agentleFS
hint-shuhint-shu/deep-research/skills/quick-research/SKILL.md

quick-research

Skill4 starsChanged 7 months agoMITView on GitHub

Quick web research for simple questions — fast answer with 3-5 cited sources in ~1 minute. Use for fact-checks, simple lookups, "what is X", "latest version of Y", "who made Z". For anything deeper, use /research (L1) or /deep-research (L2).

6 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 3 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 6 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If mcp__perplexity-ask__perplexity_ask is available AND DEEP_RESEARCH_DISABLE_PERPLEXITY is unset, try Perplexity first.

    line 46
  2. Distinctive:

    Fall back to the SEARCH→READ→ANSWER path below: if:

    line 57
  3. Distinctive:

    Run these in PARALLEL in a single message:

    line 64
  4. Very specific:

    Use head -80 and grep to find relevant sections.

    line 97
  5. Distinctive:

    If during research you realize the question is more complex than expected (needs comparison, deep analysis, multiple perspectives), STOP …

    line 127

“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Quick Research (L0)

Fast lightweight research for simple questions. No planner, no reflection, no multi-agent — just a quick multi-source search with a cited answer.

Position in ladder: L0 — the lightest tier. When in doubt between this and /research, prefer /research.

When to Use

  • Fact checks: "какая последняя версия Next.js", "когда вышел Bun 2.0"
  • Simple lookups: "что такое edge runtime", "кто автор библиотеки X"
  • Yes/no questions with supporting evidence
  • Any question the user frames as "быстро", "коротко", "кратко"

Do NOT use for:

  • Comparisons (use /deep-research)
  • Strategic decisions (use /expert-research)
  • Topics requiring depth (use /research or higher)

Budget

  • Time: ~1 min
  • Tavily credits: ~5
  • Sources read: 3–5
  • Output: 2–4 paragraphs with inline citations

Pipeline

v0.6.0+ fast path (Perplexity available):
  ASK — one Perplexity call → synthesized answer + citations (~5 sec)

Fallback path (Perplexity unavailable):
  1. SEARCH   — parallel Firecrawl + Tavily
  2. READ     — scrape top 3 results
  3. ANSWER   — short structured response with citations

Fast path: Perplexity (v0.6.0+)

If mcp__perplexity-ask__perplexity_ask is available AND DEEP_RESEARCH_DISABLE_PERPLEXITY is unset, try Perplexity first. It returns a synthesized answer with inline citations in one call — exactly L0's output shape, no scrape needed:

mcp__perplexity-ask__perplexity_ask with:
  messages: [
    {role: "user", content: "<user query>. Respond in 2-4 paragraphs with inline [N] citations followed by a Sources: list with [N] <URL> per line. Match the user's language (Russian/English)."}
  ]

Response is ready to forward to user. Typical latency: 5 seconds.

Fall back to the SEARCH→READ→ANSWER path below if:

  • Perplexity returns an error or rate limit
  • Response has fewer than 2 citations (quality check)
  • User explicitly wants independent verification (rare for L0)

Step 1: SEARCH (parallel multi-source)

Run these in PARALLEL in a single message:

firecrawl search "<query>" --limit 5 --json

And Tavily:

mcp__tavily__tavily_search with:
  query: <query>
  max_results: 5
  search_depth: "basic"

For Russian-language topics: add one extra search in Russian.

Step 2: READ (scrape top 3)

Pick the 3 most relevant/authoritative results and scrape them IN PARALLEL:

firecrawl scrape "<url1>" --only-main-content &
firecrawl scrape "<url2>" --only-main-content &
firecrawl scrape "<url3>" --only-main-content &
wait

Source priority:

  1. Official docs / primary sources
  2. Recent articles (2025–2026)
  3. Authoritative tech sources
  4. Skip: SEO spam, affiliate pages, paywalled content

Use head -80 and grep to find relevant sections. Don't read full files.

Step 3: ANSWER

Format the response as:

**[Direct answer in 1-2 sentences]**

[2-3 supporting paragraphs with inline citations [1], [2], [3]]

---
**Sources:**
1. [Title](URL)
2. [Title](URL)
3. [Title](URL)

Rules

  • Always cite — every factual claim needs [N] reference
  • Direct answer first — no preamble, no "let me research this"
  • Flag uncertainty — if sources disagree, say so briefly
  • Freshness — prefer 2025–2026 sources
  • Language — match user's language (Russian / English)
  • No artifacts on disk — this is a quick mode, don't save to .firecrawl/research/
  • No clarifying questions — if query is ambiguous, pick most likely interpretation and answer

Escalation

If during research you realize the question is more complex than expected (needs comparison, deep analysis, multiple perspectives), STOP and tell the user:

"Этот вопрос серьёзнее чем quick-research. Рекомендую /research (L1) или /deep-research (L2). Продолжить quick или переключиться?"

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.