
- "are these numbers right"
- "why did you"
- "explain routing"
- "show trace"
- "decision log"
- "why that agent"
- "explain decision"
- "show decisions"
- "trace log" category: research pairs_with:
- review
- writing
Research Skill
Three modes. Select by request signal:
| Signal | Mode | |--------|------| | Formal research, investigation, sourced report, gather evidence | Research Pipeline | | Fact check, verify claims, check facts, is this accurate, verify quote | Fact-Check | | Why did you, explain routing, show trace, decision log, why that agent | Explanation 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.
- Check provided sources first. Search caller-supplied documents before external sources. Record exact passages.
- Read laterally. Judge a source by what other sources say about it, not by its own presentation.
- 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.
- Triangulate contested claims. Two independent sources (separate origins, not wire rewrites). One source suffices for routine facts from a primary document.
- Verify quotes on three axes. All must hold: exact words match, attributed speaker confirmed, original context supports the meaning used.
- 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:
| Label | Assign when | |-------|-------------| | Verified | Evidence supports; current within staleness window; sufficient source tier | | Disputed | Evidence contradicts; newer source supersedes; quote fails any axis | | Unverifiable | Sources engage the claim but settle nothing | | Missing-source | No 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.
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
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 signal | Filter | |-------------|--------| | Names an agent or skill | DECISION/OUTCOME events matching that name | | "Why routed here" / latest | Most recent DECISION, current session first | | Outcome question | OUTCOME events, joined to decisions | | No specific target | Chronological 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
| Signal | Reference | Content |
|--------|-----------|---------|
| Event field schema, health states, join rules | references/trace-schema.md | DECISION/OUTCOME field semantics |
| Diagnosing thin trace data, consumer mistakes | references/preferred-patterns.md | Failure mode catalog for log reading |
| Parse/read errors, missing log, unmatched outcomes | references/error-handling.md | Error-fix mappings for trace reading |
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
