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/exa

@802b13f
by dianeldianel555/dskills65 stars
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High-precision semantic search and content retrieval via Exa API. Use when: (1) Deep research requiring semantic understanding, (2) Code documentation and examples lookup, (3) Company/professional research, (4) AI-powered comprehensive research tasks, (5) URL content extraction with structured output. Triggers: "research", "find papers", "code examples", "company info", "LinkedIn profiles", "deep analysis". Differentiator: Exa excels at semantic/neural search while grok-search is better for real-time news and general web content.

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Use this Skill: https://skilld.dev/gh/dianel555/dskills/exa

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referencesfiltering.md

≈562 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Filtering Results

After extracting data from search results, you may need to filter rows based on criteria from the original query. This file covers how to apply filters effectively.

Hard Filters

Hard filters have clear, binary criteria: a date range, a geographic constraint, a numeric threshold, a category membership.

Apply these mechanically:

  • Check each row against the criterion
  • Remove rows that fail
  • No judgment call needed

Examples: "published in 2025", "based in SF or NYC", "under $500B market cap", "excluding Novo Nordisk"

Negation filters ("excluding X", "not sponsored by Y") are hard filters applied in reverse. Check for the presence of the excluded value and remove matches.

Soft Filters

Soft filters require judgment: "genuine design opinion" vs "generic blog post", "actually shipping" vs "just evaluating", "high-signal" vs "noise".

For these:

  1. Read the relevant content (use web_fetch_exa if snippets are insufficient)
  2. Make a judgment call based on the content
  3. Include a brief rationale for each keep/drop decision so your reasoning is visible

Semantic negation is a type of soft filter: "no review mentions smell, noise, or pest complaints" requires reading review content and detecting whether these topics appear, even if phrased differently.

Filter Order

Apply filters in this order to minimize wasted work:

  1. Hard filters first -- cheap, mechanical, eliminates rows before you spend tokens on judgment
  2. Soft filters second -- only on rows that passed hard filters

Temporal Filters

Queries often involve time: "in the last 6 months", "began enrolling in 2025", "recent".

  • Calculate exact date boundaries from the current date before filtering
  • Check publication/event dates against the boundary
  • If a date is ambiguous (e.g. "early 2025"), note the uncertainty rather than silently including or excluding

Completeness vs Precision

The original query determines the balance:

  • "Find every..." or "exhaustive" -- err on the side of including borderline cases, flag them as uncertain
  • "Find the best..." or "top N" -- err on the side of precision, drop borderline cases
  • Default: include borderline cases with a flag, let downstream processing decide

Source: SKILL.md on GitHub

Third-party checks

2 warnings7mo4 checks · Risk SAFE
  • Gen Agent Trust Hub7mo

    The Exa Search CLI is a legitimate and safe skill for performing semantic searches and web crawling via the Exa API. It correctly handles API credentials through environment variables and relies on trusted Python libraries. No malicious behavior or security risks were identified.

  • Socket7mo

    No alerts

  • Snyk7mo

    Risk: MEDIUM · No issues

  • Runlayer7mo

    4/4 files flagged

Provenance

Signed by skilld at 802b13f. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub last week.

Activeupdated 2 months ago

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