You are reading open review comments and resolving them by making the requested code changes.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondiffity-resolveExecute the skills CLI command in your project's root directory to begin installation:
Fetches diffity-resolve from kamranahmedse/diffity and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate diffity-resolve. Access via /diffity-resolve in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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You are reading open review comments and resolving them by making the requested code changes.
thread-id (optional): Resolve a specific thread by ID instead of all open threads. Example: /diffity-resolve abc123diffity 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 newgeneral-comment creates a diff-level comment not tied to any file or line<id> accepts full UUID or 8-char prefixdiffity is available: run which diffity. If not found, install it with npm install -g diffity.diffity agent list. If this fails with "No active review session", tell the user to start diffity first (e.g. diffity or /diffity-diff).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.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:
diffity agent reply <thread-id> --body "Could you clarify what change you'd like here?"
diffity agent resolve <thread-id> --summary "Fixed: <brief description of what was changed>"
diffity agent list to confirm status.Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
I recommend diffity-resolve for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
diffity-resolve is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added diffity-resolve from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
diffity-resolve fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: diffity-resolve is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in diffity-resolve — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for diffity-resolve matched our evaluation — installs cleanly and behaves as described in the markdown.
diffity-resolve has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added diffity-resolve from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: diffity-resolve is focused, and the summary matches what you get after install.
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