survey

supercent-io/skills-template · updated Apr 8, 2026

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$npx skills add https://github.com/supercent-io/skills-template --skill survey
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Keyword: survey | Platforms: OMC / OMX / OHMG / Claude Code / Codex CLI / Gemini CLI / OpenCode

skill.md

survey - Cross-Platform Problem-Space Scan

Keyword: survey | Platforms: OMC / OMX / OHMG / Claude Code / Codex CLI / Gemini CLI / OpenCode

Survey the landscape before planning, coding, or committing to a direction.


When to use this skill

  • Before building a new feature, tool, workflow, or agent capability
  • When the user asks "what exists?", "scan the landscape", "research this space", or "survey solutions"
  • When you need problem context, current workarounds, and solution gaps before /plan, jeo, ralph, or implementation
  • When the topic spans multiple agent platforms and you need a single vendor-neutral picture

Do not use this skill when

  • The user already knows the solution and wants implementation now
  • The task is a small bug fix or narrow code change
  • The user needs a feasibility study, architecture plan, or execution roadmap rather than discovery

Output Package

Save research in a platform-neutral directory so Claude / Codex / Gemini can all reuse it:

.survey/{slug}/
├── triage.md
├── context.md
├── solutions.md
└── platform-map.md    # optional; required for agent/tooling/platform topics
  • triage.md: problem / audience / why now
  • context.md: workflows, affected users, workarounds, adjacent problems, user voices
  • solutions.md: solution inventory, categories, frequency, gaps, contradictions, key insight
  • platform-map.md: normalize platform-specific findings into settings, rules, hooks

Core Abstraction Layer

When the topic involves agent tooling, model orchestration, hooks, permissions, or vendor APIs, normalize findings into three layers:

  • settings: model, safety, temperature, max tokens, system prompt, provider parameters
  • rules: allow/deny/modify policy, guardrails, action constraints, approval logic
  • hooks: pre/post/error callbacks, notify handlers, lifecycle automation, event triggers

Use this abstraction when comparing Claude, Codex, Gemini, OMC, OMX, or OHMG. Do not describe vendor features as unrelated one-off concepts if they map cleanly into one of these three layers.

Recommended internal profile:

survey_profile:
  settings:
    search_language: English
    output_language: user-language
    max_searches_per_lane: 8-10
    save_root: .survey
  rules:
    - facts_only
    - no_code_generation
    - ask_max_2_triage_questions
    - dedupe_solution_names
    - include_source_links
  hooks:
    pre_research:
      - normalize_topic
      - choose_platform_adapter
    post_lane:
      - merge_notes
      - record_sources
    post_run:
      - write_summary
      - suggest_next_skill

Platform Adapter

Select the closest available orchestration surface, but keep the output format identical.

Platform Preferred execution shape Notes
OMC / Claude Code 4 parallel research agents. Prefer research-oriented specialist agents when available; otherwise use general-purpose agents with web search. Keep artifacts in .survey/, not .omc/, so other platforms can reuse them.
OMX / Codex CLI Use $research, explorer/planner-style agents, or parallel workers with web access. Translate vendor config into settings/rules/hooks in platform-map.md when relevant.
OHMG / Gemini / Antigravity Use Workflow Guide + PM/research-capable agents or equivalent parallel lanes. Preserve the same 4-lane structure and shared artifact names.
OpenCode / fallback Use generic web-capable agents in parallel. Do not block on missing specialized agent names.

If a platform-specific specialist does not exist, fall back to a general-purpose web-enabled agent and keep going.


Execution Policy

  • Never write product code in this skill. This is a research-only skill.
  • Search broadly in English for coverage, unless the user explicitly requires another search language.
  • Save files in the user's language unless the repository has a stronger convention.
  • Ask at most 2 triage questions, one at a time, only if what, who, or why is unclear.
  • Run all 4 research lanes in parallel whenever possible.
  • Keep claims source-backed. Include links for quotes, rankings, and non-obvious claims.
  • Deduplicate tools that appear under multiple names or product tiers.
  • Do not recommend build/kill/adopt by default. Present the landscape and gaps.

Workflow

Step 0: Triage

Parse the request into:

  • what: the pain point, idea, or capability to survey
  • who: who feels the pain or uses the workflow
  • why: why it matters now

Before proceeding, check whether .survey/{slug}/triage.md already exists.

  • If it exists, ask whether to reuse or overwrite.
  • If the user keeps the existing survey, skip to Step 3 and summarize the saved files.

Save:

# Triage
- Problem: {what}
- Audience: {who}
- Why now: {why}

Step 1: Run 4 Parallel Lanes

Launch all lanes together.

Lane A: Context

Research:

  • where the problem appears in real workflows
  • who is affected
  • how people currently work around it
  • adjacent problems and downstream consequences
  • direct user voices from communities

Return sections:

  • ## Workflow Context
  • ## Affected Users
  • ## Current Workarounds
  • ## Adjacent Problems
  • ## User Voices

Lane B: Solutions

Research:

  • products, plugins, libraries, SaaS, GitHub projects, services
  • curated lists and comparison pages
  • common pricing and packaging
  • limitations and repeated complaints

Return sections:

  • ## Solutions
  • ## Frequency Ranking
  • ## Categories
  • ## Curated Sources

Lane C: Actual Behavior

Research:

  • what people actually use in practice
  • which manual workflows persist despite vendor claims
  • common frustration patterns
  • where users drop back to spreadsheets, scripts, copy-paste, or multi-tool workflows

Return sections:

  • ## What People Actually Use
  • ## Common Workarounds
  • ## Pain Points With Current Solutions
  • ## Sources

Lane D: Alternatives / Platform Map

Default mode:

  • JTBD alternatives
  • cross-industry substitutes
  • indirect competitors

For agent / model / orchestration topics, this lane must instead normalize the space into:

  • ## Settings
  • ## Rules
  • ## Hooks
  • ## Platform Gaps

Use a comparison table for Claude / Codex / Gemini when applicable.

Step 2: Synthesize and Save

Write context.md:

# Context: {project name}

## Workflow Context
{when and where the problem shows up}

## Affected Users
| Role | Responsibility | Skill Level |
|------|----------------|-------------|
{rows}

## Current Workarounds
{numbered list with limitations}

## Adjacent Problems
{lettered or bulleted list}

## User Voices
{quotes with source links}

Write solutions.md:

# Solution Landscape: {project name}

## Solution List
| Name | Approach | Strengths | Weaknesses | Notes |
|------|----------|-----------|------------|-------|
{deduplicated list}

## Categories
{grouped by category}

## What People Actually Use
{behavior summary}

## Frequency Ranking
{most mentioned solutions}

## Key Gaps
{structural gaps not covered today}

## Contradictions
{marketed claims vs user reality}

## Key Insight
{1 paragraph}

When the topic is platform or agent related, write platform-map.md:

# Platform Map: {project name}

## Settings
| Concern | Claude | Codex | Gemini | Common Layer |
|---------|--------|-------|--------|--------------|
{rows}

## Rules
| Concern | Claude / OMC | Codex / OMX | Gemini / OHMG | Common Layer |
|---------|---------------|-------------|---------------|--------------|
{rows}

## Hooks
| Lifecycle | Claude | Codex | Gemini | Common Layer |
|-----------|--------|-------|--------|--------------|
{rows}

## Platform Gaps
{where abstractions do not align cleanly}

Step 3: Summarize for the User

Return a short summary:

## Survey complete: {slug}

### Context
- {1-2 sentence summary}
- Main workaround: {most common workaround}

### Solution Landscape
- {N} solutions across {M} categories
- Key insight: {one sentence}
- Key gap: {one sentence}

### Files
- `.survey/{slug}/triage.md`
- `.survey/{slug}/context.md`
- `.survey/{slug}/solutions.md`
- `.survey/{slug}/platform-map.md`   # if created

Do not move into planning or implementation unless the user asks.


Quick Reference

Action Instruction
Start a survey survey <topic>
Reuse existing results Check .survey/{slug}/ first
Search strateg
how to use survey

How to use survey 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 survey
2

Execute installation command

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

$npx skills add https://github.com/supercent-io/skills-template --skill survey

The skills CLI fetches survey from GitHub repository supercent-io/skills-template 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/survey

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

4.725 reviews
  • Dhruvi Jain· Dec 28, 2024

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

  • Aisha Reddy· Dec 8, 2024

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

  • Li Singh· Nov 27, 2024

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

  • Oshnikdeep· Nov 19, 2024

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

  • Amina Khanna· Nov 15, 2024

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

  • Zaid Singh· Oct 18, 2024

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

  • Ganesh Mohane· Oct 10, 2024

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

  • Rahul Santra· Sep 17, 2024

    survey reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Maya Bhatia· Sep 13, 2024

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

  • Pratham Ware· Aug 8, 2024

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

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