/triage-requests
Analyze, categorize, and prioritize a batch of feature requests from customers or stakeholders
$ npx -y skills add phuryn/pm-skills --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/triage-requests
Context preview
What this command does when you run it.
Analyze, categorize, and prioritize a batch of feature requests from customers or stakeholders
Command definition
triage-requests.mddescription: Analyze, categorize, and prioritize a batch of feature requests from customers or stakeholders
argument-hint: "<feature requests as text, file, or paste>"
/triage-requests -- Feature Request Triage
Take a pile of feature requests — from support tickets, sales calls, surveys, or Slack — and turn them into a prioritized, actionable backlog.
Invocation
/triage-requests # asks for input
/triage-requests [paste a list of requests]
/triage-requests [upload a CSV/spreadsheet]
Workflow
Step 1: Accept Feature Requests
Accept requests in any format:
- **Pasted text**: List of requests, one per line or paragraph
- **Uploaded file**: CSV, Excel, or text file with request data
- **Structured data**: If the input has columns (requester, request, date, etc.), preserve them
If no input is provided, ask the user to paste or upload their feature requests.
Parse each request to extract:
- The core ask (what the user wants)
- Context (who asked, when, why — if available)
- Frequency signals (how many people asked for similar things)
Step 2: Gather Prioritization Context
Ask the user (conversationally, not all at once):
- What is the product? What stage is it in?
- What are the current strategic goals or OKRs? (helps assess alignment)
- Any constraints to consider? (team size, technical debt, upcoming deadlines)
- Are there segments whose requests should carry more weight? (enterprise, churning users, power users)
Step 3: Categorize and Analyze
Apply the **analyze-feature-requests** skill:
- **Theme clustering**: Group similar requests into themes (e.g., "reporting & analytics", "collaboration", "mobile experience")
- **Request count per theme**: How many unique requests map to each theme
- **Strategic alignment**: Rate each theme against stated goals (High/Medium/Low/None)
- **Segment analysis**: Which user segments are driving which themes
- **Sentiment signals**: Are requests accompanied by frustration, churn threats, or delight?
Step 4: Prioritize
Apply the **prioritize-features** skill:
For each theme (and the top individual requests within each theme):
| Factor | Assessment | |--------|-----------| | **Impact** | How many users affected? How severely? | | **Strategic alignment** | Does it serve current goals? | | **Effort estimate** | T-shirt size (S/M/L/XL) | | **Risk** | What happens if we don't do this? | | **Revenue signal** | Is this tied to deals, retention, or expansion? |
Rank themes and produce a prioritized list.
Step 5: Generate Triage Report
## Feature Request Triage Report
**Date**: [today]
**Requests analyzed**: [count]
**Themes identified**: [count]
### Theme Summary
| # | Theme | Requests | Top Ask | Alignment | Impact | Effort | Priority |
|---|-------|----------|---------|-----------|--------|--------|----------|
### Priority 1: Act Now
[Themes/requests to include in near-term planning]
- **[Theme]**: [X] requests — [why it's urgent]
- Top requests: [list]
- Recommended action: [build / prototype / investigate]
### Priority 2: Plan Next
[Themes worth planning but not urgent]
### Priority 3: Collect More Signal
[Themes with potential but insufficient evidence]
### Priority 4: Decline or Defer
[Requests that don't align with strategy — with rationale]
### Notable Individual Requests
[High-value one-off requests that didn't cluster into themes]
### Patterns and Insights
- [Key insight about what users are telling you]
- [Segment-specific patterns]
- [Gaps between what users ask for and underlying needs]
Save the report as a markdown file to the user's workspace.
Step 6: Offer Next Steps
- "Want me to **create user stories** for the top-priority items?"
- "Should I **brainstorm solutions** for any of these themes?"
- "Want me to **design experiments** to validate demand before building?"
- "Should I **draft a stakeholder update** summarizing this analysis?"
Notes
- If the user provides a CSV with columns, preserve the data structure and enrich it
- Look for the need behind the request — "add dark mode" might really mean "reduce eye strain during long sessions"
- Flag requests that conflict with each other (e.g., "simplify the UI" vs. "add more configuration options")
- If request volume is large (50+), summarize themes first and offer to drill into specific themes on request
- Output the enriched data as a downloadable CSV if the input was structured data
Read more
description: Analyze, categorize, and prioritize a batch of feature requests from customers or stakeholders argument-hint: "<feature requests as text, file, or paste>"
/triage-requests -- Feature Request Triage
Take a pile of feature requests — from support tickets, sales calls, surveys, or Slack — and turn them into a prioritized, actionable backlog.
Invocation
/triage-requests # asks for input /triage-requests [paste a list of requests] /triage-requests [upload a CSV/spreadsheet]
Workflow
Step 1: Accept Feature Requests
Accept requests in any format:
- **Pasted text**: List of requests, one per line or paragraph
- **Uploaded file**: CSV, Excel, or text file with request data
- **Structured data**: If the input has columns (requester, request, date, etc.), preserve them
If no input is provided, ask the user to paste or upload their feature requests.
Parse each request to extract:
- The core ask (what the user wants)
- Context (who asked, when, why — if available)
- Frequency signals (how many people asked for similar things)
Step 2: Gather Prioritization Context
Ask the user (conversationally, not all at once):
- What is the product? What stage is it in?
- What are the current strategic goals or OKRs? (helps assess alignment)
- Any constraints to consider? (team size, technical debt, upcoming deadlines)
- Are there segments whose requests should carry more weight? (enterprise, churning users, power users)
Step 3: Categorize and Analyze
Apply the **analyze-feature-requests** skill:
- **Theme clustering**: Group similar requests into themes (e.g., "reporting & analytics", "collaboration", "mobile experience")
- **Request count per theme**: How many unique requests map to each theme
- **Strategic alignment**: Rate each theme against stated goals (High/Medium/Low/None)
- **Segment analysis**: Which user segments are driving which themes
- **Sentiment signals**: Are requests accompanied by frustration, churn threats, or delight?
Step 4: Prioritize
Apply the **prioritize-features** skill:
For each theme (and the top individual requests within each theme):
| Factor | Assessment | |--------|-----------| | **Impact** | How many users affected? How severely? | | **Strategic alignment** | Does it serve current goals? | | **Effort estimate** | T-shirt size (S/M/L/XL) | | **Risk** | What happens if we don't do this? | | **Revenue signal** | Is this tied to deals, retention, or expansion? |
Rank themes and produce a prioritized list.
Step 5: Generate Triage Report
## Feature Request Triage Report **Date**: [today] **Requests analyzed**: [count] **Themes identified**: [count] ### Theme Summary | # | Theme | Requests | Top Ask | Alignment | Impact | Effort | Priority | |---|-------|----------|---------|-----------|--------|--------|----------| ### Priority 1: Act Now [Themes/requests to include in near-term planning] - **[Theme]**: [X] requests — [why it's urgent] - Top requests: [list] - Recommended action: [build / prototype / investigate] ### Priority 2: Plan Next [Themes worth planning but not urgent] ### Priority 3: Collect More Signal [Themes with potential but insufficient evidence] ### Priority 4: Decline or Defer [Requests that don't align with strategy — with rationale] ### Notable Individual Requests [High-value one-off requests that didn't cluster into themes] ### Patterns and Insights - [Key insight about what users are telling you] - [Segment-specific patterns] - [Gaps between what users ask for and underlying needs]
Save the report as a markdown file to the user's workspace.
Step 6: Offer Next Steps
- "Want me to **create user stories** for the top-priority items?"
- "Should I **brainstorm solutions** for any of these themes?"
- "Want me to **design experiments** to validate demand before building?"
- "Should I **draft a stakeholder update** summarizing this analysis?"
Notes
- If the user provides a CSV with columns, preserve the data structure and enrich it
- Look for the need behind the request — "add dark mode" might really mean "reduce eye strain during long sessions"
- Flag requests that conflict with each other (e.g., "simplify the UI" vs. "add more configuration options")
- If request volume is large (50+), summarize themes first and offer to drill into specific themes on request
- Output the enriched data as a downloadable CSV if the input was structured data
68 PM skills and 42 chained workflows across 9 plugins. Claude Code, Cowork, and more. From discovery to strategy, execution, launch, growth, and shipping AI-built code. Designed for Claude Code and Cowork. Skills compatible with other AI assistants.
Repo: phuryn/pm-skills
Other commands on pm-skills.
- /derive-tests
Turn documented intent into a test-coverage map — inventory the tests that exist today, derive use-case cases from the system docs, separate existing coverage from proposed tests and unverified gaps, mark each unit / guarded-live / manual, and recommend a green-before-merge CI
Open command - /document-app
Reverse-engineer an AI-built codebase into the system documents reviewers and auditors need — a core set (architecture, flows, permissions, variables) plus conditional docs (emails, cron, SEO, automation) when they apply
Open command - /performance-audit-static
Static performance audit of AI-built code — find N+1 queries and request waterfalls, over-fetching, missing indexes, and caching opportunities, ranked by effort and impact
Open command - /security-audit-static
Static security audit of AI-built code — map trust boundaries, cross-reference documented intent, self-refute every finding, and report only evidence-backed risks
Open command - /ship-check
Turn a vibe-coded repo into a reviewer-ready shipping packet — document the app, wire agent context, run security and performance audits, map test coverage, and compile the results
Open command - /analyze-cohorts
Perform cohort analysis on user data — retention curves, feature adoption, and engagement trends
Open command

