deep-research

Overview of deep-research skill

The deep-research skill is a structured research workflow for agents that need to investigate a topic, compare multiple sources, and return a synthesized answer with citations. It is best for users who want more than a fast summary: analysts, writers, founders, students, and operators doing web research where source quality and viewpoint coverage matter.

What deep-research is actually for

Use deep-research when the job is not just “answer this question,” but “research this question well.” The skill pushes an agent to:

• clarify the research objective,
• break the topic into sub-questions,
• gather information from multiple perspectives,
• evaluate source credibility and recency,
• synthesize findings instead of listing links,
• and produce cited analysis.

That makes it a better fit than an ordinary prompt when the output needs traceability, balanced coverage, or decision-ready summaries.

Best-fit users and tasks

The deep-research skill is a strong fit for:

• market and competitor scans,
• policy or regulation overviews,
• technical landscape research,
• literature-style topic summaries,
• founder diligence and vendor evaluation,
• any web research task where citations matter.

It is less useful for simple factual lookup, creative brainstorming, or tasks where the user already knows the exact source set to summarize.

What makes deep-research different from a generic prompt

The main differentiator is process discipline. Instead of asking the model to “research X,” deep-research gives a repeatable sequence: clarify scope, define angles, gather sources, assess quality, then synthesize. That usually improves:

• source diversity,
• coverage of competing views,
• citation quality,
• and answer structure.

In practice, users care about whether the agent can produce a report they can trust and reuse. This skill is designed around that outcome.

What to check before you install

This repository path is lightweight: the core logic is in SKILL.md, with no extra scripts, rules, or reference files surfaced in the tree preview. That is good for quick adoption, but it also means you should expect prompt-and-workflow guidance rather than tooling, source packs, or automation helpers.

If you want a turnkey crawler, dataset pipeline, or custom ranking system, deep-research is probably too minimal on its own.

How to Use deep-research skill

Install deep-research in a Skills-enabled environment

If your agent runtime supports Skills, install deep-research from the repository:

npx skills add Shubhamsaboo/awesome-llm-apps --skill deep-research

After install, attach or invoke the skill from your compatible agent environment. The repository evidence points to a single-file skill, so there is little setup beyond adding it and giving the agent web-access or source material.

Read this file first

Start with:

• awesome_agent_skills/deep-research/SKILL.md

Because there are no additional support files surfaced here, SKILL.md is the primary source of truth for:

• when to apply the skill,
• the research process,
• output expectations,
• and the intended reasoning sequence.

Know the minimum input deep-research needs

deep-research usage is much better when you provide four things up front:

1. the research question,
2. the purpose of the research,
3. the desired depth,
4. any priority angles or constraints.

Weak input:

• “Research AI chips.”

Stronger input:

• “Research the AI chip market for enterprise inference in 2024–2025. Compare NVIDIA, AMD, Intel, and custom cloud accelerators. Focus on pricing signals, software ecosystem maturity, deployment constraints, and buyer switching costs. Deliver a cited executive summary for a CTO deciding whether to stay standardized on CUDA.”

The second version gives the skill a clear scope, comparison frame, and decision context.

Turn a rough goal into a usable research brief

A good deep-research guide starts by converting vague intent into research dimensions. Before you run the skill, specify:

• topic or decision,
• timeframe,
• geography,
• stakeholder perspective,
• must-cover subtopics,
• desired output format,
• acceptable sources,
• excluded angles.

A compact template:

• Objective: what decision or understanding is needed?
• Scope: what is in and out?
• Time range: how current must sources be?
• Perspectives: whose views should be compared?
• Deliverable: summary, memo, table, or recommendation?
• Citation expectation: inline citations, source list, or both?

This matters because the skill explicitly begins by clarifying the research question and identifying key aspects.

Use deep-research for web research, not just summarization

deep-research for Web Research works best when the agent can inspect multiple live or user-provided sources rather than paraphrase one article. The skill’s value comes from synthesis across sources and viewpoints.

A practical workflow:

1. define the question,
2. collect candidate sources,
3. ask the agent to assess credibility and recency,
4. synthesize patterns, disagreements, and gaps,
5. then produce the final report with citations.

If you skip source gathering and synthesis, you reduce the skill to an ordinary summary prompt.

Ask for source evaluation, not only findings

One of the most useful parts of deep-research is that it explicitly includes credibility checks. In your prompt, ask the agent to note:

• which sources are primary vs. secondary,
• how current they are,
• whether there are conflicts of interest,
• where evidence is thin or disputed.

This is especially important for fast-moving topics, vendor claims, health information, policy interpretation, and market estimates.

Suggested output structure for better results

To make deep-research usage more reliable, ask for an output shape such as:

1. research question,
2. scope and assumptions,
3. key findings,
4. source-backed evidence by subtopic,
5. areas of agreement and disagreement,
6. confidence or evidence-quality notes,
7. open questions,
8. cited conclusion.

That structure matches the skill’s stated synthesis workflow and reduces the chance of a shallow link dump.

Practical prompt pattern that invokes the skill well

A strong invocation pattern:

“Use deep-research to investigate [topic]. Clarify the research question first, break it into subtopics, gather information from multiple perspectives, evaluate source credibility and publication date, then synthesize findings with citations. Prioritize [angles]. Exclude [out-of-scope items]. End with key conclusions, uncertainties, and recommended next questions.”

This works because it reinforces the skill’s internal sequence instead of fighting it.

When to narrow the scope before running deep-research

The biggest practical blocker is oversized scope. If your first request spans too many markets, years, or stakeholder groups, output quality usually drops. Narrow first by:

• one geography,
• one buyer persona,
• one time window,
• one decision question,
• or one comparison set.

Example:
Instead of “Research remote work software,” ask:

• “Compare Notion, Confluence, and Coda for 500-person engineering organizations in 2025, focusing on governance, search quality, AI features, and migration risk.”

What the repository does not give you

This deep-research install is simple, but do not expect:

• built-in retrieval scripts,
• custom ranking or citation tooling,
• source libraries,
• domain-specific rules,
• or prewritten output templates beyond the core guidance.

That means the skill is easy to adopt, but your own prompt quality and runtime capabilities will heavily affect results.

deep-research skill FAQ

Is deep-research better than a normal research prompt?

Usually yes, when the task needs structure, source comparison, and citations. A plain prompt may answer quickly, but deep-research is more likely to:

• separate subtopics,
• cover multiple perspectives,
• check source quality,
• and produce a reusable research summary.

If your task is simple factual lookup, the extra structure may be unnecessary.

Is deep-research suitable for beginners?

Yes. The skill is readable and lightweight, with the core workflow in one SKILL.md file. That makes it approachable for users who want a repeatable research method without installing extra tooling.

The tradeoff is that beginners still need to write a decent research brief. The skill improves process, but it cannot guess unclear goals.

When should I not use the deep-research skill?

Skip deep-research when:

• you only need a quick answer,
• you already have a fixed source set and just need summarization,
• the task is creative rather than analytical,
• or the agent has no source access and cannot evaluate evidence well.

It is also a weak fit for highly domain-regulated work where you need specialist databases or formal legal/medical review.

Does deep-research require web access?

Not strictly, but it performs best with access to multiple sources, especially for current topics. Without web access, you can still use the deep-research skill on a user-provided corpus, but source breadth and freshness will depend on what you supply.

How does deep-research handle conflicting sources?

The workflow explicitly calls for synthesizing findings and noting areas of consensus and disagreement. In practice, you should instruct the agent to:

• present competing claims,
• identify stronger evidence,
• and explain why disagreement exists.

That is more useful than forcing a false single conclusion.

Can I use deep-research for internal company research?

Yes, if you provide the materials. The same process works on internal docs, customer transcripts, strategy memos, or competitor notes. Just tell the agent which sources are authoritative and whether external web research should be included.

How to Improve deep-research skill

Give deep-research a decision context

The fastest way to improve output is to say what the research will be used for. “Research this topic” is weaker than:

• “I need to choose a vendor,”
• “I need an investor memo,”
• “I need a balanced brief for executives,”
• or “I need a literature-style overview.”

Decision context helps the skill prioritize relevance over volume.

Specify the comparison axes up front

Many weak research outputs fail because the model chooses its own dimensions. For better deep-research results, define the axes yourself.

Example:
“Compare by total cost, integration difficulty, compliance support, switching risk, and evidence strength.”

This leads to more decision-useful synthesis than a generic pros/cons list.

Set source-quality expectations explicitly

If citation quality matters, say so. Ask the agent to prefer:

• primary sources where possible,
• recent materials for fast-moving topics,
• and clearly labeled secondary commentary when primary evidence is unavailable.

Also ask it to flag weak evidence rather than smoothing over gaps.

Force a subtopic map before full synthesis

A practical improvement step:

1. ask the agent to propose subtopics first,
2. review and refine them,
3. then run the full research pass.

This reduces missed angles and keeps the final report aligned with your real question.

Correct the most common failure modes

Typical failure modes with deep-research usage:

• scope is too broad,
• sources are not diverse enough,
• citations exist but are weak,
• findings are listed rather than synthesized,
• disagreement is ignored,
• conclusions overstate certainty.

To fix these, ask for:

• narrower scope,
• explicit source-type diversity,
• evidence-quality notes,
• a consensus vs. disagreement section,
• and a short limitations section.

Ask for uncertainties and next-step research

A strong research output should not pretend everything is settled. Improve deep-research by requiring:

• unanswered questions,
• data gaps,
• assumptions made,
• and what to research next.

This is especially useful when the first pass is exploratory and will guide a second pass.

Iterate after the first output instead of restarting

Do not throw away the first result if it is only partly right. The best refinement loop is:

• identify missing angles,
• ask for deeper work on one subtopic,
• tighten source standards,
• and request a revised synthesis.

Example follow-up:
“Expand the disagreement section on open-source vs. proprietary models. Add newer sources, separate vendor claims from independent analysis, and revise the conclusion to reflect evidence strength.”

That usually beats starting from scratch.

Pair deep-research with your own source list when stakes are high

For high-stakes work, improve trust by seeding the process with:

• must-read sources,
• known primary documents,
• credible expert publications,
• and any internal materials you already trust.

The skill still helps with synthesis, but your curated inputs reduce hallucinated authority and weak-source drift.

Keep the final ask concrete

The deep-research skill performs best when the final deliverable is explicit. Ask for one of:

• executive memo,
• comparative table,
• source-backed brief,
• literature-style summary,
• recommendation with caveats.

Concrete output requests lead to cleaner, more usable research than “tell me everything about this topic.”