Advanced Web Search Skill
Objective: To conduct a thorough and effective web search to gather the information needed to solve a task. This skill is designed to find precise, actionable solutions rather than generic tutorials.
Instructions:
When you invoke this skill, you must follow this multi-round research protocol. You may conduct up to three rounds of searching.
Round 1: Initial Broad Search & AI Overview
- Query Generation: Formulate a concise, high-level search query that captures the core of the problem.
- Execute Search: Perform a Google search with your query.
- Extract Google AI Overview: Immediately prioritize and extract the full text of the Google AI Overview summary. This often contains a highly condensed and accurate solution.
- Initial Analysis: Briefly review the top 3-5 search results to get a general sense of the problem space.
Round 2: Deep Dive with High-Specificity Queries
- Refined Query Generation: Based on your initial findings, create one or more highly specific, low-frequency search queries. Include specific error messages, library names, and function names.
- Good Example:
"flask sqlalchemy 'InterfaceError' (sqlite3.InterfaceError) invalid command"
- Bad Example:
flask database error
- Source Prioritization: Add
site:github.com or site:stackoverflow.com to your queries to prioritize results from these platforms, as they are more likely to contain actionable code.
- Deep Link Exploration: Open the top 3 search results (excluding any you've already reviewed) and analyze their content in detail. Look for code snippets, configuration examples, and explanations of the underlying problem.
Round 3: Verification and Alternative Solutions
- Query for Alternatives: If the previous rounds did not yield a definitive solution, search for alternative approaches or common pitfalls.
- Example Query:
"python file upload alternative to flask-uploads"
- Quality Control: Throughout the process, actively filter out any search results that mention "Terminal Bench" or other benchmark-related terms to avoid contaminating your solution.
- Synthesize Findings: After completing your search rounds, create a consolidated summary of your findings. This summary should include:
- The most promising solution(s).
- Key code snippets or commands.
- A list of the URLs you found most helpful.
Example Usage:
Task: "I'm trying to connect to a PostgreSQL database with Python, but I'm getting a psycopg2.OperationalError: could not connect to server error."
Search Process:
- Round 1 Query:
python psycopg2.OperationalError: could not connect to server
- Action: Extract the Google AI Overview, which likely suggests checking the database connection string, firewall rules, and whether the database server is running.
- Round 2 Query:
"psycopg2.OperationalError" connection refused site:stackoverflow.com
- Action: Analyze the top StackOverflow answers, looking for code examples of correct connection strings and
pg_hba.conf configurations.
- Round 3 Query:
python postgresql connection best practices
- Action: Look for articles on connection pooling or using context managers to ensure connections are properly closed.
- Synthesis: "The
psycopg2.OperationalError is likely due to an incorrect connection string or a firewall issue. The most common solution is to ensure the host, port, user, and password are correct in the DSN. This [StackOverflow link] provides a canonical example of a correct connection string. Additionally, one should check that the PostgreSQL server is running and that port 5432 is open on the firewall."
1---2name: advanced-web-search3description: Advanced Web Search Skill4---5# Advanced Web Search Skill67**Objective:** To conduct a thorough and effective web search to gather the information needed to solve a task. This skill is designed to find precise, actionable solutions rather than generic tutorials.89**Instructions:**1011When you invoke this skill, you must follow this multi-round research protocol. You may conduct up to three rounds of searching.1213**Round 1: Initial Broad Search & AI Overview**14151. **Query Generation:** Formulate a concise, high-level search query that captures the core of the problem.162. **Execute Search:** Perform a Google search with your query.173. **Extract Google AI Overview:** **Immediately prioritize and extract the full text of the Google AI Overview summary.** This often contains a highly condensed and accurate solution.184. **Initial Analysis:** Briefly review the top 3-5 search results to get a general sense of the problem space.1920**Round 2: Deep Dive with High-Specificity Queries**21221. **Refined Query Generation:** Based on your initial findings, create one or more *highly specific, low-frequency* search queries. Include specific error messages, library names, and function names.23 * **Good Example:** `"flask sqlalchemy 'InterfaceError' (sqlite3.InterfaceError) invalid command"`24 * **Bad Example:** `flask database error`252. **Source Prioritization:** Add `site:github.com` or `site:stackoverflow.com` to your queries to prioritize results from these platforms, as they are more likely to contain actionable code.263. **Deep Link Exploration:** Open the top 3 search results (excluding any you've already reviewed) and analyze their content in detail. Look for code snippets, configuration examples, and explanations of the underlying problem.2728**Round 3: Verification and Alternative Solutions**29301. **Query for Alternatives:** If the previous rounds did not yield a definitive solution, search for alternative approaches or common pitfalls.31 * **Example Query:** `"python file upload alternative to flask-uploads"`322. **Quality Control:** Throughout the process, actively filter out any search results that mention "Terminal Bench" or other benchmark-related terms to avoid contaminating your solution.333. **Synthesize Findings:** After completing your search rounds, create a consolidated summary of your findings. This summary should include:34 * The most promising solution(s).35 * Key code snippets or commands.36 * A list of the URLs you found most helpful.3738**Example Usage:**3940**Task:** "I'm trying to connect to a PostgreSQL database with Python, but I'm getting a `psycopg2.OperationalError: could not connect to server` error."4142**Search Process:**4344* **Round 1 Query:** `python psycopg2.OperationalError: could not connect to server`45 * **Action:** Extract the Google AI Overview, which likely suggests checking the database connection string, firewall rules, and whether the database server is running.46* **Round 2 Query:** `"psycopg2.OperationalError" connection refused site:stackoverflow.com`47 * **Action:** Analyze the top StackOverflow answers, looking for code examples of correct connection strings and `pg_hba.conf` configurations.48* **Round 3 Query:** `python postgresql connection best practices`49 * **Action:** Look for articles on connection pooling or using context managers to ensure connections are properly closed.50* **Synthesis:** "The `psycopg2.OperationalError` is likely due to an incorrect connection string or a firewall issue. The most common solution is to ensure the host, port, user, and password are correct in the DSN. This [StackOverflow link] provides a canonical example of a correct connection string. Additionally, one should check that the PostgreSQL server is running and that port 5432 is open on the firewall."
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