emerging-movers

senpi-ai/senpi-skills · updated Apr 8, 2026

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$npx skills add https://github.com/senpi-ai/senpi-skills --skill emerging-movers
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summary

Tracks Smart Money market concentration across all Hyperliquid assets and flags assets accelerating up the ranks before they become crowded top-3 plays. By the time an asset hits the top of the SM leaderboard, the easy money is gone. This catches the trajectory.

skill.md

Emerging Movers Detector v3.1

Tracks Smart Money market concentration across all Hyperliquid assets and flags assets accelerating up the ranks before they become crowded top-3 plays. By the time an asset hits the top of the SM leaderboard, the easy money is gone. This catches the trajectory.

One API call per scan. Near-zero LLM tokens. Runs every 60 seconds.

How It Works

The SM Profit Concentration Leaderboard

Senpi's leaderboard_get_markets returns all assets ranked by percentage of total Smart Money profit in the last 4-hour rolling window. This isn't trader count — it's where the money is actually flowing.

#1  ETH SHORT   31.4%  286 traders
#2  BTC SHORT   25.1%  436 traders
#3  HYPE SHORT  24.2%  330 traders
...
#36 ASTER SHORT  0.2%   18 traders  ← 60s later: #13, 0.82%, 65 traders

The script tracks this leaderboard over time and detects acceleration.

Detection Signals

Immediate Action Signals (v3+)

Signal Condition Priority
IMMEDIATE_MOVER 10+ rank jump from #25+ in ONE scan Highest — act now
NEW_ENTRY_DEEP Appears in top 20 from nowhere Very high
CONTRIB_EXPLOSION 3x+ contribution increase in one scan Very high
DEEP_CLIMBER 5+ rank jump from #25+ High

Trend Signals

Signal Condition
NEW_ENTRY First appearance in top 50
RANK_UP Jumped 2+ positions in one scan
CLIMBING 3+ positions up over several scans
ACCEL Contribution % increasing scan-over-scan
STREAK Consistently climbing every check
VELOCITY Sustained positive contribution growth

v3.1 Quality Filters

These prevent false IMMEDIATE signals that looked great on rank jump alone but failed on execution:

Filter Rule Rationale
Erratic rank >5 rank reversals in history → erratic: true, downgraded Bouncing ranks are noise
Velocity gate contribVelocity < 0.03 → lowVelocity: true, excluded from IMMEDIATE No momentum behind the move
Trader count floor <10 traders → SKIP IMMEDIATE Single whale risk
Max leverage check max leverage < 10x → SKIP Not worth the limited position sizing

See references/quality-filters.md for implementation details and real-world examples.

Architecture

┌────────────────────────────────────┐
│ Cron: every 60 seconds             │
├────────────────────────────────────┤
│ scripts/emerging-movers.py         │
│ • Loads scan history from JSON     │
│ • Fetches leaderboard (1 API call) │
│ • Parses top 50 markets            │
│ • Compares with previous scans     │
│ • Detects signals + v3.1 filters   │
│ • Saves updated history            │
│ • Outputs JSON with alerts         │
├────────────────────────────────────┤
│ Agent reads output:                │
│ • IMMEDIATE alerts → evaluate now  │
│ • Deep climbers → queue for review │
│ • No alerts → silent               │
└────────────────────────────────────┘

Files

File Purpose
scripts/emerging-movers.py Scanner script
emerging-movers-history.json Auto-managed scan history (last 60 scans)
max-leverage.json Optional: asset max leverage reference

Output

See references/output-schema.md for the complete JSON schema.

Key top-level fields: alerts[], topMovers[], immediateMovers[], deepClimbers[], scanCount, timestamp.

Per-alert fields: asset, direction, rank, prevRank, contribution, traderCount, reasons[], contribVelocity, isImmediate, isDeepClimber, erratic, lowVelocity.

Cron Setup

*/1 * * * * python3 scripts/emerging-movers.py

Agent Response Logic

  • isImmediate: true + erratic: false + lowVelocity: falseEvaluate immediately for entry via Scanner
  • isDeepClimber: true → Queue for next scanner run
  • erratic: true or lowVelocity: true → Log but do not act
  • No alerts → Silent

Companion skills

  • opportunity-scanner — use Scanner to deep-dive assets flagged by Emerging Movers
  • autonomous-trading — full loop integrating Emerging Movers as entry trigger
  • wolf-strategy — uses IMMEDIATE_MOVER as primary entry signal
how to use emerging-movers

How to use emerging-movers 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 emerging-movers
2

Execute installation command

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

$npx skills add https://github.com/senpi-ai/senpi-skills --skill emerging-movers

The skills CLI fetches emerging-movers from GitHub repository senpi-ai/senpi-skills 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/emerging-movers

Reload or restart Cursor to activate emerging-movers. Access the skill through slash commands (e.g., /emerging-movers) 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)
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general reviews

Ratings

4.841 reviews
  • Diya Bansal· Dec 28, 2024

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

  • Zara Bansal· Dec 28, 2024

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

  • Shikha Mishra· Dec 20, 2024

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

  • Zara Agarwal· Dec 8, 2024

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

  • Ira Taylor· Nov 27, 2024

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

  • Diya Thomas· Nov 19, 2024

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

  • Dev Torres· Nov 19, 2024

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

  • Soo Liu· Nov 19, 2024

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

  • Kiara Nasser· Oct 18, 2024

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

  • Henry Chen· Oct 10, 2024

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

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