diffity-resolve

kamranahmedse/diffity · updated Apr 8, 2026

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$npx skills add https://github.com/kamranahmedse/diffity --skill diffity-resolve
0 commentsdiscussion
summary

You are reading open review comments and resolving them by making the requested code changes.

skill.md

Diffity Resolve Skill

You are reading open review comments and resolving them by making the requested code changes.

Arguments

  • thread-id (optional): Resolve a specific thread by ID instead of all open threads. Example: /diffity-resolve abc123

CLI Reference

diffity agent diff
diffity agent list [--status open|resolved|dismissed] [--json]
diffity agent comment --file <path> --line <n> [--end-line <n>] [--side new|old] --body "<text>"
diffity agent general-comment --body "<text>"
diffity agent resolve <id> [--summary "<text>"]
diffity agent dismiss <id> [--reason "<text>"]
diffity agent reply <id> --body "<text>"
  • --file, --line, --body are required for comment
  • --end-line defaults to --line (single-line comment)
  • --side defaults to new
  • general-comment creates a diff-level comment not tied to any file or line
  • <id> accepts full UUID or 8-char prefix

Prerequisites

  1. Check that diffity is available: run which diffity. If not found, install it with npm install -g diffity.
  2. Check that a review session exists: run diffity agent list. If this fails with "No active review session", tell the user to start diffity first (e.g. diffity or /diffity-diff).

Instructions

  1. List open comment threads with full details:
    diffity agent list --status open --json
    
    If a thread-id argument was provided, filter to just that thread. The JSON output includes the full comment body, file path, line numbers, and side for each thread.
  2. If there are no open threads, tell the user there's nothing to resolve.
  3. For each open thread, check the comments array and the author.type field ("user" or "agent") on each comment: a. Skip general comments (filePath __general__) — these are summaries, not actionable code changes. b. Skip threads where the last comment is an agent reply that asks the user a question (e.g. "Could you clarify...?") and the user hasn't responded yet — the agent is waiting for user input. Still process threads where the agent left the original comment (code suggestion, review feedback, etc.) — those are actionable. c. [nit] comments — these are minor suggestions but still actionable. Resolve them like any other comment. d. [question] comments (from the user) — read the question, examine the relevant code, and resolve the thread with your answer as the summary:
    diffity agent resolve <thread-id> --summary "Your answer here"
    
    e. Comments phrased as questions without an explicit [question] tag (e.g. "should we add X?" or "can we rename this?") are suggestions — treat them as actionable requests and make the change. f. Read the comment body from the JSON output and understand what change is requested. Interpret the intent:
    • If the comment suggests a code change, make the change.
    • If the comment suggests adding documentation, add or update the relevant docs.
    • If the comment asks a question that implies an action (e.g. "should we add X?"), treat it as a request to do that action.
    • If the comment is genuinely unclear and you cannot determine what action to take, reply asking for clarification instead of silently skipping:
      diffity agent reply <thread-id> --body "Could you clarify what change you'd like here?"
      
    g. Read the relevant source file to understand the full context around the commented lines, then make the requested change using the Edit tool. h. After making the change, resolve the thread with a summary:
    diffity agent resolve <thread-id> --summary "Fixed: <brief description of what was changed>"
    
  4. After resolving all applicable threads, run diffity agent list to confirm status.
  5. Tell the user to check the browser — resolved status will appear within 2 seconds via polling.
how to use diffity-resolve

How to use diffity-resolve 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 diffity-resolve
2

Execute installation command

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

$npx skills add https://github.com/kamranahmedse/diffity --skill diffity-resolve

The skills CLI fetches diffity-resolve from GitHub repository kamranahmedse/diffity 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/diffity-resolve

Reload or restart Cursor to activate diffity-resolve. Access the skill through slash commands (e.g., /diffity-resolve) 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

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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

4.536 reviews
  • Ganesh Mohane· Dec 28, 2024

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

  • Shikha Mishra· Dec 24, 2024

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

  • Mei Thomas· Dec 24, 2024

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

  • Yusuf Martinez· Dec 8, 2024

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

  • Yash Thakker· Nov 15, 2024

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

  • Fatima Martin· Nov 15, 2024

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

  • Dhruvi Jain· Oct 6, 2024

    Registry listing for diffity-resolve matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Michael Martin· Oct 6, 2024

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

  • Diego Srinivasan· Sep 21, 2024

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

  • Amina Menon· Aug 12, 2024

    Solid pick for teams standardizing on skills: diffity-resolve is focused, and the summary matches what you get after install.

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