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research

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

Install / Use

npx skills add notque/vexjoy-agent --skill research

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Category

Security

Supported Platforms

Universal

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.

Substance
29/30
Structure
18/20
Description
12/15
Adoption
11/20
Freshness
15/15

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 found

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.

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.

SkillScoreStarsUpdatedFormat
research (this skill)by notque8542512d agoSKILL.md
algorithmic-artby anthropics100177.9k22d agoSKILL.md
pptxby anthropics100177.9k22d agoSKILL.md
designby nextlevelbuilder100133.6k11d agoSKILL.md
ui-ux-pro-maxby nextlevelbuilder100133.6k11d agoSKILL.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.

name: 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.

  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:

| 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 |

Related Skills

View on GitHub
GitHub Stars425
CategorySecurity
Updated12d ago
Forks48

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions