explainx.ainewsletter3.4k
Productivity

nansen-token-research▌

nansen-ai/nansen-cli · 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/nansen-ai/nansen-cli --skill nansen-token-research
0 commentsdiscussion
summary

All commands: nansen research token <sub> [options]

skill.md

Token Deep Dive

All commands: nansen research token <sub> [options]

--chain required for spot endpoints. Use --token <address> for token-specific endpoints.

Info & Price

nansen research token info --token <addr> --chain solana
nansen research token ohlcv --token <addr> --chain solana --timeframe 1h

Timeframes: 1m, 5m, 15m, 30m, 1h, 2h, 4h, 1d, 1w, 1M

Holders

nansen research token holders --token <addr> --chain solana
nansen research token holders --token <addr> --chain solana --smart-money

Flows

nansen research token flows --token <addr> --chain solana --days 7
nansen research token flow-intelligence --token <addr> --chain solana
nansen research token who-bought-sold --token <addr> --chain solana

flow-intelligence breaks down by label: whales, smart traders, exchanges, fresh wallets, public figures.

DEX Trades

nansen research token dex-trades --token <addr> --chain solana --limit 20

PnL

nansen research token pnl --token <addr> --chain solana --sort total_pnl_usd:desc

Perps (no --chain)

nansen research token perp-trades --symbol ETH --days 7
nansen research token perp-positions --symbol BTC
nansen research token perp-pnl-leaderboard --symbol SOL

Flags

Flag Purpose
--chain Required for spot endpoints (ethereum, solana, base, etc.)
--token Token address (alias: --token-address)
--symbol Token symbol for perp endpoints (e.g. BTC)
--timeframe OHLCV interval
--smart-money Filter to SM wallets only (holders)
--days Lookback period (default 30)
--sort Sort field:direction (e.g. total_pnl_usd:desc)
--fields Select specific fields
--table Human-readable table output
--format csv CSV export

Notes

  • Perp endpoints use --symbol (e.g. BTC), not --token.
  • holders --smart-money returns UNSUPPORTED_FILTER for tokens without SM tracking.
  • flow-intelligence may return all-zero flows for illiquid tokens.
how to use nansen-token-research

How to use nansen-token-research 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 nansen-token-research
2

Execute installation command

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

$npx skills add https://github.com/nansen-ai/nansen-cli --skill nansen-token-research

The skills CLI fetches nansen-token-research from GitHub repository nansen-ai/nansen-cli 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/nansen-token-research

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

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

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

4.7★★★★★36 reviews
  • ★★★★★Chaitanya Patil· Dec 20, 2024

    nansen-token-research has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Emma Abbas· Dec 20, 2024

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

  • ★★★★★Liam Bhatia· Dec 12, 2024

    Registry listing for nansen-token-research matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Omar Robinson· Nov 23, 2024

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

  • ★★★★★Piyush G· Nov 11, 2024

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

  • ★★★★★Diya Smith· Nov 11, 2024

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

  • ★★★★★Aanya Menon· Nov 3, 2024

    nansen-token-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Soo Malhotra· Oct 22, 2024

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

  • ★★★★★Omar Verma· Oct 10, 2024

    nansen-token-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Shikha Mishra· Oct 2, 2024

    We added nansen-token-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

showing 1-10 of 36

1 / 4