query-writing

langchain-ai/deepagents · updated Apr 8, 2026

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$npx skills add https://github.com/langchain-ai/deepagents --skill query-writing
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summary

For straightforward questions about a single table:

skill.md

Query Writing Skill

Workflow for Simple Queries

For straightforward questions about a single table:

  1. Identify the table - Which table has the data?
  2. Get the schema - Use sql_db_schema to see columns
  3. Write the query - SELECT relevant columns with WHERE/LIMIT/ORDER BY
  4. Execute - Run with sql_db_query
  5. Format answer - Present results clearly

Workflow for Complex Queries

For questions requiring multiple tables:

1. Plan Your Approach

Use write_todos to break down the task:

  • Identify all tables needed
  • Map relationships (foreign keys)
  • Plan JOIN structure
  • Determine aggregations

2. Examine Schemas

Use sql_db_schema for EACH table to find join columns and needed fields.

3. Construct Query

  • SELECT - Columns and aggregates
  • FROM/JOIN - Connect tables on FK = PK
  • WHERE - Filters before aggregation
  • GROUP BY - All non-aggregate columns
  • ORDER BY - Sort meaningfully
  • LIMIT - Default 5 rows

4. Validate and Execute

Check all JOINs have conditions, GROUP BY is correct, then run query.

Example: Revenue by Country

SELECT
    c.Country,
    ROUND(SUM(i.Total), 2) as TotalRevenue
FROM Invoice i
INNER JOIN Customer c ON i.CustomerId = c.CustomerId
GROUP BY c.Country
ORDER BY TotalRevenue DESC
LIMIT 5;

Error Recovery

If a query fails or returns unexpected results:

  1. Empty results — Verify column names and WHERE conditions against the schema; check for case sensitivity or NULL values
  2. Syntax error — Re-examine JOINs, GROUP BY completeness, and alias references
  3. Timeout — Add stricter WHERE filters or LIMIT to reduce result set, then refine

Quality Guidelines

  • Query only relevant columns (not SELECT *)
  • Always apply LIMIT (5 default)
  • Use table aliases for clarity
  • For complex queries: use write_todos to plan
  • Never use DML statements (INSERT, UPDATE, DELETE, DROP)
how to use query-writing

How to use query-writing on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add query-writing
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/langchain-ai/deepagents --skill query-writing

The skills CLI fetches query-writing from GitHub repository langchain-ai/deepagents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/query-writing

Reload or restart Cursor to activate query-writing. Access the skill through slash commands (e.g., /query-writing) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.767 reviews
  • Dhruvi Jain· Dec 28, 2024

    Useful defaults in query-writing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Arjun Brown· Dec 24, 2024

    query-writing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aarav Singh· Dec 8, 2024

    query-writing has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Sophia Tandon· Dec 4, 2024

    I recommend query-writing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Min Okafor· Nov 27, 2024

    query-writing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Zara Sethi· Nov 23, 2024

    Keeps context tight: query-writing is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Oshnikdeep· Nov 19, 2024

    query-writing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Anika Ndlovu· Nov 15, 2024

    query-writing has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Amelia Harris· Oct 18, 2024

    We added query-writing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Charlotte Mensah· Oct 14, 2024

    query-writing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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