pr-comments

casper-studios/casper-marketplace · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/casper-studios/casper-marketplace --skill pr-comments
0 commentsdiscussion
summary

Fetch all unresolved PR review threads, deduplicate across bots, triage by severity, and produce a fix plan for human approval. After sign-off, resolve ignored threads and spawn subagents to fix real issues.

skill.md

PR Comments — Triage & Fix

Fetch all unresolved PR review threads, deduplicate across bots, triage by severity, and produce a fix plan for human approval. After sign-off, resolve ignored threads and spawn subagents to fix real issues.

Invocation

  • /pr-comments — auto-detect PR from current branch
  • /pr-comments 608 — specific PR number

Phase 1: Fetch Unresolved Threads

1a. Identify the PR

# Auto-detect from current branch, or use the provided PR number
gh pr view --json number,headRepositoryOwner,title,headRefName,baseRefName

1b. Fetch ALL review threads via GraphQL

Use GraphQL to get thread resolution status — this is the only reliable source of truth.

gh api graphql -f query='
{
  repository(owner: "{OWNER}", name: "{REPO_NAME}") {
    pullRequest(number: {PR_NUMBER}) {
      reviewThreads(first: 100) {
        nodes {
          id
          isResolved
          isOutdated
          path
          line
          comments(first: 20) {
            nodes {
              databaseId
              author { login }
              body
              path
              line
              originalLine
              createdAt
              url
            }
          }
        }
      }
    }
  }
}'

Paginate if hasNextPage is true. Collect every thread.

1c. Filter to unresolved threads only

  • Keep threads where isResolved == false
  • Note isOutdated — the diff may have moved; flag these for extra scrutiny

1d. Also fetch issue-level comments (PR conversation tab)

gh api --paginate "repos/{OWNER}/{REPO_NAME}/issues/{PR_NUMBER}/comments?per_page=100"

Filter to comments from human reviewers only (not bots). These are often the most important.

Phase 2: Deduplicate & Classify

Multiple bots often flag the same underlying issue on the same file/line. Group them.

2a. Group by file + line range

Threads targeting the same file within a 5-line range likely address the same issue. Merge them into a single logical issue.

2b. Parse severity from bot comments

Each bot uses different severity markers:

Bot Format Example
coderabbitai[bot] Emoji badge in body 🟠 Major, 🟡 Minor, 🔴 Critical
gemini-code-assist[bot] SVG image alt text ![medium], ![high], ![low]
chatgpt-codex-connector[bot] Shield badge P1, P2, P3
devin-ai-integration[bot] HTML comment metadata Parse devin-review-comment JSON for severity

Map all to a unified scale: Critical > Major > Medium > Minor > Nitpick

When multiple bots flag the same issue at different severities, take the highest.

2c. Classify each issue

For each deduplicated issue, determine:

  1. Category: security | bug | correctness | performance | accessibility | style | config | docs
  2. Severity: Critical / Major / Medium / Minor / Nitpick
  3. Confidence: How likely is this a real problem vs. a false positive?
    • Human reviewer comments → always high confidence
    • Multiple bots flagging the same thing → high confidence
    • Single bot, no context about codebase patterns → low confidence
    • Bot flagging a SKILL.md or config file → usually noise

2d. Identify ignore candidates

Flag as ignore candidate if ANY of these apply:

  • Bot comment on a non-source file (.md, config, migrations) with no security implications
  • Style/nitpick-level feedback that contradicts project conventions (check AGENTS.md)
  • Bot flagging something that was intentionally designed that way (check git blame / PR description)
  • Outdated thread (isOutdated == true) where the code has already changed
  • Duplicate of another issue already being addressed
  • Bot suggesting a pattern that contradicts a loaded skill or AGENTS.md convention

Phase 3: Write the Fix Plan

Write the plan to .claude/scratchpad/pr-{PR_NUMBER}-review-plan.md.

Plan Format

# PR #{PR_NUMBER} Review Plan — "{PR_TITLE}"

**Branch:** {branch_name}
**PR URL:** {pr_url}
**Threads fetched:** {total} total, {unresolved} unresolved, {outdated} outdated
**Bot breakdown:** {count per bot}

---

## Issues to Fix (ordered by severity)

Only include issues that will actually be fixed. Items classified as ignored in Phase 2 go EXCLUSIVELY in the Ignored section below — never list them here.

### 1. [{SEVERITY}] {Short description of the issue}

- **File:** `path/to/file.ts#L{line}`
- **Category:** {category}
- **Flagged by:** @bot1, @bot2
- **Comment URL:** {url to first comment}
- **What's wrong:** {1-2 sentence explanation in plain english}
- **Suggested fix:** {concrete description of what to change}

> Original comment (from @bot1):
> {relevant excerpt — strip boilerplate/badges}

---

### 2. [{SEVERITY}] ...

---

## Ignored (with reasoning)

Each ignored item appears ONLY here — not duplicated in the Issues to Fix section above.

### I1. @{bot} on `path/to/file.ts#L{line}`

- **Why ignored:** {specific reason — e.g., "contradicts project convention in AGENTS.md to not use explicit return types", "outdated thread, code already changed", "style nitpick on a config file"}
- **Original comment:** {link to comment}

### I2. ...

---

## Summary

- **{N} issues to fix** across {M} files
- **{K} comments ignored** ({reasons breakdown})
- Estimated complexity: {low/medium/high}

Present to user

After writing the plan, tell the user:

Review plan written to .claude/scratchpad/pr-{PR_NUMBER}-review-plan.md. {N} issues to fix, {K} ignored. Please review and confirm to proceed.

STOP HERE. Wait for the user to review and approve. Do not proceed until they confirm.

Phase 4: Execute (after human approval)

Once the user approves (they may edit the plan first — re-read it before executing):

4a. Resolve ignored threads

For each ignored issue, resolve the GitHub thread with a brief comment explaining why:

# Post a reply comment on the thread
gh api -X POST "repos/{OWNER}/{REPO_NAME}/pulls/{PR_NUMBER}/comments" \
  -f body="Acknowledged — {reason}. Resolving." \
  -F in_reply_to={COMMENT_DATABASE_ID}

# Resolve the thread via GraphQL
gh api graphql -f query='
mutation {
  resolveReviewThread(input: { threadId: "{THREAD_NODE_ID}" }) {
    thread { isResolved }
  }
}'

Use concise, specific dismiss reasons. Examples:

  • "Acknowledged — project convention is to omit explicit return types (see AGENTS.md). Resolving."
  • "Acknowledged — outdated thread, code has been refactored. Resolving."
  • "Acknowledged — this is intentional; sessionStorage is only accessed client-side. Resolving."

4b. Fix real issues with subagents

Group related issues that touch the same file or logical unit. Then launch parallel subagents (one per file or logical group) using the Task tool:

Launch a Task subagent (subagent_type: "general-purpose") for each group:

Prompt template:
"Fix the following PR review issue(s) on branch {BRANCH}:

Issue: {description}
File: {path}#{line}
What's wrong: {explanation}
Suggested fix: {fix description}

Read the file, understand the surrounding context, and make the fix.
After fixing, verify the change is correct.
Do NOT touch unrelated code."
  • Use subagent_type: "general-purpose" for each group
  • Launch groups in parallel where they touch different files
  • Sequential if they touch the same file

4c. After all subagents complete

  1. Resolve the fixed threads on GitHub (same GraphQL mutation as 4a, with a comment like "Fixed in latest push.")
  2. Report results to the user

Known Bot Noise Patterns

These are almost always ignorable — but verify before dismissing:

  1. coderabbit on SKILL.md / AGENTS.md files — flags markdown structure, irrelevant
  2. gemini suggesting explicit return types — check project AGENTS.md or lint config before accepting
  3. devin HTML comment metadata — often duplicates what coderabbit already found
  4. codex P3 style suggestions — usually preferences, not bugs
  5. Any bot suggesting as casts or non-null assertions — check project conventions before accepting
  6. vercel[bot] deployment comments — pure noise, never actionable
  7. Bot comments on migration files — almost always false positives (auto-generated code)
how to use pr-comments

How to use pr-comments 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 pr-comments
2

Execute installation command

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

$npx skills add https://github.com/casper-studios/casper-marketplace --skill pr-comments

The skills CLI fetches pr-comments from GitHub repository casper-studios/casper-marketplace 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/pr-comments

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

4.643 reviews
  • Omar Liu· Dec 20, 2024

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

  • Dhruvi Jain· Dec 16, 2024

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

  • Mia Gupta· Dec 16, 2024

    pr-comments reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Hiroshi Khanna· Dec 4, 2024

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

  • Omar Nasser· Nov 23, 2024

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

  • Yuki Gonzalez· Nov 19, 2024

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

  • Soo Li· Nov 11, 2024

    pr-comments reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Oshnikdeep· Nov 7, 2024

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

  • Ganesh Mohane· Oct 26, 2024

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

  • Advait Rao· Oct 26, 2024

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

showing 1-10 of 43

1 / 5