perplexity

Overview of perplexity skill

The perplexity skill is a routing and usage guide for Perplexity-powered web research inside softaworks/agent-toolkit. Its real job is not just “search the web,” but helping an agent choose the right Perplexity tool for a request, keep result volume under control, and avoid using web search when a more specific tool would do better.

Who this perplexity skill is for

This perplexity skill fits users who need:

• current web information
• resource discovery and source URLs
• lightweight research on broad topics
• better defaults than a raw “search the web” prompt

It is especially useful if you want an agent to decide between quick search, conversational answering, and deeper research without wasting tokens.

What users actually get from perplexity

The value of perplexity here is workflow discipline:

• choose Perplexity Search when you want links and recent sources
• choose Perplexity Ask when you want a direct answer
• escalate to a Researcher agent for deep, multi-step research

That distinction matters because many agents over-search, return too many results, or use web search for tasks that should stay inside docs or workspace tools.

Best-fit jobs-to-be-done

Use perplexity for:

• “find recent articles about…”
• “look up current best practices for…”
• “search for tutorials/resources on…”
• “what’s the latest on…”
• “ask Perplexity for a quick summary of…”

If your goal is web research with a freshness requirement, this skill is a good fit.

Important boundaries before you install

This skill is intentionally narrow. It says not to use Perplexity for:

• library or framework docs → use Context7
• workspace-specific questions → use Nx MCP
• Graphite gt CLI questions → use Graphite MCP
• a specific known URL → use a URL crawler
• deep research by default → use /research <topic>

That makes perplexity more useful than a generic search wrapper: it reduces wrong-tool usage.

What differentiates this skill from ordinary prompting

A normal prompt might say “search the web for X.” This skill adds operational guidance that improves quality:

• starts with low search limits to avoid context bloat
• distinguishes search vs answer vs research
• gives clear “do not use” cases
• treats web research as a scoped tool, not a default reflex

For install decisions, that is the main advantage.

How to Use perplexity skill

Install context for perplexity

If you are using the toolkit’s standard install flow, add the skill with:

npx skills add softaworks/agent-toolkit --skill perplexity

Then read:

1. skills/perplexity/SKILL.md
2. skills/perplexity/README.md

SKILL.md is the faster operational reference; README.md gives more explanation.

Read these repository files first

Start with:

• SKILL.md for routing rules and default parameters
• README.md for fuller usage intent

This skill has no large supporting rules/ or resources/ tree, so most of the useful guidance is right in those two files.

Decide which Perplexity path to use

The repository makes three practical paths clear:

• Perplexity Search: best when you need URLs, sources, or recent articles
• Perplexity Ask: best when you need a direct conversational answer
• Researcher agent via /research <topic>: best for deeper, broader investigation

A simple selection rule:

• Need links? use Search.
• Need a concise answer? use Ask.
• Need synthesis across many angles? use Researcher.

Use perplexity only for the right trigger phrases

The skill is designed for requests like:

• “search”
• “find”
• “look up”
• “ask”
• “research”
• “what’s the latest”

That may sound obvious, but it prevents a common failure mode: using web research for every ambiguous question.

Start with the default search limits

The strongest practical advice in this perplexity guide is to begin with small limits. The repo explicitly recommends:

• max_results: 3
• max_tokens_per_page: 512

Why this matters:

• keeps answers focused
• reduces noisy source dumps
• avoids spending tokens on low-value pages
• makes first-pass research faster

Increase limits only when the initial search is clearly insufficient or the user explicitly wants broader coverage.

What input perplexity needs from you

For good perplexity usage, provide:

• the exact topic
• the freshness need, if any
• the desired output type
• any constraints on source type or scope

Weak input:

• “search AI agents”

Stronger input:

• “Search for recent 2024–2025 articles on enterprise AI agent evaluation frameworks. Return 3 strong sources with URLs and a one-line reason each.”

The stronger version tells the skill what to search, how current it should be, and what success looks like.

Turn a rough goal into a better prompt

A good pattern for perplexity for Web Research is:

Goal + time frame + source preference + output format

Example:

• “Find recent best-practice articles on RAG evaluation from the last 12 months. Prefer practical engineering sources. Return 3 URLs and summarize the main evaluation criteria.”

That works better than:

• “research RAG evaluation”

Because it narrows recency, source type, and response structure.

Suggested workflow for practical perplexity usage

A reliable workflow is:

1. Start with Perplexity Search
2. Review whether the top 3 results are relevant
3. If you mainly need interpretation, switch to Perplexity Ask
4. If coverage is still too shallow, escalate to /research <topic>

This staged approach is better than jumping straight into exhaustive research.

When to increase result limits

Increase search breadth only if:

• the first pass found little of value
• the topic is unusually fragmented
• the user asked for comprehensive coverage
• you need multiple viewpoints or sources

Do not increase limits just because “more results feels safer.” In practice, that often lowers answer quality.

Misfit cases that block adoption

Do not install this expecting a universal research layer. The perplexity skill is a poor fit if your work is mostly:

• official API or framework documentation lookup
• repository or workspace introspection
• fixed-URL extraction
• deep literature-style synthesis by default

In those cases, the skill’s own guidance points you to other tools.

A practical example prompt

A strong starter prompt:

“Use perplexity to search for recent guidance on AI product analytics instrumentation. I need 3 high-quality sources with URLs, published recently if possible, plus a short note on why each source is worth reading.”

Why it works:

• explicit tool intent
• current-information cue
• manageable result count
• clear output format
• source-quality expectation

perplexity skill FAQ

Is perplexity mainly a search tool or a research tool?

Both, but not in the same way. In this repo, perplexity is best treated as a lightweight web research layer:

• Search for URLs and recent sources
• Ask for a direct answer
• hand off deep investigation to /research

Is this better than a normal “search the web” prompt?

Yes, if you want more consistent behavior. The skill adds:

• tool selection rules
• explicit non-use cases
• lower default search limits
• escalation guidance

Those are the parts that reduce guesswork.

Is perplexity good for beginners?

Yes. The scope is narrow, and the routing rules are easy to follow. Beginners mostly need to remember one thing: use it for generic web research, not docs, workspace questions, or known URLs.

When should I not use this perplexity skill?

Skip it when the task is:

• official docs lookup
• workspace-specific analysis
• a specific URL fetch
• deep research that already needs a researcher workflow

That is one of the strongest signals in the repo, and following it improves results.

Does perplexity replace documentation tools?

No. This perplexity guide is explicit that docs questions should go to Context7, not Perplexity. That boundary is important because web results are often noisier than official docs.

Is the skill opinionated about token usage?

Yes. It deliberately starts with tighter search limits. That is a feature, not a missing capability. The goal is useful first-pass research without flooding the context window.

How to Improve perplexity skill

Give perplexity a research brief, not a topic fragment

Better output usually comes from specifying:

• topic
• recency
• audience or use case
• preferred source type
• requested format

Instead of:

• “find MCP resources”

Use:

• “Find recent implementation-focused resources on MCP server design for engineering teams. Return 3 URLs, and note which are best for architecture vs hands-on setup.”

Ask for output structure up front

A simple structure request improves perplexity usage a lot:

• “3 sources”
• “one-line takeaway each”
• “include URL”
• “compare them”
• “flag which source is most current”

This reduces rambling summaries and makes results easier to act on.

Prevent the most common failure mode: wrong tool choice

A weak result often starts before the search even runs. Improve quality by checking:

• Is this really generic web research?
• Would Context7 be better?
• Is this a known URL task?
• Is this actually deep research?

Many bad outputs are routing errors, not search errors.

Use a narrow first pass, then iterate

The best way to improve perplexity is usually:

1. run a small search
2. inspect relevance
3. refine the query
4. only then widen scope

This is better than starting broad. It produces cleaner sources and makes it easier to see what is missing.

Refine queries with missing dimensions

If the first output is weak, add one or more of:

• date range
• geography
• audience
• source type
• technical depth
• comparison target

Example refinement:

• first pass: “search AI eval frameworks”
• improved: “Search for recent engineering-focused AI evaluation frameworks for LLM apps, emphasizing production monitoring and offline eval.”

Improve source quality with explicit preferences

If you care about trustworthiness, say so:

• prefer official company engineering blogs
• prefer implementation guides over opinion pieces
• prefer recent sources
• exclude vendor landing pages if possible

That changes result quality more than simply asking for “more results.”

Know when to escalate beyond perplexity

If you need:

• broad synthesis across many subtopics
• evidence gathering over multiple rounds
• a research memo rather than quick findings

move from the perplexity skill to the Researcher agent. Good usage includes knowing when not to keep pushing the lighter tool.

Improve the skill locally if you maintain it

If you are editing the repo, the biggest improvements would be:

• add one or two full prompt examples for Search vs Ask
• document Perplexity Ask with the same specificity as Search
• include a short decision table for “search / ask / research / not Perplexity”
• show one bad query and its improved version

Those additions would reduce ambiguity faster than adding more general prose.